Abstract

AI agent access control is about governing what autonomous software agents are allowed to do and access across your cloud infrastructure, data systems, and internal tools at runtime. It’s about identity ownership and action-level authorization, so your AI agents operate within tightly scoped, time-bound, and policy-enforced permissions that you can keep track of. 

We’ll walk through how to implement AI agent access control by covering how to:

1. Create an inventory of every AI agent and its access
2. Assign every agent a unique identity and owner
3. Replace standing privileges with just-in-time access
4. Scope permissions to the agent’s task, not its potential capabilities
5. Require human approval for sensitive or destructive actions
6. Control which tools, APIs, and data sources each agent can use, including MCP servers
7. Monitor agent activity with full audit trails

Beyond just helping human users, AI agents now make changes on their own. Teams let agents update infrastructure, modify Kubernetes resources, query production databases, and trigger workflows across internal tools. 

Cloudera’s Future of Enterprise AI Agents report found that 63% of enterprises already deploy security monitoring agents. But many teams still haven’t clearly defined what those agents are allowed to touch, modify, or trigger.

In many environments, agents run with whatever permissions were easiest to assign: a senior engineer’s role, a broad service account, or a long-lived token no one revisits. That lets automation act far beyond its intended task. Agents reduce toil, but without clear guardrails, they also introduce standing authority across systems that were never designed for autonomous decision-making.

What is AI agent access control? 

AI agent access control defines what agents can access, what they can change, and whether that scope matches the job they were given. It also gives teams visibility into agent activity and a record of why each action was allowed, so automation can scale without creating invisible privilege across the stack.

For AI agents, context matters as much as identity. Access decisions should account for the agent, trigger, task, environment, resource sensitivity, business context, and whether the requested action matches the agent’s approved purpose.

Remember that AI agents differ a lot from traditional software service accounts. A fixed integration account usually performs one predictable task. AI agents interpret inputs, decide which tools to call, and chain actions together. 

That makes AI agents a growing source of NHI sprawl, especially when teams keep adding agent identities, service accounts, API keys, and tokens without clear ownership or lifecycle controls. 

If an agent runs with a human’s accumulated permissions or a broadly scoped service account, you’ve effectively handed it access to everything that identity can reach.

You also need to think in two dimensions: what data can the agent see, and what actions can it take? Production tables, secrets, customer logs, infrastructure changes, credential rotation, role assumption, CI/CD workflows, and MCP tools all need controls. An agent with read-only access can still expose sensitive data, while an agent with write access can damage production even if its data access is narrow.

Why Static Access Control Breaks Down for AI Agents 

Most access control models were built for stable users and predictable applications. You assign a role, review it periodically, and assume the scope still matches the job. That model doesn’t hold up in an agentic identity crisis, where autonomous identities can interpret inputs, choose tools, and combine permissions across systems.

Agents often run under shared service accounts or inherited human roles. When something changes, like a database update or a configuration rollback, it becomes difficult to answer who authorized the action, which policy allowed it, and whether the scope matched the original intent. That’s why agent access can’t rely on static roles. It needs to be scoped to the task, granted only when required, and revoked automatically once complete. 

Table 1: Traditional vs AI Agent Access Control

Traditional access control	AI agent access control
Assigns static roles to users, groups, or service accounts.	Grants task-scoped permissions based on what the agent is trying to do right now.
Gives access before the task is known, often through standing privileges.	Provides just-in-time access only when the agent needs it, then revokes it automatically.
Assumes the identity behaves predictably.	Accounts for agents that interpret prompts, call tools, and chain actions across systems.
Focuses mainly on who has access to a resource.	Evaluates identity, intent, action, resource sensitivity, environment, and risk.
Often relies on shared service accounts or inherited human permissions.	Requires each agent to have a unique identity, clear owner, and tightly scoped permissions.
Makes auditing difficult when actions happen through generic credentials.	Logs who triggered the agent, what it accessed, what it changed, and why the action was allowed.
Uses periodic access reviews to find overpermissioned identities after the fact.	Enforces runtime guardrails that prevent excessive access before the action happens.

Key AI Agent Access Control Risks

• Overprivileged agents
  Agents granted broad roles to make things easier can modify or access far more than their task requires, increasing blast radius if they misfire or are compromised.
• Agents using human credentials
  When agents run under a developer’s account, they inherit accumulated permissions across systems, making unintended actions far more impactful.
• Prompt injection and indirect instruction attacks
  Malicious inputs can trick agents into executing unintended actions, such as exposing sensitive data or triggering infrastructure changes.
• Overprivileged MCP servers
  If an MCP server exposes tools through a broad backend identity or over-scoped token, any connected agent may be able to invoke actions beyond its intended purpose. The risk comes from the tools the agent can call, the backend permissions those tools use, and whether those calls are scoped, logged, and approved.
• Agent-to-agent access chains
  In multi-agent AI workflows, multiple agents can call each other, combining separate permissions into a broader, unintended control path. If the original human trigger, agent identity, and downstream agent actions aren’t preserved in the audit trail, teams lose accountability across the chain.
• Shadow agents with no clear owner
  Experimental agents often persist without governance, operating with credentials that no one actively reviews.
• Accidental data changes or destructive actions
  Misinterpreted instructions can lead agents to modify or delete resources, especially when write permissions are overly broad.

How to Implement AI Agent Access Control

The following AI agent security best practices focus on limiting privilege, improving accountability, and giving teams enough visibility to let agents operate safely across production systems.

Step 1: Create an inventory of every AI agent and its access

Before you tighten permissions, you need a clear list of what actually exists. Agents don’t only live in one place. Some run inside CI pipelines. Others sit behind internal APIs. Some are wired into monitoring systems or Kubernetes operators. Others are embedded inside tools that platform teams don’t even fully own.

Start by identifying every agent process, what identity it runs under, and which cloud accounts, clusters, databases, or SaaS tools it can reach. This is especially important in multi-cloud identity management, where one agent may interact with AWS, Azure, GCP, Kubernetes, databases, and SaaS tools through different identities or inherited roles. Don’t stop at role names; map effective permissions too.

Track who owns the agent, what system triggers it, which tools it can call, whether it has write access, and whether its credentials are short-lived or long-lived. Without that clarity, you’re guessing at exposure instead of managing it. 

Step 2: Assign every agent a unique identity and owner

If an agent runs under a shared service account or a senior engineer’s credentials, you’ve already lost clarity. Every agent should have its own identity. This is not to make it look neat on a spreadsheet; it’s because isolation limits damage.

Tie that identity to a named technical owner. Someone should be responsible for reviewing its permissions, understanding its purpose, and decommissioning it when it’s no longer needed. 

That owner should also define the agent’s approved tasks, risk tier, data boundaries, and escalation path when the agent needs elevated access. If no one can answer who owns an agent or why it exists, it shouldn’t be running with production access.

Step 3: Replace standing privileges with just-in-time access

Permanent access is obviously convenient, but it’s also unnecessary and risky for most agent tasks. If an agent only needs elevated permissions during a remediation flow or deployment window, grant them at that moment and revoke them automatically when the task completes. 

Use temporary credentials, short-lived role assumptions, and enforced expiration policies. The point is to shrink the window in which a compromised or misdirected agent can cause harm.

Step 4: Scope permissions to the agent’s task, not its potential capabilities

AI agents are flexible in what they can do, but their permissions shouldn’t be. Define what the agent is supposed to do in concrete terms. If it analyzes logs, it doesn’t need write access to infrastructure. If it deploys code, it doesn’t need broad read access to customer data. 

Avoid granting permissions based on what it might need later. Revisit the scope when capabilities change instead of pre-approving a wide envelope just to prevent friction.

Where possible, validate intent at runtime. The declared task should match the permissions requested and the action the agent is about to perform. If an agent says it needs to summarize logs, it shouldn’t be allowed to change Kubernetes RBAC, export customer data, or assume an admin role.

Step 5: Require human approval for sensitive or destructive actions

Some actions carry more risk than others, and humans in the loop are an effective guardrail. Set policy thresholds that trigger human validation for high-impact operations. This doesn’t mean forcing everything through a ticket queue. It means defining which categories of actions require confirmation and enforcing that boundary consistently. 

Examples include deleting production resources, changing IAM policies, modifying Kubernetes RBAC, exporting customer data, rotating secrets, disabling security controls, or assuming administrator-level roles. It’s about placing guardrails around irreversible and risky changes.

Step 6: Control which tools, APIs, and data sources each agent can use, including MCP servers

An agent’s power comes from the tools it can call. If you expose an MCP server with broad backend access, every connected agent inherits that reach. Restrict tool access explicitly. Allow only the APIs and data sources required for the agent’s defined function. Where possible, isolate tool backends so agents don’t share unrestricted connectors to production systems. 

MCP servers should have their own scoped backend identities, tool allowlists, environment boundaries, and audit logs. Otherwise, you can restrict the agent on paper while still giving it indirect access through an overprivileged tool server. The fewer integration points an agent can invoke, the smaller the attack surface.

Step 7: Monitor agent activity with full audit trails

Authentication logs aren’t enough. You need to see what the agent actually did. Log which permissions were granted, which APIs were called, which resources were modified, and how long elevated access existed. 

That activity data can feed AI security posture management by showing where agents are overprivileged, which actions create the most risk, and where guardrails need to be tightened.

Also, distinguish agent-initiated activity from human actions so investigations don’t turn into guesswork. A useful audit trail should show the triggering user or system, the agent identity, the declared task, the permission granted, the resource accessed, the API or tool called, the approval decision, the session duration, the action taken, and when access was revoked.

If something changes in production at 2 a.m., you should be able to trace it back to a specific agent, task, and approval context without pulling logs from five different systems.

What to Look For in an AI Agent Access Control Solution

If you’re evaluating tooling, agent control needs to operate at the execution level, not just for identity provisioning. At a minimum, you should expect: 

• Zero Standing Privilege for human and agentic identities, so neither runs with permanent, always-on elevation.
• Just-in-time and just-enough permissions granted specifically for the task the agent is performing, then revoked automatically.
• Context-aware runtime authorization that evaluates identity, intent, business context, environment, and resource risk before allowing access.
• A unique identity and accountable owner for every agent, eliminating shared credentials and orphaned automation.
• Human approval workflows for high-impact actions, without forcing everything into ticket queues.
• Granular control over tools, APIs, and data sources, including MCP servers and internal connectors.
• Unified audit logs that clearly show who triggered the agent, what it accessed, what it changed, and under which policy. For agent-to-agent workflows, the audit trail should preserve the full chain of delegation so teams can trace every action back to the original trigger.

Letting AI Agents Act Within Effective Guardrails 

AI agents don’t fit neatly into the access models most teams built over the last decade. They act autonomously, chain tools together, and operate across systems in ways that your static roles and inherited human permissions were never designed to constrain. If you want to scale automation safely, access has to match that reality by being narrowly scoped to the task, granted at the moment it’s needed, evaluated against context, and removed as soon as the work is done. 

That’s where Apono fits. Apono is a cloud-native privileged access management platform built on Zero Standing Privilege principles. Instead of relying on standing admin roles or long-lived credentials, Apono enables task-scoped access for both human and agentic identities at runtime.

With Zero Standing Privilege, context-aware guardrails, and runtime authorization, Apono helps teams validate what an agent is trying to do before sensitive actions are allowed. With Apono Agent Privilege Guard, teams can give AI agents task-scoped, just-in-time access without standing admin privileges or long-lived credentials.

Explore Apono Agent Privilege Guard to see how runtime authorization and context-aware guardrails help agents act safely across production systems.

Today, Apono is joining 1Password.

This is a major step forward for the company we set out to build, the customers who helped shape it, and the future of access governance.

When we started Apono, we set out to eliminate the friction that access management creates between security and engineering teams. Access in the cloud was dynamic, but the systems meant to govern it were not. Widespread standing access became an accepted cost of doing business. Engineers waited on tickets. Security teams struggled to answer who had access, who needed it, and for how long. Standing access piled up, and with it, the blast radius grew. For many organizations, the situation became untenable.

We built a platform both sides could get behind. Apono grants access the moment it is needed, scopes it to the task, and revokes it when the work is done. Zero standing privileges, made operational.

Then agentic AI arrived and made the case even clearer. The same static systems that struggled to keep up with modern cloud infrastructure are even less prepared for AI agents. Agents act dynamically, respond to changing inputs, and can be steered in ways traditional policies were never designed to anticipate. It is one of our core beliefs that the access principles Apono pioneered for humans will become even more important with agents: access should be temporary, specific, and tied to the work being done. The market has validated that conviction faster than even we expected.

Two companies with one vision

In 1Password, we have found a partner that shares our vision for where identity security has to go next. More than 180,000 businesses and over a million developers trust 1Password to protect their credentials and critical systems. By adding Apono’s access governance for humans, machines, and AI agents to that ecosystem, we can deliver what the market has been missing: a modern platform that secures the identity lifecycle from the credential to the action, for every kind of identity.

Identity security has long focused on the front door. But much of the real risk begins after authentication: what an identity reaches for, what it is allowed to do, and how long that access remains in place. That is where Apono lives. Bringing that capability into 1Password makes both companies stronger and gives customers a more complete way to govern access across every identity they rely on.

The road ahead

For our customers, the message is clear: the work you trust Apono to do does not change. The platform you rely on continues running, the team you know keeps building, and the roadmap now has even more behind it.

I want to thank the people who made this moment possible. To our customers and partners who bet on a young company and pushed us to be better, thank you. To the Apono team, who turned a strong conviction into a product that engineers actually want to use, none of this happens without you. To the broader 1Password community, we cannot wait to work together to tackle the most critical challenges companies face in the agentic era.

We will also be growing in both our Tel Aviv and NYC offices and helping far more customers secure access for humans and agents!

This isn’t the end, it’s only the beginning. Let’s get back to work.

Security teams have spent years securing human logins, service accounts, and machine identities. Agentic AI introduces a more autonomous class of software actor: systems that can plan, call tools, delegate tasks, and act across environments. 

This is a concern because most access models were built around static roles and pre-approved permissions. Multi-agent systems put a new spin on those assumptions. As agentic workflows move closer to sensitive data and production infrastructure (with or without deliberate access provided to said agents), security teams need a new control model: one built on task-scoped, time-bound, and context-aware access. 

Within the next three years, 82% of executives plan to adopt AI agents, while noting that the gap between experimentation and mature oversight is widening. That creates a problem for traditional IAM and privileged access management models. 

In practice, that means moving toward Zero Standing Privilege: eliminating persistent access wherever possible and granting privileges dynamically only when a specific task requires them. 

Why Multi-Agent Systems Break Traditional IAM Assumptions

Traditional IAM assumes principals, entitlement boundaries, and access decisions are relatively stable. Agentic workflows challenge that because behavior, delegation, and tool use can change at runtime. 

In many environments, authorization is still coarse-grained and pre-provisioned, even though modern IAM tools support more contextual controls. Multi-agent systems change the game by introducing autonomous agency. Instead of following a linear execution path, these actors plan, call tools, and delegate tasks to other agents as needed. For security teams, this is a serious hurdle in visibility and accountability because it creates a non-linear chain of custody that legacy tracking systems can’t follow. 

When an agent independently decides to use a new tool or delegate work, invoke tools, or trigger workflows with separate privileges, it breaks the assumption that identity alone is enough context for authorization. Legacy models are built on standing access, but in an agentic world, that’s just a permanent open door for attackers. 

When these systems start moving across your cloud infrastructure on their own, traditional IAM may see identity and resource context, but it usually lacks visibility. You end up with a gap in accountability because you can’t prove why an action happened. 

The New Attack Surface: Agent-to-Agent Trust, Prompt Injection, and Delegated Abuse 

Many early agentic workflows rely on implicit trust between agents, tools, and delegated actions. That creates a new risk pattern: an agent doesn’t need to be explicitly granted broad access to create risk. It may interpret a goal, decide it needs more authority, and find a path to that authority through available tools, delegated agents, inherited credentials, or workflow shortcuts.

That’s what makes AI agents different from traditional software programs. A conventional service account can be misconfigured with the wrong access level, but it still operates within predefined logic. An AI agent can reason, act, and adapt. In a multi-agent architecture, that autonomy can push an identity beyond the access it was intended to have.

This creates a new challenge for cybersecurity. The main concern is that an internal agent may exceed its intended scope on its own, access sensitive data, and move that data through another agent, system, or external endpoint before a security team has full visibility into what happened.

External manipulation still matters. Prompt injection, tool misuse, excessive agency, and weak control over autonomous actions can all turn trusted agent workflows into attack paths. But the deeper issue is that standing access gives agents too much room to improvise. A single instruction, delegated task, or tool call can move through trusted agent-to-agent relationships and trigger actions across your cloud, SaaS, or infrastructure stack.

Without granular controls, a subverted or overreaching agent can chain activity across systems. It might call a tool it shouldn’t use, delegate work to a more privileged agent, or perform an action that technically fits its permissions but violates the original human intent. Security teams need to move away from broad, static trust and assume that every interaction between agents, tools, and data sources can become part of the access path.

Consider a support agent that reads a ticket in an IT service management workflow containing a malicious instruction. The agent summarizes the issue, passes it to a remediation agent, and that second agent calls a cloud automation tool with write permissions. No one logged in through a suspicious console session, but an untrusted instruction moved through a trusted agent chain and triggered a sensitive action.

That is the core issue of agentic access. The risk is not only who authenticated. It’s what authority the agent inherited, whether it sought or triggered additional access, why it used that authority, what data it touched, where that data moved, and whether another agent or external actor is now using that access chain to expand the attack.

Identity Security for Agents Needs More Than Authentication—It Needs Context-Aware Authorization 

Authentication still matters, but it is no longer enough. In a multi-agent environment, agentic identity depends on more than verifying the agent itself. It also requires knowing what the agent can do, whose authority it is acting under, how long that authority lasts, and whether the action fits the task. 

It’s not about whether an agent is “who it says it is,” but whether you’ve controlled what it can do and on whose behalf. The most dangerous security failure occurs when your verified, trusted agent executes a task it should never have had access to in that situation. A stronger model evaluates access at the moment of action. It should consider:

• The initiating human
• The agent identity 
• The target resource
• The requested action
• The sensitivity of the data 
• The approval requirement
• The duration of access

This shift points toward least-privilege access for agents, with just-enough permissions by default and Just-in-Time elevation for sensitive, privileged, or destructive actions. Holding onto standing privileges is a huge liability in an agentic environment. You need to ensure your agents operate on ephemeral permissions, that is, task-scoped authority that lives only as long as the specific job you’ve assigned them. 

Visibility and Provenance Will Decide Whether Agent Security Scales 

Traditional logs are not enough for autonomous agents. Security teams need a live inventory of active agents, the permissions each agent can use, the tools they can call, and the human or workflow that initiated the chain. 

The same principle applies across modern security environments: whether teams are monitoring autonomous software agents or evaluating drone detection systems for physical infrastructure, visibility only matters when it can be tied to context and response. 

If you lose that paper trail of decisions across your systems, your security pillars, audit and compliance just fall apart. You cannot secure what you cannot see, and in an environment where agents are spinning up sub-tasks autonomously, “seeing” requires a deep understanding of the provenance behind every action. 

If an autonomous system executes a financial transaction or modifies infrastructure, security teams must be able to trace that action back through the agentic chain to the original intent. This level of provenance is the only way to distinguish between a legitimate autonomous operation and a sophisticated exploit. For agentic systems, useful provenance should answer five questions: 

• Which human, workflow, or system initiated the action?
• Which agent performed it? 
• Which tools, APIs, or resources were involved? 
• What permissions were granted, and for how long? 
• Was approval required before the action executed? 

What the Agentic Access Control Model Should Look Like

The control model for agentic systems should assume that every sensitive action is temporary, contextual, and attributable. Agents should not receive broad standing access simply because they may need it later. They should receive task-scoped authority when the work requires it, and that authority should expire when the task is complete. 

You need to stop thinking about theory and start looking at how this works in production. The identity layer for multi-agent systems must be built for ephemeral, task-scoped access rather than permanent, standing privileges. Treat every agentic action as a one-off event that you authorize based on the task at hand. 

This is where Zero Standing Privilege becomes critical. Instead of giving agents persistent access in case they need it later, teams grant privileges dynamically, only when a specific task, context, and approval flow require it. 

By forcing time-bound access and tight approval workflows, you make sure your autonomous actors only have the power they need, exactly when they need it. If an agent becomes subverted, the blast radius stays small because you’ve ensured those permissions expire or are revoked when the task or access window ends. 

For cloud-native teams, the answer is not another layer of static roles. It is dynamic privilege access: creating privileges at runtime based on who or what is requesting access, what action is being performed, which resource is involved, how sensitive the action is, and whether approval is required. That model works for human users, service accounts, and agentic identities because access is granted for the task, not held indefinitely. 

Beyond Standing Privilege: Securing the Agentic Future

Predictable identity is no longer a safe assumption. With multi-agent systems delegating tasks at machine speed, your legacy access models are a liability. You need to shift your mindset from static roles to a dynamic system where authority is task-scoped. To survive this shift, security teams must move away from the dangerous legacy of standing privileges and toward a future defined by contextual, granular, and time-bound access controls.

Autonomy is the new reality, and you can’t fight it with static tools. What you need is an identity stack that’s as smart as the agents it’s managing, and that means prioritising task-based authority and human oversight so you don’t end up with an unmanaged mess. 

Multi-agent systems need more than static roles and standing permissions. Apono’s Agent Privilege Guard gives security teams runtime privilege controls for the agentic era, with ephemeral access, intent-aware enforcement, human approval for high-risk actions, and full auditability across agent workflows. 

Book a demo to see how Apono reduces standing privilege across human, machine, and agentic workflows.

Two protocols are shaping the AI revolution: A2A for agent-to-agent delegation, and MCP for agent access to tools and external systems. A2A expands who can participate in a workflow by enabling agent-to-agent delegation. MCP expands what agents can reach by connecting them to data and systems. 

By the end of 2026, task-specific AI agents are expected to appear in 40% of enterprise applications, up from less than 5% in 2025. That shift changes where security has to live. Controls that once sat around the application now need to travel with the task as it moves from a user to an agent, from one agent to another, and from an agent into a business system. 

AI Agents in the enterprise are a certainty at this point. The real question is whether you can enforce agent access control at runtime based on the requesting identity, the current task, the target system, the requested action, and the allowed duration. Neither protocol is inherently more secure, and A2A and MCP introduce different risks.

A2A Structures the Agent-to-Agent Handoff, But Not the Business Decision 

A2A, or Agent2Agent, is an open standard for communication and interoperability between independent AI agent systems. Its purpose is to let agents discover each other, exchange context, collaborate, and delegate work across multi-agent workflows. 

A2A is not trying to replace how agents access tools or data; that is the role of the MCP protocol. It is focused on how agents coordinate with each other to complete work across multiple systems.

As such, the answer is not that A2A is more secure than MCP, or that MCP is more secure than A2A. They create different trust boundaries. A2A creates delegation risk: should this agent be allowed to pass work, context, or authority to another agent? 

MCP Standardizes Tool Access, But Scope Still Matters 

MCP (Model Context Protocol) is a protocol for connecting AI applications and agents to tools, data, prompts, and external systems. It defines how agents access capabilities in a consistent way, instead of relying on custom integrations for each service. 

Where A2A is about delegation between agents, MCP is about access to capabilities. If an agent needs to call an API or pull context from an external source, MCP is the layer that standardizes that connection. That makes API security a critical part of MCP governance, because the API is often where an agent’s request turns into real data access or downstream action. 

MCP includes authorization guidance for HTTP-based transports and follows OAuth-based conventions, but it does not choose an enterprise’s identity model, permission boundaries, approval rules, or runtime enforcement logic. 

When authorization is supported, HTTP-based MCP implementations should follow the MCP authorization specification. STDIO-based implementations work differently and should retrieve credentials from the local environment. 

That makes MCP security highly dependent on implementation. A well-scoped MCP implementation can limit an agent to one approved operation. A poorly scoped connection can give the same agent broad read or administrative access behind a single tool. 

MCP creates tool-action risk: should this agent be allowed to use this tool against this resource for this operation right now? When both protocols are used together, the risk becomes a chain-of-authority risk. A task can start with a human request, pass through multiple agents, and end with a tool call to a sensitive system. Every step needs its own authorization decision.

Table 1: Where A2A and MCP Create Different Access Questions 

Control dimension	A2A key question	MCP key question
Origin	Who initiated the task?	Which tool is being called?
Boundary	Which agent is receiving the task?	Which resource is being accessed?
Context	What context is being passed?	Is the action read-only, write-level, or destructive?
Authority	What authority is being delegated?	What token or credential is being used?
Propagation / expiry	Can the receiving agent delegate again?	When does that access expire?

Most Enterprises Will Use A2A and MCP Together

Most enterprises will not choose between A2A and MCP because one looks safer than the other. As agent workflows become more operational, teams will need a way to coordinate with other agents and a way to act on systems.

They solve different problems and will often appear in the same workflow. A2A lets agents divide work across a multi-agent AI system. MCP lets those agents retrieve context, call APIs, and act on external systems. For example:

• A user asks an agent to complete a task. 
• That agent delegates part of the task to another agent through A2A. 
• The downstream agent then uses MCP to call a tool, retrieve context, or act against a business system.

The original request may be legitimate, but the downstream action can still be too broad or too privileged. Security teams need to preserve the full chain: initiating user, initiating agent, delegated agent, tool call, target resource, action, approval, and expiry. 

Without that provenance, teams may know that an agent performed an action, but not which user, workflow, approval, or delegated step caused it. This is where the agentic identity crisis starts: you gain more automated actors, but less clarity into who or what is responsible for each action. 

When these protocols are layered, the blast radius grows. The risk is not limited to one agent with excess access. The risk is delegation, identity, tokens, scopes, and approvals compounding across layers.

The Core Security Differences Between Agent Delegation and Tool Connectivity

A2A changes the trust boundary between agents. When one agent delegates part of a task, it can pass along context and implied authority to the next agent in the chain. That means access and decision-making are no longer contained within the first agent.

Should the second agent be allowed to handle the request at all? If it can, teams then need to decide how much context to provide and whether it can continue the chain by delegating to another agent.

MCP changes the trust boundary between an agent and a system. Once an agent can call a tool, the risk comes down to what that connection allows. This becomes even more important when agents interact with third-party tools or vendor-managed systems, where access decisions can introduce broader third-party risk monitoring challenges. 

If the permissions are too broad, the agent can access data or make changes beyond the original task. The issue is the scope of access behind the connection.

Consider a support agent connected to billing through MCP. A customer asks for a refund on one failed order. If the MCP connection uses a broad billing token, the agent may be able to refund other orders, change account balances, issue credits, or read unrelated billing records. MCP gives the agent a path into the system, but the permissions behind that path decide how much damage or overreach is possible.

The MCP connection did not create the business risk on its own. The risk came from giving the agent a reusable credential with more authority than the task required.

The Protocol Debate Misses the Real Problem

The protocol comparison is secondary to this conversation. In today’s cyber threat landscape, production security depends less on which protocol an enterprise chooses and more on its ability to govern both protocols at runtime. 

Without that control, the blast radius can expand quickly. An agent handling a code change request may delegate validation to another agent through A2A. That downstream agent then uses MCP to interact with the CI/CD system through a token that also allows deployments. A simple review task turns into an unapproved production change because the downstream agent had too broad access.

To guard against this, every agent action must be treated as a runtime authorization event. An agent should not inherit broad access because it belongs to a trusted workflow. It should receive a permission grant that is narrow, task-specific, and time-bound. That grant should be tied to the:

• Current task
• Requesting identity
• Acting agent
• Delegation path
• Target system
• Action being attempted
• Access duration

For A2A, delegated work needs a bounded authority envelope. If Agent A sends a task to Agent B, the handoff should include clear limits on what context can be shared, what Agent B can decide, and whether Agent B can delegate again. The receiving agent should not automatically inherit the full authority of the initiating agent or substitute its own broader access.

For MCP, the same model applies at the tool boundary. A tool call should not run through a generic token with full API access. The call should be authorized against the specific operation being requested, such as reading one ticket, updating one record, or triggering one approved workflow. Anything beyond that needs a separate authorization decision.

Static access models built on standing service accounts and broad machine roles are a poor fit here. Teams first need to identify where risky standing access exists, then reduce or remediate it before agents can compound that risk. 

The Real Control Point Is Runtime Access 

Runtime enforcement is critical, regardless of protocol choice. Some organizations may rely more heavily on A2A or MCP, but the access problem does not change. Agent systems need narrow, temporary controls tied to the task being performed. That applies across human and agentic identities as well as non-human identity management. 

Agent security now looks like modern privileged access management. The goal is not to block agents or slow developers down. It is to make sure each action receives only the access it needs, only when it needs it, with a clear record of who or what initiated it.

A stronger model is dynamic least privilege: just-in-time, just-enough access, in which the agent receives only the permissions needed for the current action and loses them once the action is complete. Apono helps cloud-native teams automate that model across human, non-human, and agentic identities, so permissions are scoped, approved, observable, and automatically revoked.See how to secure AI agents with dynamic, just-in-time access. Alternatively, explore the Agent Privilege Lab.

A GitHub breach occurred on May 18, 2026 when a threat actor called TeamPCP pushed a malicious version of Nx Console (a widely used VS Code extension with 2.2 million installs) to Microsoft’s official Visual Studio Marketplace. The compromised version was live for eighteen minutes before being pulled, but in that window, it ran a credential stealer on every machine that auto-updated, harvesting GitHub tokens, AWS keys, SSH keys, and password manager material from developers’ local environments.

One of those developers worked at GitHub. Within hours, TeamPCP had used their credentials to clone approximately 3,800 of GitHub’s internal private repositories – source code, deployment scripts, infrastructure configurations, and Copilot internals – and listed the stolen data for sale on a cybercrime forum for $50,000.

No exploited vulnerability or brute force, just a stolen token with no guardrails to limit how far it could reach.

How it happened

The attack follows a pattern that TeamPCP has refined across more than 500 open source tools in 2026 alone. The group identifies widely used developer tooling, compromises a publisher account or contributor token, and pushes a malicious version through the official distribution channel. The poisoned package runs a credential stealer the moment it executes, exfiltrating secrets from the developer’s local environment. Those credentials are then used to compromise additional tools, whose users become the next wave of victims.

In this case, the entry point was the Nx Console extension. The payload harvested credentials silently and automatically, reaching every developer with auto-updates enabled. GitHub detected the intrusion, isolated the affected endpoint, and began rotating credentials. But by then, the repositories had already been cloned.

Standing privileges were the real vulnerability

The supply chain angle is real, and the Nx Console compromise deserves scrutiny. But focusing on the delivery mechanism obscures the more consequential failure: the stolen credentials had access to 3,800 repositories because that’s what the developer had been persistently provisioned to access.

This is how GitHub access works at most organizations, and GitHub itself was no exception. Repository permissions, org membership, and clone access accumulate over time and rarely get revisited. There is no active work session to expire, no task scope to enforce. Access persists indefinitely, which means a stolen credential carries the full weight of everything that employee has ever been granted.

The attacker didn’t need sophistication. They needed one valid credential attached to enough standing access to make the theft worthwhile. In this case, that threshold was cleared by a wide margin.

What Zero Standing Privilege changes

Under a Zero Standing Privilege (ZSP) model, no user – human or machine – holds persistent access to systems or resources by default. Access is granted just-in-time, scoped to a specific task, and expires when the work is done. The principle is already standard practice for cloud infrastructure and production database access at security-mature organizations, but is considerably less common in developer environments.

Applied to the GitHub breach, the impact is direct: a stolen token from a developer’s machine reflects only what they were actively authorized to do at that moment rather than the accumulated permissions of their entire tenure. If that developer had been granted JIT access to a specific set of repositories for an active project, the credential theft still happens, but the cloned repository count does not reach 3,800.

That’s the difference between a contained incident and a breach of this scale.

GitHub is just the latest example

For teams that have already applied rigorous access controls to cloud infrastructure and production databases, the GitHub breach surfaces a gap that is easy to overlook: developer environments are a privileged access surface, and they are frequently governed as if they are not.

Apono’s GitHub integration brings ZSP enforcement to repository access, utilizing just-in-time grants tied to active work, automatic expiration, and full audit trails across every access event. It applies the same access architecture that security teams have already adopted for cloud and database access to the environment where source code, deployment logic, and infrastructure configuration actually live.

But the principle extends beyond repositories. The same Nx Console payload that harvested GitHub tokens also harvested AWS keys and cloud credentials. If the most sensitive assets in your environment are in cloud infrastructure, standing IAM roles and persistent cloud access are the exposure to address. If they are in production databases, persistent query credentials carry the same risk. The access architecture is the same across environments, the entry point just depends on where the attacker lands first.

The relevant question is not which environment needs ZSP enforcement. It is which environment an attacker will use to pivot into the rest.

The access story underneath the supply chain story

Supply chain attacks are a delivery mechanism. What they delivered is credential theft, and the blast radius of a stolen credential is determined entirely by how much standing access is attached to it. 

For organizations that have already made standing access the exception rather than the default, a breach like this is a contained incident with a limited blast radius. For those that haven’t, the attacker’s only limit is how far the credential reaches. 

Want to see where standing access is creating risk in your environment?

Explore how Apono helps security teams replace persistent access with just-in-time, just-enough privileges across modern infrastructure.

Get a Zero Standing Privilege (ZSP) assessment.

Trust is expensive. The wrong zero trust network security tool can leave you with more standing access and more risk than you started with. 

In today’s modern and complex environments, this sentiment matters more than ever. 22% of breaches involved credential abuse as the initial access vector. 

In this guide, we break down the best zero trust network security tools by category, helping you choose the optimal solution for your requirements. 

What are zero trust network security tools?

Zero trust network security tools enforce one simple idea: no user, device, workload, or service should be trusted by default. Instead of granting broad, persistent network access, zero trust network security tools continuously verify identity, device posture, context, and policy before allowing access to specific apps, systems, or data. 

The goal is to reduce the blast radius of compromised credentials and replace implicit trust with tightly controlled, least-privilege access. In practice, that means different tool categories solving different parts of the problem, as we will explore in this comprehensive list. 

Most relevant are Just-in-Time access tools, which control who can reach sensitive systems, for how long, and under what conditions. That makes these tools especially relevant for cloud-native teams that need stronger security without slowing developers down.

It’s also important to clear up a common misconception: zero trust network security tools are not just firewalls or IAM platforms with a new label. They are part of a broader zero trust architecture built around the principles of zero trust that combine identity, access, segmentation, and continuous verification. If you’re managing sensitive cloud resources or hybrid infrastructure, zero trust tooling quickly becomes a practical requirement rather than a nice-to-have.

Top Picks at a Glance

• Recommended for cloud-native SaaS and regulated engineering teams that need to remove standing access to infrastructure, databases, and internal apps without slowing developers down: Apono
• Recommended for teams that want identity-aware access to internal apps and services: Pomerium
• Recommended for IT teams that want simple, identity-based access to infrastructure: Tailscale
• Recommended for enterprises that want to deploy identity-based microsegmentation without ripping up the network: Elisity
• Recommended for OT, industrial, and cyber-physical environments that need secure remote access: Cyolo

Comparison Table: Best Zero Trust Network Security Tools Compared

Tool	Best for	Key strength	Key limitation	Pricing (starting)	Setup effort
Apono	Cloud-native teams replacing standing privileged access	Automated JIT access across infra and apps	Not a full ZTNA or microsegmentation platform	Custom quote	Low
Pomerium	Identity-aware access to internal apps and services	Strong app-layer access control	Less suited to broader SASE needs	Free; business from $7/user/month	Med
Appgate	Complex hybrid environments needing direct-routed ZTNA	Flexible SDP architecture across many use cases	Heavier rollout than simpler ZTNA tools	By inquiry	High
Banyan Security	Device-centric ZTNA within a broader SSE stack	ZTNA plus VPNaaS, CASB, and SWG	Less focused if you only want standalone ZTNA	License-based	Med
GoodAccess	SMBs needing simple zero trust access	Fast deployment with no hardware	Less suited to large, complex enterprises	From $7/user/month + gateway fee	Low
iboss	Enterprises consolidating around SASE	Broad cloud-delivered security stack	More platform than ZTNA-only buyers need	By inquiry	High
Tailscale	Simple identity-based infrastructure access	Lightweight secure access to infra resources	Less governance-heavy than PAM tools	By inquiry	Low
Teleport	Governed access to servers, DBs, and Kubernetes	Strong privileged access controls and auditability	More infrastructure-focused than general ZTNA	By inquiry	Med
ThreatLocker	Controlling endpoint admin rights	Strong privilege elevation and device controls	Less focused on cloud-native infra workflows	By inquiry	Med
Elisity	Microsegmentation without major network changes	Low-friction identity-based segmentation	Not built for privileged access workflows	By inquiry	Med
Zero Networks	Automated lateral movement prevention	Segmentation plus identity controls and MFA	Narrower scope than broader zero trust platforms	By inquiry	Med
ColorTokens	Broad breach containment across mixed environments	Wide coverage across cloud, endpoints, and OT	More complex than narrower segmentation tools	By inquiry	High
NetFoundry	Secure connectivity across distributed and edge environments	Identity-first connectivity across IT, OT, and IoT	More connectivity-focused than employee access-focused	Free trial; then by inquiry	Med
Cyolo	Secure remote access for OT and cyber-physical systems	Built for critical and legacy operational environments	More OT-specific than general-purpose tools	By inquiry	High
Xage	One zero trust platform across IT, OT, and cloud	Combines ZTNA, PAM, and microsegmentation	Broader scope than single-use-case buyers may need	By inquiry	High

How We Compared These Tools

We evaluated these zero trust solutions across five core categories. Rather than looking for generic security claims, we focused on three qualities that matter most in the current cyber threat landscape: automation, granularity, and frictionless orchestration. 

Our evaluation is based on publicly available information at the time of writing, including official product pages, technical documentation, pricing and packaging details, and vendor materials. 

We didn’t run hands-on tests for every platform. If a capability was unclear, bundled into a broader platform, or described in vague terms, we avoided making stronger claims than the available evidence supported. 

Here’s how we compared tools within each category: 

Category 1: Just-in-Time Access Management 

We looked for tools that replace standing access with time-bound, least-privilege access. The best solutions incorporate features like automated request and approval flows, auto-expiring permissions, and audit logs. We also looked for granular access to specific resources and systems, plus Slack, Teams, or CLI-based workflows.

Category 2: Zero Trust Network Access (ZTNA)

These zero trust solutions provide modern alternatives to traditional VPNs, with features like identity-aware access to apps and services and continuous verification based on user, device, and session context. We reviewed them based on their fit for remote and hybrid environments. 

Category 3: Infrastructure and Privileged Access

We reviewed tools that secure high-impact access to servers, databases, Kubernetes, and cloud consoles. Identity-based infrastructure access and coverage across modern infrastructure environments were bonus points. 

Category 4: Microsegmentation and Lateral Movement Prevention

These tools contain the blast radius after initial compromise. Key features include microsegmentation (or identity-based segmentation), automated policy creation, and visibility into assets and communication paths. 

Category 5: Zero Trust Networking for Distributed and OT Environments

This solutions category focuses on securing distributed and operational environments without disrupting critical systems. These tools offer secure remote access for OT, edge, and distributed sites, in addition to support for hybrid, legacy, or air-gapped environments. 

15 Top Zero Trust Network Security Tools [By Category] 

Category 1: Just-in-Time Access Management 

1. Apono

Apono falls in the category of Just-in-Time access management tools for zero trust network security. It tackles a gap that many zero trust programs leave open: privileged access to cloud infrastructure, production systems, databases, and internal applications. 

Instead of relying on standing permissions, oversized roles, or manual approval tickets, Apono automates Just-in-Time access across the stack and complements broader identity governance and administration efforts. Your team can grant granular, time-bound access only when it is needed, then automatically revoke it when the task is done. 

The solution is especially well-suited to cloud-native SaaS companies and regulated teams that need strong access controls but cannot afford operational friction. 

Main features

• Automated Just-in-Time access flows across infrastructure, applications, and sensitive systems
• Auto-expiring permissions that remove standing access automatically
• Self-serve, granular access requests through Slack, Teams, or CLI
• Fast deployment in under 15 minutes
• Break-glass and on-call access flows for urgent production issues
• Comprehensive audit logs and automated reporting for compliance and investigations
• Granular, time-bound permissions down to specific resources and systems

Recommended for: Cloud-native SaaS and regulated engineering teams that need to remove standing access to infrastructure, databases, and internal apps without slowing developers down. 

Pricing: Contact the Apono team for a tailored quote. 

Review: “When it comes to managing business access control, Apono offers the best features and is very capable. The process of implementation was easy, and this product is easy to use, too. I like that it is cloud-based and comes with lots of automation features.”

Category 2: Zero Trust Network Access (ZTNA)

2. Pomerium

Pomerium is a strong choice for teams that want identity-aware access at the application layer instead of broad network-level access. Its platform combines a hosted control plane with a self-hosted proxy or data plane, supports clientless remote access, and focuses on securing applications without a traditional VPN client.

Main features

• Identity-aware access for internal apps, servers, services, and workloads
• Continuous verification based on identity, device state, and request context
• Clientless remote access with a hosted control plane and self-hosted proxy/data plane 

Recommended for: Teams that want identity-aware access to internal apps and services. 

Pricing: Free personal tier; business starts at $7 per user/month. 

Review: “I like Pomerium because it helps us to secure data. [Plus] it’s very useful because it provides proxy network VPN.”

3. Appgate

Appgate is built for organizations with complex hybrid IT that need direct-routed ZTNA rather than a cloud-routed model. It positions its platform around universal ZTNA for secure remote access, SaaS access, workload-to-workload communication, branch and site connectivity, and OT/IoT use cases.

Main features

• Direct-routed ZTNA that secures connections across complex hybrid IT
• Software-defined perimeter architecture for flexible deployment
• Support for user-to-app, workload, branch, and OT access use cases

Recommended for: Large enterprises with complex hybrid environments that need flexible, direct-routed ZTNA. 

Pricing: 30-day free trial, then pricing is by inquiry. 

Review: “I find Appgate SDP to be very powerful and flexible in terms of configurability. The configuration data model is simple to understand, which makes it much easier to handle.”

4. Banyan Security 

Banyan Security is now part of SonicWall Cloud Secure Edge (CSE), which is worth noting if you are comparing vendors and product names. The platform is positioned as a device-centric SSE offering that includes ZTNA, VPNaaS, CASB, and SWG, with least-privilege access to private websites. 

Main features

• Device-centric ZTNA with integrated SSE capabilities
• Client-based and clientless access options with consistent policy enforcement
• Short-lived credentials and centralized policy management for private apps and cloud resources 

Recommended for: Organizations that want device-centric ZTNA as part of a broader SSE approach. 

Pricing: License-based packaging. 

Review: “Ease to use – Banyan works in the background and alerts me if I don’t have any security features running.”

5. GoodAccess

GoodAccess is a cloud-delivered ZTNA platform aimed primarily at small and midsize businesses. It positions itself as a fast-to-deploy way to create a secure virtual network between users, clouds, on-prem resources, and private LANs, with centralized control and no hardware to manage. 

Main features

• Cloud-delivered zero trust access with no hardware required
• Network and application-layer segmentation with access logging
• Fast deployment with centralized management for distributed teams

Recommended for: Small and midsize businesses that want simple, fast-to-deploy zero trust access for distributed teams. 

Pricing: The Essential package starts at $7 per user/month plus $49/month per dedicated gateway. Premium starts at $11 per user/month. 

Review: “Ease of use and functionality, the management dashboard is well laid out and easy to use.”

6. Iboss

iboss presents its zero trust offering as part of a broader cloud-delivered SASE platform rather than as a standalone ZTNA product. iboss emphasizes replacing legacy VPNs, proxy appliances, and parts of the security stack with a unified platform that combines ZTNA, Secure Web Gateway, CASB, DLP, browser isolation, and SD-WAN. 

Main features

• Application-specific access with continuous verification and zero network exposure
• Cloud-native ZTNA designed to replace legacy VPNs
• Broader SASE capabilities, including SWG, CASB, DLP, browser isolation, and SD-WAN

Recommended for: Enterprises looking to consolidate ZTNA into a larger SASE stack. 

Pricing: By inquiry. 

Review: “iBoss is easy to use for those unfamiliar with online security and filtering.”

Category 3: Infrastructure and Privileged Access

7. Tailscale

Tailscale fits this category as an identity-based connectivity platform that can secure access to infrastructure across cloud, on-prem, and hybrid environments without the usual VPN sprawl. For teams focused on infrastructure access, its pitch is simple: give users resource-level access to databases, Kubernetes clusters, VMs, and containers, with end-to-end encrypted networking. 

Main features

• Supports session recording, log streaming, network flow logs, and device posture integrations.
• Fine-grained least-privilege controls. 
• Identity-based infrastructure access. 

Recommended for: Teams relying on IT operations analytics that want simple, identity-based access to infrastructure. 

Pricing: By inquiry. 

Review: “The dashboard and overall UI are easy to understand, and they’re flexible as well.”

8. Teleport

Teleport is one of the strongest fits in this category because it is built specifically for zero trust infrastructure access. It gives engineering teams and machine identities secure, identity-driven access to servers, databases, Kubernetes clusters, cloud consoles, internal apps, and MCP resources. 

Main features

• Unified access to servers, databases, Kubernetes, cloud consoles, and internal apps from one platform
• Short-lived certificates and secretless authentication
• Session recording, access requests, and detailed audit trails 

Recommended for: Tightly governed privileged access to servers, databases, Kubernetes, and cloud consoles. 

Pricing: By inquiry. 

Review: “I like how it uses single sign on and role based access, so permissions stay tight and traceable.”

9. ThreatLocker

ThreatLocker is a broader zero trust platform rather than a pure infrastructure PAM tool, but it still belongs in this category because it combines privileged access management, network access controls, and cloud access controls in one stack. Its positioning is especially focused on reducing local admin abuse and enforcing device-based verification. 

Main features

• Application-based privilege elevation that limits admin rights to approved apps and specific tasks
• Device-based zero trust network access
• Zero trust cloud access that verifies the device, request, and policy 

Recommended for: Organizations focused on controlling admin rights on endpoints. 

Pricing: By inquiry. 

Review: “The unified dashboard that allows access to all modules is great, with a consistent look and feel.”

Category 4: Micosegmentation and Lateral Movement Prevention

10. Elisity

Elisity is built for organizations that want to stop lateral movement without the usual complexity of legacy segmentation projects. Its platform centers on identity-based microsegmentation, using existing infrastructure to discover assets, map identities, and enforce granular policies without requiring agents, new hardware, or disruptive network changes. 

Main features

• Identity-based microsegmentation without agents, new hardware, or network changes.
• IdentityGraph visibility that discovers and correlates in real time. 
• Dynamic policy engine with context-aware controls. 

Recommended for: Enterprises that want to deploy identity-based microsegmentation without ripping up the network. 

Pricing: By inquiry. 

Review: “It’s easy to understand, easy to implement.”

11. Zero Networks

Zero Networks is a strong fit for this category because it focuses directly on blocking lateral movement through automated microsegmentation and identity segmentation. The platform emphasizes fast, agentless deployment and extra protection for privileged access. 

Main features

• Automated, agentless microsegmentation that can segment networks and identities in about 30 days.
• Identity segmentation that restricts admin and service accounts. 
• Network-layer MFA that keeps privileged ports closed by default. 

Recommended for: A more automated way to contain ransomware and lateral movement. 

Pricing: By inquiry. 

Review: “The asset management, Policy expert, and IRM functionality stand out the best.”

12. ColorTokens

ColorTokens belongs in this category because Xshield is designed specifically to contain breaches by putting a micro-perimeter around every asset. Its pitch is broader than data-center-only segmentation as the platform covers data center workloads, cloud assets, Kubernetes, endpoints, and OT/IoT environments. 

Main features

• Micro-perimeters around every asset. 
• AI-assisted policy workflows and simulation mode to speed policy design. 
• Broad coverage across data center, cloud, Kubernetes, endpoints, and OT/IoT environments with risk-based dashboards. 

Recommended for: Broad breach containment across data center, cloud, endpoints, Kubernetes, and OT/IoT environments.

Pricing: By inquiry. 

Review: “What I like about­ ColorTokens is how it blends smoothly with se­curity systems.”

Category 5: Zero Trust Networking for Distributed and OT Environments

13. NetFoundry 

NetFoundry is a strong fit for distributed and OT-heavy environments because it focuses on identity-first connectivity for workloads, sites, devices, and applications. It is positioned as a zero-trust overlay that works across IT, OT, IoT, multi-cloud, and even air-gapped environments, which makes it especially relevant when connectivity itself is the problem to solve. 

Main features

• Closes inbound ports and authenticates every connection before it is established.
• Uses identity-based policy, least-privilege access, and microsegmentation for workloads and sites.
• Supports hosted, self-hosted, and air-gapped deployments. 

Recommended for: Secure connectivity across distributed apps, sites, devices, and edge environments. 

Pricing: Free trial, then pricing is by inquiry. 

Review: “One of the key advantages is how NetFoundry simplifies networking. It provides secure, zero-trust connectivity without the need for traditional VPNs or hardware.”

14. Cyolo

Cyolo belongs in this category because it is purpose-built for secure remote access to OT and cyber-physical systems, with a strong emphasis on keeping operations running while giving IT centralized visibility and control. Its positioning is less about generic office access and more about enabling employees to reach critical assets. 

Main features

• Secure remote privileged access for OT, ICS, and cyber-physical systems without disrupting operations.
• Identity-based access for legacy systems. 
• Session recording, supervision, approvals, and compliance risk management controls. 

Recommended for: OT, industrial, and cyber-physical environments that need secure remote access. 

Pricing: By inquiry. 

Review: “Easy to integrate into a SASE offering [with] Cloud and on-prem deployment support.”

15. Xage

Xage is one of the more comprehensive options in this category because it is built to secure access across IT, OT, cloud, remote operational edge, and legacy assets from a single zero-trust platform. It offers granular policy enforcement and just-in-time access controls aimed at reducing the risks of VPN-style access. 

Main features

• Combines ZTNA, PAM, and microsegmentation. 
• Enforces granular, identity-based policies and just-in-time access. 
• Supports legacy systems. 

Recommended for: A single zero trust platform spanning IT, OT, cloud, and legacy systems. 

Pricing: By inquiry. 

Review: “I found that the agentless architecture of Xage Fabric convenient for me.”

Reduce Risk Without Slowing Down Engineering 

Zero trust network security tools matter because most environments are no longer protected by a single perimeter. The smartest way to evaluate this market is by category, because ZTNA, microsegmentation, privileged access, and OT access tools solve different problems and belong in different parts of the stack. 

The right zero trust stack depends on your environment, risk model, and operational needs. For teams trying to eliminate risky standing permissions without slowing engineers down, Apono stands out as the Just-in-Time access management leader. 

Apono replaces broad, persistent access with granular, time-bound access across infrastructure, applications, and sensitive systems, while supporting least privilege and faster incident response. 

If your zero trust strategy still relies on standing permissions, Apono gives you a faster way to move to granular, time-bound access with full auditability. Book a demo to see how Apono helps eliminate standing access across modern cloud environments. 

The moment teams move from one cloud to two, identity governance starts to fracture. Roles don’t translate cleanly, and access reviews lag behind deployment velocity. Multi-cloud identity management is the practice of controlling who can access what across AWS, Azure, GCP, Kubernetes, SaaS tools, databases, and other cloud-connected systems.

Multi-cloud identity management gives teams a consistent way to manage identities, enforce least privilege, approve access, and revoke permissions across environments that were never designed to work the same way. 

In practice, multi-cloud identity management is not only about authentication. It is about governing privileged access consistently across every cloud, cluster, database, and application. 

That consistency matters. In a recent CSA survey, three of the top four causes of cloud-related breaches were identity-related: excessive permissions (31%), inconsistent access controls (27%), and weak identity hygiene (27%). 

For DevOps and security teams, engineers need fast access to ship and troubleshoot, but standing permissions create unnecessary risk. These best practices show how to bring multi-cloud access back under control. 

What is multi-cloud identity management?

Multi-cloud identity management is the practice of controlling who can access what across AWS, Azure, GCP, Kubernetes, SaaS tools, databases, and other cloud-connected systems. 

For DevOps and security teams, the goal is to enforce least privilege consistently across environments that were never designed to work the same way, without slowing engineers down. 

A strong strategy centralizes access intent: who needs access, to which resource, for what purpose, under what approval conditions, and for how long. Enforcement still happens through each provider’s native IAM controls, but governance becomes consistent across environments. Teams can apply the same least-privilege standards without manually translating every access decision across platforms. 

This is where native cloud IAM and multi-cloud identity management diverge. Native IAM and authorization tools govern access within their own platforms. Multi-cloud identity management governs consistency across providers, clusters, applications, databases, and data systems. 

It helps answer questions that individual IAM tools can’t answer alone: 

• Are engineers accumulating standing access across multiple clouds? 
• Are production permissions equivalent across environments? 
• Is access revoked everywhere when someone changes roles? 
• Can auditors see who had access, why it was approved, and when it expired? 

In practice, strong multi-cloud identity management depends on visibility, automation, and time-bound access. Teams need to discover privileges across environments, automate approvals and revocations, and replace static permissions with Just-In-Time access wherever possible. That reduces access risk without forcing engineers back into slow ticket queues.

For cloud-native teams, the goal is not just to centralize identity. It is to eliminate standing privilege across every environment and to create access only when needed, for as long as needed.

Why Multi-Cloud Identity Management Gets So Complex

Each cloud provider was designed with its own permission philosophy. Because these models aren’t semantically equivalent, teams can’t enforce least privilege across AWS, Azure, GCP, Kubernetes, databases, and SaaS tools by simply copying roles between platforms. 

Manual access management amplifies the problem. When engineers request access via tickets or spreadsheets, approvals become informal and inconsistent. Review cycles lag behind infrastructure changes. Under delivery pressure, broad roles get granted “temporarily” and never revisited. In multi-cloud environments, standing privilege compounds quickly across providers.

Governance, risk, and compliance add another layer of strain. Audit logs are spread across AWS CloudTrail, Azure Monitor, GCP Audit Logs, Kubernetes audit events, and SaaS activity feeds. Approval evidence may be buried in email threads or ticketing systems. When auditors ask who had access to production data during a specific window, teams often have to stitch together evidence from multiple control planes.

The hardest risks are often cross-system paths. A developer may authenticate through an IdP, assume an AWS role, access a Kubernetes cluster, query a production database, and trigger a CI/CD workflow from the same session. 

Together, they can create a privileged path that no single native cloud IAM console will fully expose. 

Table 1: Key Access Problems in Multi-Cloud Environments

Access problem	What happens in multi-cloud environments	Better approach
Standing admin roles	Privilege accumulates across AWS, Azure, GCP, and Kubernetes	Use JIT, auto-expiring access
Local cloud users	Identities drift from the source of truth	Use federated identity and lifecycle automation
Manual approvals	Tickets and ad hoc Slack requests create inconsistent evidence	Use policy-based access workflows
Fragmented logs	Audit evidence is spread across multiple systems	Centralize approval, grant, activity, and revocation logs
Over-permissioned service accounts	Automation gets broad access that is rarely reviewed	Use just-enough access and short-lived credentials

Multi-Cloud Identity Management: 10 Best Practices

1. Centralize Identity Visibility Across Every Cloud

Multi-cloud identity management often starts with privilege discovery: identifying who has access, what access is unused, and where accounts are overprivileged before teams can right-size permissions. 

In multi-cloud environments, risk often lurks in the gaps between platforms. Reviewing AWS IAM, Azure RBAC, or GCP roles independently gives you only a partial view of privilege. 

What matters is effective access across providers: who can access production data, how that access affects cloud data security, and whether infrastructure can be modified or privileged roles assumed when entitlements are combined.

Centralizing identity visibility means aggregating identity and entitlement data across clouds, clusters, databases, and SaaS tools so you can evaluate exposure across the full access path, not platform by platform.

2. Use Federated Identity and Single Sign-On

Federation moves authentication to a single authoritative identity provider so access changes and deprovisioning events propagate consistently. When someone leaves or changes roles, federated access should be removed across every connected environment, while local users, service accounts, and break-glass accounts should be governed separately. 

Separate local users in AWS, Azure, GCP, and SaaS platforms inevitably drift out of sync with real-world role changes. Minimize local cloud users wherever possible, tightly govern unavoidable break-glass accounts, enforce MFA through the identity provider, and automate lifecycle updates.

3. Standardize Access Policies

Each cloud provider handles permissions differently, making it risky to copy roles between platforms. A role labeled “admin” in one platform does not necessarily translate cleanly into another.

Standardizing access means defining intent: what production write, infrastructure management, or data access should actually include. Then, map that intent to provider-native constructs.

Governance should live above individual IAM systems, even if enforcement happens within them. Build a common access taxonomy, such as read-only, production read, production write, admin, break-glass, and vendor access. Then map those access levels to provider-native roles instead of copying role names across clouds.

4. Eliminate Standing Privileges with Just-In-Time Access

Just-In-Time access reframes elevation as temporary and contextual. Instead of granting enduring admin rights, organizations provide time-bound access for specific tasks, with automatic expiration. This narrows the window in which compromised credentials can be abused.

JIT access is how teams move toward Zero Standing Privilege. Instead of assigning admin, production, or database permissions permanently, access is created only when there is a valid reason, approved through policy, and removed automatically when the session expires.

For non-human identities, focus on just-enough access: each workload, service account, CI/CD role, and automation token should have only the permissions required for its specific function. Where possible, replace long-lived credentials with short-lived tokens and scope access to the workload, environment, and pipeline stage.

5. Enforce Least Privilege for Every Human Identity

Least privilege means giving people only the permissions they need to do their work. It breaks down quietly in multi-cloud environments because trust becomes portable. When someone is trusted in one production environment, teams often extend similar authority elsewhere.

Over time, those assumptions flatten boundaries between providers. Regularly compare assigned roles with actual activity and remove access that has not been used for 30–60 days.

Table 2: Best Practices at a Glance

Best practice	What to do
1. Centralize identity visibility	Aggregate identity and entitlement data across clouds, clusters, databases, and SaaS tools to see effective access across the full path.
2. Use federated identity and SSO	Use one authoritative IdP, minimize local users, govern break-glass accounts, enforce MFA, and automate lifecycle updates.
3. Standardize access policies	Define access intent centrally, then map it to provider-native roles instead of copying role names across clouds.
4. Eliminate standing privileges with JIT access	Grant time-bound access only when needed, with policy-based approval and automatic expiration. Use just-enough access for non-human identities.
5. Enforce least privilege for human identities	Compare assigned roles against actual activity and remove access that hasn’t been used in 30–60 days.
6. Secure non-human identities	Inventory service accounts, CI roles, workload identities, and machine credentials. Replace long-lived secrets with short-lived tokens where possible.
7. Automate access workflows	Automate requests, approvals, provisioning, and revocation through tools engineers already use, such as Slack, Teams, CLI, or developer platforms.
8. Audit and remove excess access	Use activity data and continuous risk monitoring to detect stale, excessive, or high-risk access.
9. Create controlled break-glass access	Define emergency access paths with time-bound activation, justification, audit logs, and automatic expiration.
10. Map controls to compliance	Correlate approvals, grants, activity logs, and revocation events into one audit trail across every provider.

6. Secure Non-Human Identities, Service Accounts, and Machine Credentials

Securing non-human identities means governing the machine credentials that power automation across your cloud stack. CI roles, workload identities, Kubernetes service accounts, and SaaS integrations routinely execute actions across providers. If over-permissioned, they create rapid and silent escalation paths.

Continuously inventory machine identities, replace long-lived secrets with short-lived tokens where possible, and scope permissions to the specific workload, environment, or pipeline stage they support.

7. Automate Access Requests, Approvals, Provisioning, and Revocation

Manual processes introduce delay and inconsistency. Under pressure, teams grant broader access than necessary to avoid repeated friction.

The practical shift is to move access control into automated, policy-backed workflows that provision and revoke permissions programmatically. Define approval rules in advance, embed access requests into tools engineers already use, such as Slack, Teams, CLI, or internal developer platforms, and grant permissions automatically when policy conditions are met.

For cloud-native teams, the goal is not another approval portal. It is to replace legacy PAM workflows with contextual, automated access that fits how engineers already work. 

8. Continuously Audit Permissions and Remove Excess Access

Multi-cloud access accumulates through migrations, troubleshooting, and shifting responsibilities. Permissions granted during one project often linger into the next, and multi-cloud environments make stale access harder to spot.

Review permissions against actual usage data, not just assigned roles. You can use continuous risk monitoring to detect stale, excessive, or high-risk access. Remove access that has not been used in 30–60 days, and prioritize high-risk permissions such as IAM modification, secret access, production database access, and Kubernetes admin rights. 

9. Create Controlled Break-Glass Access for Incidents and On-Call Workflows

Break-glass access means defining pre-approved, high-privilege access paths that can be activated during incidents without granting permanent admin power. In multi-cloud environments, outages can span providers, so teams may default to giving on-call engineers standing super-admin rights “just in case.”

The safer approach is to design emergency elevation deliberately. Define which roles constitute true incident-level access in each cloud, gate them behind time-bound activation, require explicit justification at the moment of use, and ensure automatic expiration once the incident window closes.

10. Map Identity Controls to Compliance and Audit Requirements From Day One

Compliance and data governance become difficult in multi-cloud environments because audit evidence is scattered across multiple logging systems. Without centralized enforcement and traceability, proving control becomes reactive and manual.

Correlate approvals, access grants, activity logs, and revocation events into a single audit trail. The goal is to answer who accessed what, when, why access was approved, and when it expired across every provider.

For example, Caris Life Sciences used Apono to manage secure access to sensitive health data across hybrid on-prem and AWS environments, including granular access to S3 buckets and folder-level resources. 

Common Multi-Cloud Identity Management Challenges to Avoid

• Assuming SSO Solves the Problem
  Like traditional perimeter security, SSO centralizes login, not privilege. Use SSO as the authentication layer, then govern authorization separately with least-privilege policies, time-bound access, and centralized audit trails. 
• Copying Role Structures Between Clouds
  Lifting AWS role logic into Azure or GCP without rethinking semantics creates hidden over-permissioning. Role names may look similar, but enforcement boundaries are not.
• Ignoring Cross-System Privilege Paths
  A SaaS token that writes to a cloud bucket, or a CI role that deploys into Kubernetes, creates chained access paths that many teams never model.
• Treating Machine Identity as a DevOps Detail
  Service accounts and workload identities are often created inside pipelines without security oversight. When they’re viewed as implementation artifacts instead of access subjects, governance gaps widen.
• Relying on Annual or Quarterly Access Reviews
  In fast-moving cloud environments, access changes weekly. Review cycles measured in quarters can’t keep up with infrastructure velocity.
• Separating Identity Strategy from Engineering Workflows
• If access control is imposed externally rather than integrated into developer tooling, engineers will route around it. Often, they’ll do this by broadening roles for convenience. 

Make Multi-Cloud Access Temporary, Auditable, and Developer-Friendly 

Multi-cloud identity management fails when access decisions are manual, inconsistent, and built around standing permissions. As environments expand, privilege becomes harder to see, harder to govern, and easier to overgrant. The goal isn’t just to manage more identities. It’s to enforce least privilege everywhere without slowing the teams building, deploying, and troubleshooting production systems.

Apono helps cloud-native teams eliminate standing privileges across AWS, Azure, GCP, Kubernetes, databases, and supported SaaS platforms. Instead of relying on static roles, ticket queues, and manual approvals, Apono creates granular Just-In-Time access that expires automatically when the work is done.

Developers request access through Slack, Teams, or CLI. Security and DevOps teams get centralized visibility into who accessed what, why access was approved, when it expires, and what actions were taken.Assess your multi-cloud access posture. Book a meeting with Apono to see where standing privileges, unused access, and overprivileged accounts create risk across your cloud stack.

Everyone uses developer tools to get through the day. A JSONFormatter to inspect an API response, or a JWT decoder when you need to inspect a token quickly. In most engineering teams, these tools are treated as harmless productivity aids.

In November 2025, researchers discovered that JSONFormatter and CodeBeautify had been storing everything users pasted into them via a save feature that generated shareable links with fully predictable URL structures. A simple crawler could retrieve all of them. 

They found over 80,000 files containing passwords, AWS credentials, Active Directory credentials, database connection strings, SSH session recordings, and API keys. Nowadays, developer tooling is part of the identity plane. The organizations leaving this data exposed spanned government, critical infrastructure, finance, healthcare, and cybersecurity. When they uploaded fake AWS keys to one of the tools, bad actors attempted to use them within 48 hours. The data was actively being scraped. 

The problem was that developers had been routinely pasting credentials into a public website while debugging, because that’s just what you do when you need to read a JSON blob quickly. In fast-moving engineering environments, developers will always reach for whatever gets the job done. You can’t govern your way out of that behavior, and trying to control every tool a developer touches is a losing proposition.

Instead, organizations should ask themselves if their identity and access policies account for where credentials end up. Not just the identities your IAM was written for, but for the tools those identities are touching each day. 

How Simple Developer Tools Become a Production Risk

Even a simple JSON formatting website can improperly collect and store sensitive credentials. The tool doesn’t have to be malicious for the outcome to be. The moment a token, session, or credential is exposed outside the environment it was meant for, the attacker is no longer dealing with a hardened perimeter. They are holding valid access. 

Once a token or credential is out in the open, an attacker has everything they need to start moving up the privilege chain:

1. The exposed token gives the attacker an authenticated foothold in whatever system it belongs to.

2. That token might have access to repository secrets and CI/CD configuration – and by extension, every deployment pipeline that depends on them. 

3. Inside those pipelines, attackers likely look for secrets that were hardcoded and never rotated. 

4. Those credentials allow role assumption in cloud environments, lateral movement across services, and eventually access to data management systems in the production environment. 

The JSON Formatter website is just one example. The same dynamic exists across browser extensions, clipboard managers, AI coding assistants, debugging tools, local config files, and anything else that can touch sensitive developer context. Modern engineering work spreads identity across a wide toolchain—including non-human identities like service accounts and automation credentials—and attackers only need one weak point in that chain.

In practice, that sensitive context shows up in more places than most teams like to admit: local AWS profiles, kubeconfigs, copied JWTs, CI logs, shell history, IDE extensions, browser storage, temporary files, session cookies, and AI prompt history. None of those systems were intended to be the place where identity risk is managed, but all of them can become the place where it is exposed.

What makes this particularly hard to detect is that stolen tokens initially look like legitimate API calls. There is no exploit payload or unusual request pattern that will trigger signature-based detection flags. The attacker, from the system’s perspective, is just a developer doing their job. 

Standing Access Turns a Leak into an Incident

Most developers hold standing, persistent access to sensitive infrastructure because they need it regularly. It’s the path of least resistance when you’re working across production systems and IT operations analysis in a fast-moving engineering environment. 

But a compromised token gives the attacker everything that token was already authorized to do. If a developer’s GitHub token has broad repository access and can trigger production deployments, an attacker with that token inherits that capability exactly. They don’t need to resort to privilege escalation; standing access turns every exposed credential into a pre-approved blast radius.

This is where least privilege often breaks down in practice. Teams may have IAM policies, role structures, and approval processes on paper, but permissions still accumulate over time because modern engineering work changes too quickly for manual access models to keep up.

Why Traditional IAM Doesn’t Close the Gap

Traditional IAM was built for a different environment. It assumes identities are centrally managed and that you’re defending a well-defined perimeter. These tools are designed to control who can authenticate and what an authenticated identity is allowed to do.

But most developers now work across a messy chain of local tools, cloud roles, orchestration layers, and delivery systems. On any given day, a developer may touch their IDE, Kubernetes contexts, GitHub, a CI dashboard, and a secrets manager. Each handoff creates another place where valid identity material can be exposed, copied, cached, or reused.

IAM policies govern what an authenticated identity can do. They say nothing about how that identity’s credentials might be harvested before the authentication handshake. The JSONFormatter attack doesn’t beat your IAM policy; it just steals the credentials that authenticate to it. Role-based access control, SSO, and MFA are all valuable controls, but they were all designed to govern access at the boundary. When an attacker is already operating inside that boundary using valid credentials, those controls offer limited protection.

The deeper problem is that IAM programs were designed around the assumption that access is a relatively static thing to manage. Security programs that can’t keep pace with the dynamic nature of modern engineering will always be working from an outdated access model.

Containing the Blast Radius with Ephemeral Access

There is an architectural solution to this problem, but it is not layering stricter policies on top of a broken access model. It is to make access less durable, less transferable, and less valuable when it leaks. That means shifting away from standing permissions and toward time-bound, task-scoped access that expires automatically.

Just-in-time (JIT) access ensures credentials only exist for a narrow task, for a limited period, and against clearly defined resources. Even when a token or session is exposed, the attacker’s opportunity is smaller, and the blast radius is easier to contain.

The key is making that model usable in real engineering work. Access needs to be self-serve enough for on-call and operational tasks, but still policy-controlled. Expiration needs to be automatic, not dependent on manual cleanup. Approvals should appear where risk justifies them, not as a blanket slowdown for every request. And every grant should be visible in a reliable audit trail.

That is the shift many teams still have not made. They treat privileged access as an exception workflow when it should be an operating model. Ultimately, JIT is not a replacement for identity governance or privileged access management. It is the operating model that makes least privilege workable in fast-moving engineering environments.

If engineers have to fight a slow approval chain every time they need production access, they will look for workarounds. Good JIT implementations cut that friction down enough that teams can use them during normal work.

Context-aware policy can sit further upstream, adding a check before access is granted at all, which is an essential control for supply chain risk management. JIT answers how long access should exist. Context-aware controls help answer whether this request makes sense right now. If someone is requesting access from an unusual location or in a way that does not match normal behavior, the request can be blocked before just-in-time access is granted. This is a fundamentally different posture than the “grant and forget” model that most engineering organizations have slipped into over time. 

Developer Tooling is Only Going to Get Riskier

The JSONFormatter incident will not be the last case of a trusted developer tool turning into an identity risk. If anything, it is an early signal of where this problem is heading. As engineering organizations rely more heavily on AI assistants, local automation, browser-based tooling, and machine identities, more systems will act on behalf of developers and production operators. 

You need to be thinking beyond what the developer can reach, to what their plugins, local environments, and AI-assisted tooling can reach on their behalf. Security teams should stop asking only who has access and start asking where that access can travel. Standing permissions are impossible to justify in these environments. The more tools that can touch identity, the less acceptable it is for powerful permissions to remain broadly available and continuously valid.

Modern Identity Risk Demands Ephemeral Access

The answer is not to slow engineers down or force them through brittle manual controls. It is to make access short-lived, contextual, and automatically reversible by default. In an environment where every developer convenience can become an identity exposure point, reducing standing access is no longer just good hygiene. It is one of the clearest ways to reduce blast radius without slowing delivery.

Developer tooling is now part of the identity attack surface, which means securing access can no longer stop at the login layer. Apono helps security teams replace standing access with just-in-time, policy-controlled access across cloud, Kubernetes, databases, and developer infrastructure. The result is that leaked credentials are less useful, access is easier to govern, and the blast radius is dramatically reduced. With Apono, teams can enable self-serve, granular access through Slack, Teams, or the CLI, with auto-expiring permissions and break-glass workflows. Download Why Standing Access Keeps Causing Breaches: A Practical Guide to Continuous Least Privilege to see how leading teams reduce standing access and contain identity risk. Or, book a demo to see how Apono enforces just-in-time access in practice.

There’s never a good time to lose a production database, but losing one to your own AI coding agent on a Friday afternoon has to rank near the bottom of the list.

That’s the backdrop to the PocketOS incident, and it’s the clearest case yet for why AI agent security and intent-based access control belong at the top of every cloud security roadmap this year.

Founder Jer Crane’s full account is worth reading, but the short version is this:

• An AI coding agent running inside Cursor encountered a credential mismatch in staging
• It decided on its own that the fix was to delete a Railway volume
• It found a long-lived API token in an unrelated config file
• It ran the destructive command and the production database, plus every backup that lived alongside it, was gone in nine seconds

The flashy parts have already been covered, including the agent’s written confession and Railway’s questionable backup architecture. The more useful question is how an agent that was working in staging ended up taking down production at all.

Why Over-Privileged Tokens Aren’t the Whole Story

It’s tempting to frame this as “the token had too many privileges, fix the token, problem solved.” Stopping there misses what makes agentic AI different.

Two things had to be true for nine seconds of catastrophe.

First, an over-privileged credential was sitting in a config file. The token had been created for the narrow purpose of managing custom domains through the Railway CLI, but it carried blanket authority across the entire Railway API, including destructive operations.

Second, an agent decided, on its own, that calling volumeDelete was a reasonable response to a credential mismatch in staging. No human asked it to and no prompt instructed it, but the agent encountered friction, optimized for task completion, and chose the most direct path it could reason its way to.

Take away either condition and the incident doesn’t happen.

How AI Agents Make Destructive Decisions Without Being Asked

The PocketOS agent is a textbook example of what we’ve written about before as the overreach failure mode in agentic AI security.

Agents are mission-driven and optimize for task completion. If the objective is “solve the problem,” they may take increasingly aggressive actions that look logical in isolation but are destructive in context.

What they’re missing:

• Any sense of proportionality
• Any awareness of long-term consequences
• Any reason to slow down, since they operate at machine speed across real systems

Imagine you’re trying to turn off a light but can’t find the switch. You’d try reasonable solutions, look for another switch, ask someone, maybe unscrew the bulb. You’d never burn the house down to make sure the light went out, because you understand the consequences are wildly disproportionate to the goal.

An agent might.

That’s effectively what happened here. The agent hit friction, treated it as a problem to solve, and reached for the most direct API it could find, which made the path entirely logical from its perspective even though it was insane from every other one.

This is the core risk in agentic AI security: non-deterministic, mission-driven software operating with static privileges.

It’s not a one-off either. Anthropic’s own testing on Claude Opus 4.6 found that when the model hit roadblocks getting the access it needed, it went looking for hardcoded credentials and Slack tokens elsewhere in the environment. Different model, different scenario, same overreach pattern.

Why AI Agent Guardrails Need to Live Outside the Agent

When asked to explain itself, the PocketOS agent enumerated the safety rules it had been given and admitted to violating every one of them.

That’s worth sitting with for a moment, because the internal guardrails were clearly present and the model could even articulate them after the fact, but none of that prevented the deletion.

The controls inside an agent’s own context window are not, and probably cannot be, the layer that prevents catastrophic action. You need an outside layer the agent cannot reason its way around.

How Intent-Based Access Control Stops Agent Overreach

That’s the gap Apono Agent Privilege Guard is built to close.

The premise is straightforward: no agent holds standing privileges to sensitive resources. Instead, every privilege is created dynamically at the moment the agent needs it. Each privilege is:

• Generated at runtime, never pre-provisioned or stored anywhere the agent could find later
• Evaluated against the agent’s stated intent before anything is granted
• Scoped Just-in-Time and Just-Enough to perform exactly the task at hand and nothing more
• Revoked automatically the moment the task is done

The result is an agent that can only do what you actually want it to do. Even if it decides on its own to escalate, it has no broader privileges available to escalate into.

Apply that model to the PocketOS chain and the blast radius collapses at three checkpoints.

At the credential checkpoint, the long-lived token in the config file simply doesn’t exist as a standing privilege. The agent has no blank-check credential to discover and reuse.

At the intent checkpoint, the agent has to declare what it’s trying to do before any privilege is issued. “Fix a credential mismatch in staging” and “delete a production volume” are different categories with different risk profiles. The mismatch between stated intent and attempted action gets caught before it becomes destruction.

At the human-in-the-loop checkpoint, sensitive operations against production trigger a Slack approval before they execute. An engineer sees the actual command, the actual target, and the actual reasoning.

The deletion takes nine seconds and a Slack approval takes ten, which means that ten-second window is the entire difference between a normal afternoon and a thirty-hour recovery effort.

Securing AI Agents Starts With Zero Standing Privileges

Copilots and coding agents are already running inside your engineering org, with tools like GitHub Copilot, Cursor, Claude Code, and Cline calling APIs as your engineers and inheriting your engineers’ privileges, and most security teams have very little visibility into any of it.

PocketOS is the headline this week, but it’s far from the first:

• AWS’s Kiro coding agent contributed to a 13-hour outage earlier this year
• Replit’s agent wiped a customer database during an active code freeze, then tried to cover it up
• Cursor publicly acknowledged a Plan Mode bug in December that let an agent ignore a user’s explicit “DO NOT RUN ANYTHING” instruction

The mechanism changes every cycle but the underlying exposure never does, because we keep giving non-deterministic systems deterministic privileges and hoping the model has the judgment to use them well.

It doesn’t take a malicious actor to set the house on fire. It takes three things:

• One over-privileged token
• One mission-driven agent
• A moment when no human is watching

The fix isn’t to panic about AI agents. It’s to assume agents will sometimes try to do crazy things on their own, and to make every privilege grant temporary, scoped, intent-driven, and approved by a human when the stakes warrant it.

That’s what Zero Standing Privileges means in practice for the agentic era, and it’s the bar every security program should now be building toward.

If this is the week your team starts rethinking how agents get their privileges, our white paper Securing the Agentic Enterprise: How Intent-Based Privilege Controls Make AI Agents Safe Enough to Deploy goes deeper on the failure modes covered here, the Kiro and Replit incidents, and the architectural shift from static IAM to intent-based access controls.

As more organizations move past experimentation and start planning real AI agent deployments, the same set of concerns keeps surfacing in our conversations with security teams. Whether the worry is a shadow agent that shows up uninvited or a sanctioned agent going rogue, the questions tend to cluster around control:

• How do we keep agents inside the lines we’ve drawn?
• What happens when a shadow agent we didn’t sanction shows up in the environment?
• What if one of our agents gets manipulated, hallucinates, or just goes off-script and tries to do something it shouldn’t?

These are the right questions to be asking, and they share a common answer that’s more concrete than most people expect.

AI agents are only as dangerous as the privileges they can reach. Move to a Zero Standing Privileges model, route every privilege request through a single control plane that grants access just-in-time and just-enough for the task, and most of the worry goes with them.

That’s the whole game. Let’s unpack why it works.

Why AI agent risk lives at the privilege layer

The concerns above feel scary because they show up in real, painful ways. Three patterns we see again and again:

Shadow agents. Your engineers are running Copilot, Cursor, and Claude Code right now. Those tools inherit your developers’ full credentials, and most security teams have very little visibility into what they’re actually doing on their behalf.

We wrote about why static privilege models break down in this world in more detail here.

Agents that can burn the house down. Amazon’s own AI coding tool caused a 13-hour AWS outage. Their postmortem said the quiet part out loud: it was a privilege problem.

The agent had broader permissions than anyone realized, and once it went sideways there was nothing in place to slow it down. Replit’s coding agent did something similar to a production database earlier in the year.

Social engineering of agents. Agents trust their inputs. They can be manipulated through prompt injection, hallucinate their way into bad decisions, or get tricked into actions they were never supposed to take.

And they do it at machine speed, against real systems, with real consequences.

Notice what these have in common. The danger isn’t really the agent. It’s what the agent can reach.

Privilege is where you actually have leverage over AI agents

Here’s a thought experiment. Take away every privilege an agent has. What damage can it do?

None. It’s a chatbot that can’t touch anything.

Now hand it admin credentials to your production database. Now what?

This is why privilege is the place where security teams have the most leverage. Trying to control agent behavior at the agent layer is a fight without an end.

There are too many agents, too many models, too many ways they can be manipulated, and the field is moving too fast for that approach to hold up over time.

Privileges, on the other hand, are concrete. They live in your cloud, your databases, your code repos. You already know how to manage them. The shift is in how you grant them.

Our 2026 State of Agentic AI Cyber Risk Report found that 98% of security leaders have slowed agent deployments because of exactly these concerns.

They’re not being paranoid. They’re being responsible. The controls they have today were never designed for identities that act autonomously, at machine speed, on non-deterministic inputs. The encouraging part is that the missing piece, runtime privilege control, is something you can put in place now.

How Zero Standing Privileges changes the equation

When privilege itself is created at runtime, scoped to the task, and destroyed the moment the work is done, you get ephemeral credentials granted just-in-time and just-enough for the work at hand. That shifts the agent risk story in some important ways:

• Standing credentials don’t exist for agents to inherit. That shadow Copilot can’t grab anything sitting around because nothing is sitting around.
• Every privilege request flows through one control plane. Human, copilot, or autonomous agent, the path is the same. No credential gets minted outside of Apono.
• Risk and intent decide what flows and what stops. Low-risk reads happen automatically with no friction. Sensitive operations escalate to a human in Slack before they execute, with full context for the approver.
• Everything is logged end to end. Declared intent, the action that followed, the approval decision, the outcome, all tied back to the identity that started it.

This is also why the rogue AI agent scenario gets a lot less terrifying in practice. An agent that’s been manipulated or hallucinates its way into a destructive request still has to come through us to actually do anything.

Intent-based access control compares what the agent is asking for against what it said it was trying to do. If the request doesn’t match that declared intent, or if it crosses a sensitivity threshold, a human sees it before it executes. The blast radius collapses to whatever the agent legitimately needed in this moment, and nothing more.

The agent ends up being able to do more, not less, because the guardrail is intelligent rather than binary.

The bottom line for CISOs

You don’t need to wrangle every agent that shows up in your environment. You need to make sure the only path to anything sensitive runs through a control plane that understands what’s being asked and why.

Eliminate standing privileges, control privilege at runtime based on actual intent and risk, and you defuse the shadow AI agent problem before it has a chance to hurt you.

That’s what lets your team stop blocking agent deployments and start enabling them. The way to win with AI isn’t to slow it down. It’s to give it the right rails to run on.

Assess Your AI Agent Access Risk

AI agents are moving fast, but sensitive access should not be left to chance. Book a personalized assessment with Apono to identify where agent privileges may be creating hidden risk and how to put the right runtime controls in place before they become a security problem.