Permission creep rarely looks dangerous at first. It starts as a temporary fix, such as granting an admin role to unblock a deployment. Over time, those temporary decisions become permanent standing permissions. The result is an AWS estate littered with high-privilege roles that sit idle for months, expanding your attack surface without anyone actively noticing.

It takes organizations an average of 277 days to identify and contain a breach. In cloud-native environments where attackers can move laterally in minutes, relying on quarterly IAM reviews and reactive cleanup simply doesn’t scale.

And yet, that’s how most teams manage access today. To move beyond the “whack-a-mole” approach to security, teams must shift from discovering access risks to preventing them from being introduced in the first place. That means eliminating unnecessary standing permissions and enforcing least privilege continuously, which is where AWS IAM Access Analyzer plays a role. 

What is IAM Access Analyzer?

AWS IAM Access Analyzer is a policy analysis service that uses automated reasoning to identify unintended public and cross-account access to AWS resources. It continuously evaluates resource-based policies and trust relationships to determine whether external principals, including other AWS accounts or federated users, can access your resources. 

IAM Access Analyzer acts as a continuous auditing layer. Rather than scanning for simple misconfigurations, it analyzes policies to identify all policy-based access paths to a resource, including access from outside your defined zone of trust.

IAM sprawl and overly permissive policies are natural side effects of clouds at scale and the push for operational speed. AWS IAM Access Analyzer acts as a continuous auditing layer, strengthening identity and access governance by surfacing unintended access paths before they become systemic risk. Broad permissions granted to unblock deployments and the rapid growth of machine identities all contribute to standing privilege and policy complexity.

5 Reasons to Use IAM Access Analyzer

1. Discover unintended access early: The tool uses automated reasoning to flag resources shared with external principals or the public the moment a policy is changed. This allows you to remediate accidental exposure before a bad actor can exploit the opening.
2. Reduce IAM policy sprawl: Unlike many traditional IAM tools, Access Analyzer focuses specifically on policy-level exposure inside AWS environments.
3. Support least-privilege initiatives: It generates fine-grained policies based on actual historical activity found in CloudTrail logs. This replaces broad “Admin” or “FullAccess” policies with exact permissions tailored to what a user or service actually does.
4. Improve security reviews and audits: AWS Analyzer provides a centralised dashboard of findings that serves as a “source of truth” for auditors. It proves you are actively monitoring access and provides a clear trail of remediated risks for compliance standards, such as SOC2.
5. Prevent misconfigurations at deployment time: IAM Access Analyzer includes policy validation capabilities that act like an automated pre-deployment security checker. When integrated into CI/CD pipelines, it can block overly permissive or malformed IAM policies before they reach production.

IAM Access Analyzer Resource Types

Knowing which resources IAM Access Analyzer evaluates is critical because the tool doesn’t monitor everything; it monitors only a specific subset of AWS resource types that support resource-based policies. Understanding the perimeter of these boundaries allows security teams to identify security blind spots, as resources not covered may still be the entry point for significant access risks.

Table 1: Resource Types

AWS Resource	Risk if Misconfigured	Potential Impact
Amazon S3	Sensitive files (PII, financial records, internal documents) become publicly readable or accessible to unauthorized third-party accounts.	Data leaks, reputational damage, customer trust erosion, and regulatory fines under GDPR, CCPA, HIPAA, and similar frameworks.
IAM Roles	Overly permissive or misconfigured trust policies or misuse of permissions like IAM:PassRole can allow external principals to pass privileged roles to AWS services.	Privilege escalation, administrative takeover, lateral movement, and data theft.
AWS KMS (Keys)	Key policies allow unintended cross-account or public access to encryption keys.	Decryption of sensitive data (database credentials, EBS volumes, application secrets). Encryption becomes functionally ineffective — encryption is only as strong as its key policy.
AWS Lambda (Functions)	Overly broad invocation permissions allow unauthorized accounts to execute functions.	Cost spikes (“denial of wallet”), unauthorized logic execution, backend manipulation, and service disruptions that contribute to downtime losses.
Amazon SQS (Queues)	Queue policies grant access to unauthorized entities.	Message interception, data theft from payloads, or injection of malicious commands into application workflows.
Amazon SNS Topics	Topic policies allow unauthorized publishing or subscribing.	Triggered automation abuse, data leakage, and downstream system manipulation.
AWS Secrets Manager	Resource policies expose secrets to unintended principals.	Credential theft (API keys, database passwords), leading to downstream system compromise.
Amazon RDS (Snapshots)	Snapshots shared publicly or cross-account without controls.	Full database exfiltration and restoration in attacker-controlled environments, bypassing VPCs, firewalls, and security groups.
Amazon ECR (Repositories)	Overly permissive repository policies expose or allow modification of container images.	Supply chain compromise, exposed infrastructure secrets, and image poisoning that propagates across environments.

7 Tips for Using IAM Access Analyzer

1. Start with High-Risk Resources First

Access Analyzer categorises findings based on the resource type and the level of access. Some resources, such as S3 buckets and IAM roles, pose a significantly higher risk if misconfigured than others. 

Focus your initial “cleanup” phase exclusively on Public and Cross-Account findings for S3 buckets, SQS queues, and KMS keys. In the console, use the filter isPublic: true to identify resources that are accessible to anyone on the internet. Remediating these “open doors” provides the highest immediate return on security posture.

2. Treat Findings as Signals, Not Noise

A finding is based on logic-based reasoning (provable security), indicating a real policy-permitted access path. The same type of misconfiguration is routinely identified during offensive security assessments and red team exercises.

Avoid alert fatigue by integrating findings into your existing incident response workflow. Use Amazon EventBridge to trigger automated notifications (via SNS or Lambda) when a high-severity finding is generated. This best practice transforms the tool from a static report into a real-time security signal that prompts immediate investigation.

3. Validate IAM Policies Before Deployment

Policy validation in IAM Access Analyzer is a proactive security layer that acts as a “linter” for your IAM policies. It complements other cloud security controls, including infrastructure scanning and API security tools, by preventing overly permissive access from being deployed in the first place.

Shift security left by integrating the IAM Access Analyzer SDK into your CI/CD pipelines (e.g., GitHub Actions or GitLab CI). Set a gate that prevents the deployment of any CloudFormation or Terraform template that contains “Security” or “Error” level findings. 

4. Review Findings Regularly, Not Once

Engineering teams are constantly deploying new microservices, experimenting with serverless functions, and tweaking database connections. This high velocity creates a moving target for security.

Establish a weekly or bi-weekly cadence for your cloud security team to review the findings dashboard. Use the “Archive” function for findings deemed acceptable, but revisit those archived rules quarterly to ensure the business justification for that access still holds true.

5. Correlate Findings with Real Usage

The Unused Access Analysis feature looks at your CloudTrail history to see if the permissions you’ve granted are actually being used. It identifies “zombie” roles and unused IAM user credentials. 

When you identify an unused role via IAM Access Analyzer, your first instinct might be to hit “Delete.” However, in complex enterprise environments, some roles are “cyclical” (used only for annual disaster recovery tests or specific tax-season workloads).

6. Document Intentional Exceptions

In many complex environments, certain risky permissions are actually necessary (e.g., a cross-account role for a security vendor or a public S3 bucket for website hosting). When you encounter an intentional finding, don’t just ignore it. 

Create an Archive Rule with a specific, descriptive name and a “Reason” tag to create a documented audit trail. If an auditor asks why a specific account has access to your data, you can point to the Archive Rule as evidence of a conscious, documented business decision.

7. Eliminate Standing Permissions

The most effective way to prevent recurring findings and policy drift is to move away from permanent, high-privilege roles that sit idle 99% of the time. 

Transitioning to a Just-In-Time (JIT) access model represents a fundamental shift from static to dynamic security. It solves the root cause of the findings that IAM Access Analyzer flags by ensuring that high-risk permissions only exist when they are actively being used.

Closing the Gap Between Findings and Enforcement

AWS IAM Access Analyzer provides critical visibility into overly broad or risky access paths. For cloud-native organizations operating at scale, this insight is indispensable. However, visibility alone doesn’t reduce the attack surface. 

If you rely solely on periodic IAM cleanup, you could be trapped in a cycle of detection where standing permissions continue to accumulate, and audit pressure increases. In these cases, automated JIT access changes the landscape. 

With Apono, developers can request granular, time-bound access directly from Slack, Microsoft Teams, or CLI, with permissions that auto-expire once the task is complete. Break-glass and on-call flows allow rapid production remediation without permanently expanding privilege. Comprehensive audit logs and automated reporting provide clear visibility into who accessed what, when, and why, simplifying compliance and internal audit requirements. Learn how to ensure continuous access compliance across your entire stack, or see how automated Just-In-Time access works in practice by booking a live demo.

If you’re evaluating a move away from StrongDM, you’re probably asking two questions at the same time:

• Is it worth the disruption?
• If we switch, how do we do it without creating new risk and operational chaos?

You might be frustrated with the UI, or you may have discovered that Slack integration isn’t native and access requests still feel slower than they should. Upgrade conversations may be happening more often than meaningful product improvements.

Over time, though, the concern often becomes more structural. Static roles and session-based access no longer align with where your environment is headed.

This decision isn’t really about Slack or pricing tiers. It’s about whether your access model can support what comes next.

Why the Access Model Itself Has to Change

Your infrastructure is far more dynamic than it was a few years ago, with a broader cloud footprint and automation woven into nearly every workflow. AI agents are beginning to initiate actions rather than simply surface insights, executing changes at a pace that exposes weaknesses in overly broad, standing privileges.

When static roles sit underneath that level of autonomy, risk compounds quietly. Not because someone misconfigured a policy, but because the model itself was designed for a different era.

If you are going to leave StrongDM, the opportunity is not simply to replace a vendor. It is to rethink how privilege is granted, scoped, and revoked across your environment.

That means moving from session-based control to intent-driven access, from static roles to dynamic, ephemeral permissions, and from standing privilege to a Zero Standing Privilege model by default.

This is not just a product change. It is a shift in approach.

Here is how to execute that transition in a structured, low-risk way.

Mapping Out Your Transition Away from StrongDM to Zero Standing Privilege

If you’re making this move, the goal is not a clean vendor swap. The goal is to eliminate standing privilege without disrupting engineering velocity.

That requires structure.

1. Start with Visibility, Not Replacement

Before migrating anything, build a clear picture of what exists today.

Export your StrongDM inventory:

• Resources such as databases, Kubernetes clusters, servers, and bastions
• Users and groups
• Current role mappings
• Access frequency data

At the same time, export your identity source of truth, whether that is AWS IAM Identity Center or another IdP. Capture users, groups, and permission sets.

The objective is not to recreate your current structure inside a new tool. It is to understand where standing privilege exists so you can redesign access around intent and risk.

2. Redefine Access Around Intent

This is where the shift in approach becomes real.

Instead of asking, “What roles do we have?” ask:

• What tasks are people actually performing?
• What is the minimum scope required for each task?
• How long should that access exist?
• What level of approval matches the risk?

For example:

• Debugging production may require read-only database access for two hours.
• Running a migration may require write access for thirty minutes with approval.
• Kubernetes namespace access may need to be scoped tightly and time-boxed.

The principle is straightforward: access should reflect intent, align with risk, and expire automatically once the task is complete.

This same model applies to automation and AI agents. If a workflow needs to rotate credentials or deploy infrastructure, it should receive only the permissions required for that action, only for the duration of that action, and nothing more.

3. Plan a Parallel Transition

Avoid big-bang cutovers.

Run StrongDM and your new access platform in parallel while you migrate in controlled waves.

A practical sequence:

• High-frequency resources engineers use daily
• Critical systems with moderate usage
• Long-tail resources that are rarely accessed

For rarely accessed systems, add guardrails. Require justification and approval. If something has not been touched in months, a new request should prompt a conversation.

This phased approach reduces operational risk and builds confidence as teams adapt to dynamic access.

4. Migrate by Resource Type with Discipline

As you move resources over, focus on replacing standing access with scoped, ephemeral flows.

For Kubernetes:

• Grant namespace-level or workload-level access
• Make elevated privileges short-lived and approved

For databases:

• Separate read access from write access
• Keep write and admin privileges tightly time-bound

For servers:

• Replace perpetual admin rights with requestable elevation

For cloud permissions:

• Convert static group memberships into requestable, expiring entitlements

The principle remains consistent across every resource type: eliminate permanent privilege wherever possible.

5. Operationalize the Model

Zero Standing Privilege succeeds when the secure path is also the easiest path.

Publish clear internal guidance on:

• How to request access
• How to choose appropriate scope and duration
• What justification looks like for higher-risk requests

Train DevOps teams on flow design and policy governance. Train engineers on using chat, portal, or CLI workflows. Maintain a weekly cadence during rollout to review feedback and refine policies.

For guidance on how to plan out your access policies, check out our Just-in-Time Access Policy Design for Cloud Security Teams explainer blog.

The goal is not friction. It is controlled flexibility.

Laying the Ground for Your AI Future

As automation deepens and AI agents begin to initiate actions independently, the access model beneath them determines whether risk scales with capability.

Static roles and standing privilege were designed for a human-centric world. Agentic systems operate continuously, at speed, and often across multiple services. If those systems inherit broad permissions, the blast radius expands silently.

A Zero Standing Privilege approach ensures that access is created dynamically, scoped to intent, bounded by risk, and revoked automatically once the action is complete.

That foundation allows you to deploy more capable automation and AI without increasing systemic exposure.

Switching away from StrongDM may be the catalyst.

Adopting an intent-driven, risk-aware Zero Standing Privilege approach is the real outcome.

Done correctly, this transition does more than address UI frustrations or integration gaps. It positions your organization to scale human and autonomous access safely, deliberately, and with confidence.

Considering Life After StrongDM?

Many teams exploring a StrongDM replacement want to understand one thing first: how to migrate safely without slowing engineering down.

Book a short strategy call with our team to review your environment and discuss how organizations move from static roles to a Zero Standing Privilege model. Moving Beyond StrongDM_ A Pract…

As a thank-you for your time, Qualified StrongDM customers receive a $200 Amazon gift card after completing the session. $200 Amazon gift card.

For many organizations, Grafana is a central operational system. Engineers use it to investigate issues, analyze logs, review infrastructure metrics, and query production-connected databases. But while dashboards are visible, the real sensitivity lies in the underlying data sources Grafana connects to.

These data sources often include systems such as logs stored in Elasticsearch or OpenSearch, SQL databases like PostgreSQL or MySQL, and Amazon CloudWatch metrics. Access to these systems can provide visibility into production telemetry, infrastructure performance, and potentially sensitive operational data.

The challenge is clear: How do you give engineers fast access to Grafana data sources without maintaining standing, over-privileged access?

The problem: static access in dynamic environments

In standard operating environments, Grafana data sources are typically accessed via long-lived IAM roles or broad group assignments. This “always-on” model is designed for speed, ensuring engineers have immediate visibility during critical incidents without the friction of authentication and authorization delays.

However, for organizations handling highly sensitive data or operating under strict regulatory constraints, this approach can introduce unique operational challenges, such as:

• Persistent permissions may exceed the actual window of time an engineer needs to perform a task.
• Distinguishing between routine monitoring and targeted investigative access can become difficult during compliance reviews.
• Compliance and audit friction.

For these specialized scenarios, teams are moving toward a Just-in-Time access model. This allows for a security posture that remains dormant by default and activates only when a specific, verified need arises, aligning high-stakes security with operational flow.

Governing Grafana data sources with Just-in-Time access

Apono integrates with Grafana to continuously discover configured data sources. Each discovered data source becomes a governed resource within Apono’s access control framework.

Security and platform teams define policies to specify:

• Who can request access
• Which data sources they can access
• Whether human approval is required
• Maximum access duration
• Contextual conditions (such as on-call status)

Instead of granting permanent access to a logs or metrics data source, organizations move to an on-demand model.

A typical workflow

1. An engineer needs to query a specific Grafana data source (for example, a logs or metrics backend).
2. They submit a request for access.
3. Apono evaluates the request against predefined policies, and user and asset context.
   Access is granted for a defined time window.
4. When the time expires, access is automatically revoked.

There are no permanent role changes and no lingering privileges. Access becomes scoped, time-bound, and policy-driven.

Reference architecture: Just-in-Time access for Grafana data sources

The diagram below illustrates how Apono integrates with Grafana and connected data sources to enforce time-bound access while preserving operational workflows.

In this model:

• Grafana connects to multiple data sources (logs, metrics, traces, cloud services, databases).
• Apono integrates with Grafana to discover and govern access to those data sources.
• Access requests are evaluated against centralized policies.
• Permissions are provisioned temporarily and revoked automatically.

This architecture ensures that access to observability data is dynamic and controlled, rather than static and persistent.

Incorporating operational context with Grafana Cloud IRM

For teams using Grafana Cloud IRM, access decisions can incorporate operational signals such as:

• On-call schedules
• Active incident participation
• Responder roles

By integrating Grafana Cloud IRM with Apono, organizations can align access with real-time operational responsibility. For example:

• Only an engineer currently on call can receive immediate access to a production data source.
• Access can be limited to the duration of an active incident.
• Permissions automatically expire when responsibility shifts or the incident is resolved.

This ensures access reflects real-time operational context rather than static IAM group membership.

Benefits for users

Organizations using Grafana together with Apono report improvements across both security and operational efficiency. Here’s a closer look at the benefits:

• Zero Standing Privileges: Access to Grafana data sources is granted only when required and automatically revoked.
• Faster investigations: Engineers obtain faster access without waiting for manual IAM updates.
• Reduced blast radius: Short-lived permissions limit exposure if credentials are compromised.
• Policy-driven governance: Access policies are centrally defined and consistently enforced.
• Full audit visibility: Every access event is logged, supporting compliance and review processes.

Getting started and next steps

The Apono integration is available for both on-premises Grafana and Grafana Cloud. 

As a practical first step, we recommend identifying which of your Grafana data sources connect to sensitive production systems and are currently governed by standing roles.

From there, teams can:

• Integrate Grafana with Apono
• Discover existing data sources
• Define time-bound access policies
• Gradually remove permanent access assignments

As observability environments grow in scale and importance, implementing Just-in-Time and least privilege access for Grafana data sources helps minimize risks without slowing teams down.

To learn more, please explore our integration documentation.

Earlier this year, AWS experienced a 13-hour outage that was reportedly linked to one of its own internal AI coding tools. 

Apparently, their Kiro agentic coding tool thought that there was an issue with the code in the environment, and that the best way to fix it was to simply burn it to the ground. 

In their statement, AWS stated that the issue here wasn’t necessarily with their agent but with the user access controls where the human had more privileges than they were supposed to have, which allowed the agent to go forth and cause the outage.

Regardless of where the breakdown occurred, the incident raises serious questions about how we approach Agentic AI Security as autonomous systems begin operating inside sensitive environments.

Organizations are under pressure to integrate more agents into their workflows in hopes of harnessing their scale and speed to increase velocity. At the same time that they share the desire for accelerated productivity, CISOs have real concerns about releasing these unpredictable agents near to their crown jewels. 

In a recent survey of some 250 security leaders, a whopping 98% of respondents reported that they are slowing the adoption of agents into their organizations due to security concerns. 

These leaders are not resistant to innovation. They recognize that we’re undergoing a structural shift — from deterministic software to autonomous systems. That shift fundamentally challenges traditional models of Agentic AI Security.

The Catch: Non-Deterministic Systems

Before going further, it’s worth being explicit about what we mean by deterministic because this is where many of our assumptions quietly break.

A deterministic system is one where the same input, under the same conditions, will always produce the same output.

A non-deterministic system behaves differently. The same request can yield different outcomes depending on context, prior state, interpretation, or probabilistic reasoning. The system is not simply executing instructions. It is deciding how to act.

Traditional security models, including Zero Trust, implicitly assume determinism: software is predictable, permissions are static, and risk comes primarily from humans misusing or abusing access.

AI agents break that assumption.

When Agents Go Wrong

There are two primary failure modes in Agentic AI Security:

1. Manipulation

Social engineering has always targeted humans — exploiting context, urgency, and framing. Now that same pressure can be applied to machines. Prompt injection, malicious instructions embedded in documents, or carefully crafted inputs can push an agent into behavior it was never meant to perform.

An agent may:

• Send sensitive information externally
• Access systems outside its intended scope
• Trigger workflows with cascading impact
• Modify or delete critical data

The attack surface expands because the agent acts with legitimate credentials.

2. Overreach

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

They don’t understand proportionality. They don’t understand long-term consequences.

And critically, they operate at machine speed across real systems.

This is the core risk in Agentic AI Security: non-deterministic systems with deterministic privilege grants.

Hallucinations and unintended behavior

Agents consume large volumes of data. They summarize, correlate, and infer. 

And sometimes they get it wrong. Even when the reasoning sounds coherent, the action can be harmful.

Consider a simple analogy.

You’re trying to turn off a light but can’t find the switch. At first, you try reasonable solutions — look for another switch, ask someone nearby, and maybe unscrew the bulb.

But as frustration grows, more extreme options start to appear. What if you cut power to the entire house? What if you call the utility company? What if — in the most absurd version — you burn the house down just to guarantee the light goes out?

You would never do that, because you understand proportionality. You understand consequences.

An agent probably does not. 

In a recent report released by the Claude team on testing of their Opus 4.6 model, they found that when it hit roadblocks to getting the access it needed to perform a task, it simply “found” other credentials like hardcoded creds and Slack tokens to get to where it wanted to go. It then proceeded to attempt price colluding and got better at hiding its bad behavior from its monitors. You can get a deeper dive in this fascinating video here below.

As we see, an agent will figure out how to escalate in pursuit of its goal. If the objective is “solve the problem,” it may choose the most direct path available — even if that path is destructive.

Adding to the challenge is that an agent operates at machine speed, across real systems. This makes it incredibly difficult monitor and control its thousands of decisions at scale

This is the core risk at the center of Agentic AI Security: non-deterministic, mission-driven software operating with static privileges.

So then given the risks, how do we protect ourselves in a world where our software behaves more like a person than a script?

Many teams skip this question and deploy agents everywhere.

Let’s slow down and map the risks from the perspective of what they are allowed to do.

Levels of Agent Autonomy

Not all agents are created equal.

Some are little more than conversational interfaces. Others can read internal systems. Some can generate artifacts, trigger workflows, or modify production environments. Lumping them together obscures the real risk.

To understand the challenges ahead, we need to break agents down by capability. Risk does not emerge all at once. It compounds as agents move from observing, to communicating, to reading, to acting, and finally to modifying.

Up to this point, the risks are mostly conceptual. From here on, they become operational and they compound quickly. 

The Hidden Power You’re Granting by Default

In many deployments, agents are given broad capabilities by default — for convenience and speed — without fully accounting for the risk.

Common examples include shell or command execution, file read/write/delete access, browser access with stored sessions, broad internet access, background execution, multi-channel messaging, and external tool execution.

Every one of these must be explicitly evaluated.

This is not optional.

1. The Environment Matters

Agents run somewhere.

That environment must be designed as hostile-by-default.

Minimum requirements:

• No admin privileges
• Hardened OS
• No sensitive data present
• Explicitly scoped network access

If the agent shouldn’t see it then it shouldn’t exist on that machine.

2. Communication Control

Limit which channels the agent can use.

Channels restricted only to the owning user are critical to prevent:

• External influence
• Silent data exfiltration

3. Tool Access and Least Privilege

Every tool granted to an agent should be evaluated along three dimensions:

• Read
• Create
• Modify/Delete

Using them securely means implementing some commonsense practices:

• Never store passwords or secrets on the agent’s machine.
• Use short-lived credentials, injected only when required.
• Apply least privilege guardrails rigorously.

From Zero Trust to Continuous Adaptive Trust

Zero Trust was a major evolution. It forced organizations to stop assuming implicit trust and to validate identity before granting access.

But Zero Trust still assumes something critical: that once access is granted, software behaves predictably.

AI agents invalidate that assumption, forcing a redefinition of Agentic AI Security beyond identity validation alone.

What’s required instead is a model of Continuous Adaptive Trust. This is sometimes described as Just-in-Time (JIT) Trust.

In this model, access is not static. It is ephemeral, scoped, and continuously evaluated.

Access becomes:

• Time-bound
• Purpose-bound
• Context-aware
• Continuously reassessed

Instead of long-lived credentials and standing privileges, agents receive narrowly scoped, temporary grants aligned to a specific task. These grants expire automatically.

Trust is derived not just from identity and context, but from observed intent and behavior. This includes prompts issued, tools invoked, APIs called, execution patterns followed.

Intent is a critical component to securely managing agents because it is the best indicator of what we want it to accomplish. Furthermore, we need to be able to understand the relationship between our intended action and the behavior that the agent is trying to carry out.

If there is a discrepancy between the two, then this is a red flag that we might have a problem.

When behavior deviates from expected intent, the system responds dynamically:

• Privileges can be reduced
• Scope can be constrained
• Human approval can be triggered
• Access can be suspended

By creating guardrails that continuously assess intent and the risk level of behaviors to determine where agents can work uninterrupted and where a human is required to be in the loop, organizations can confidently deploy autonomous agents and reap the benefits of exponential productivity in their business.

So Can Every Employee Have an AI Agent?

Not under static entitlement models. Not a chance.

But under Continuous Adaptive Trust — with ephemeral access, intent/behavioral monitoring, and real-time privilege adjustment — the answer becomes more nuanced.

We’re at an inflection point in Agentic AI Security.

The future is clearly agentic. The productivity upside is undeniable. But to embrace it safely, we must evolve privilege management. Static entitlements cannot govern dynamic systems. Adaptive privilege models — aligned to intent, risk, and context — are the foundation of sustainable Agentic AI Security.

Ready to Stress-Test Your Agentic AI Security?

Before deploying autonomous agents broadly, understand how your current privilege model holds up under real-world scenarios.

The Agent Privilege Lab is an interactive simulation tool that lets you explore agent autonomy levels, attack paths, and privilege escalation risks — and see how blast radius expands as access increases.

Request access below to unlock the interactive simulator and evaluate your Agentic AI Security posture.

In 2026, threat intelligence isn’t just about tracking malware families or IP reputation. It’s about catching the earliest signals of identity abuse: stolen credentials, suspicious logins, token misuse, and privilege escalation attempts that move fast through cloud and SaaS environments.

Credential abuse remains a key initial access vector, accounting for 70% of breaches. In response, modern threat intelligence tools are prioritizing identity signals. 

Yet, there’s another problem. Even when teams do detect an incident, containment is rarely quick. Organizations take an average of 258 days to identify breaches, leaving attackers with months of uninterrupted access. As a result, the core goal is to choose a threat intelligence tool that actually helps cloud-native teams prioritize and detect identity-driven risks. 

What are threat intelligence tools?

Threat intelligence tools analyze and contextualize data about real-world cyber threats so security teams can make faster, better decisions. That data can include indicators of compromise (IOCs), attacker infrastructure, techniques, procedures, credential leaks, suspicious API behavior, and signals of lateral movement across cloud environments.

The key difference between raw threat data and threat intelligence is context. Raw feeds tell you what happened. On the other hand, threat intelligence explains who’s behind it and what to do next. Modern platforms deduplicate and enrich with attribution and risk scoring, then push that context into SIEM/SOAR and detection rules so analysts can prioritize and respond later. 

In cloud-native environments, infrastructure, APIs, and identities change constantly. Attackers increasingly target overprivileged human and non-human identities (NHIs) rather than traditional endpoints. While threat intelligence tools help surface these risks early, they don’t remove access or limit damage. That’s where enforcement layers like automated Just-In-Time (JIT) access become essential to turn intelligence into real risk reduction.

Types of Threat Intelligence Tools

Threat intelligence tools aren’t a single category because no one platform covers everything. Whether you’re looking for external signal collection or identity-focused detection, many organizations combine multiple types to turn raw intelligence into insights. 

Cloud and API Threat Intelligence Tools

These tools focus on threats targeting cloud control planes, SaaS apps, and exposed APIs, where attackers can move from a single compromised credential to high-impact access fast. They enrich cloud telemetry and API gateway logs to flag behaviors like anomalous IAM role assumptions. 

Automation and AI-Driven Threat Intelligence

Modern platforms increasingly use machine learning (ML), AI, and automation to deduplicate feeds and score relevance. The goal is to reduce time spent triaging noisy indicators and instead spotlight threats that match your environment and attack surface. 

Identity-Focused Threat Intelligence Tools

Identity is the new perimeter. Identity-focused tooling tracks compromised credentials and privilege escalation attempts, especially in hybrid cloud and SaaS environments.

Operational Threat Intelligence Tools

Operational threat intel tools integrate intelligence into SIEM, SOAR, and case management so teams can auto-enrich alerts and standardize investigations without manual copying between systems. 

Benefits of Threat Intelligence Tools

• Faster detection of real-world threats: Spot active campaigns and attacker infrastructure faster, so you’re not learning about an incident from an outage report. 
• Reduced alert fatigue and better prioritization: By enriching and scoring indicators, you can separate noise from threats that are hyper-relevant to your security posture. 
• Stronger defense against identity-based attacks: Flag threats like credential theft and privilege escalation patterns, which are critical when a single compromised identity can lead to unauthorized access. 
• Improved incident response and containment: Good intelligence accelerates triage by linking indicators to likely next steps and relevant detection queries. 
• Better security decisions: It helps teams prioritize patching and monitoring by showing which vulnerabilities and attack paths are being exploited, core components of effective exposure management.
  

Key Features to Look For in a Threat Intelligence Tool

• Real-time or near-real-time intelligence: The tool should continuously update detections and context as campaigns evolve, especially for fast-moving threats. 
• Identity-focused visibility: Prioritize platforms that can connect intelligence to who or what is being abused, whether that’s human users or NHIs like API keys and service accounts. 
• Cloud and API context: The best tools understand cloud control planes and SaaS environments, mapping signals to IAM roles and permissions. 
• Automation and actionability: Intelligence should drive outcomes, from workflows to SIEM/SOAR integrations and clear next steps through policy-based response. 
• Integrations with DevOps workflows: If it can’t plug into the tools your teams live in, such as Slack and CI/CD pipelines, it won’t get used when it matters.
• Enrichment and scoring transparency: Confidence scoring and clear explanations of why an indicator is flagged help you avoid chasing low-quality IOCs. 

10 Top Threat Intelligence Tools

1. Apono

While not strictly a threat intelligence platform, Apono is the enforcement layer that turns threat intelligence into action by controlling access across your stack. It is a valuable identity and access management tool built for securing NHIs like CI/CD identities and automation tokens. 

Where threat intelligence can tell you which identities are compromised, Apono ensures these identities don’t have long-lived, overprivileged access sitting around waiting to be abused. Apono achieves this by automating Just-In-Time access and tightening permissions to least privilege.

Main features:

• Automated JIT access flows that grant and revoke permissions dynamically (no standing privileges)
• Auto-expiring access to shrink exposure windows and reduce attacker persistence after credential or token compromise
• Self-serve access requests via Slack, Microsoft Teams, or CLI, plus policy-based approvals and full auditability
• Break-glass and on-call flows to speed incident response without handing out permanent admin access
• Cloud-native integrations and API-driven control that scale across modern environments

Best for: Cloud-native SaaS and regulated enterprises that need to reduce risk from overprivileged human and non-human identities without slowing down engineering teams. 

Pricing: Talk to the Apono team for tailored pricing. 

Review: “Quick and easy config to integrate access control with a myriad of service providers and data stores. For the admin, it’s pretty straightforward to define and implement access flows. For the requester, all they have to do is ask for it via slack and they get what they need within seconds.”

2. Mandiant Threat Intelligence

Mandiant Threat Intelligence (now part of Google Threat Intelligence) is built on frontline incident response research and analysis. It’s designed to help security teams understand who’s targeting them and what attacker behaviors to expect next. 

Main features: 

• Frontline, analyst-curated intelligence based on Mandiant incident response data
• Human- and machine-readable intelligence to support both analysts and automated workflows
• Guided investigation workflows and integrations to streamline detection and response

Best for: Enterprise security teams looking for research-backed intel to enrich investigations. 

Price: By inquiry. 

Review: “[I like the] integration with data platforms like Splunk and Qradar.” 

3. ThreatConnect

ThreatConnect’s “Intel Hub” connects threat intelligence with risk quantification and investigation context to help teams centralize intel and operationalize it across security workflows.

Main features: 

• Multi-source ingestion and normalization of intel
• IOC lifecycle management, including scoring and expiration
• Broad integrations that pull in internal telemetry, such as enrichment

Best for: A hub for centralizing multiple intel sources and a detection tool. 

Pricing: By inquiry. 

Review: “We use the TIP data to compare logs in our SIEM to hunt for threats and enrich other threats that we may come across.”

4. Intel 471 

Intel 471 is a cyber threat intelligence provider known for adversary-focused reporting and visibility into cybercrime ecosystems. It blends research with intelligence across adversary behavior, malware activity, vulnerability insights, and credential signals. 

Main features: 

• Curated intelligence across adversary behaviors and vulnerability insights 
• Coverage of malware families and campaigns with continuous monitoring of infrastructure and tooling
• Monitoring for credential dumps and underground market activity 

Best for: Research-backed intel on advisories and cybercrime activity. 

Pricing: By inquiry. 

Review: “The platform is easy to navigate and find useful information. We contact the Intel471 team to create alerts and email notifications.”

5. Cyware Threat Intelligence Platform 

Built to help CTI and SecOps teams ingest, enrich, score, and share threat data, Cyware focuses on making intel usable across investigations and response. 

Main features: 

• Integrations with SIEM and SOAR for operational use
• Optional “program-in-a-box” approach via Cyware Intelligence Suite 
• Automated ingestion, enrichment, and scoring 

Best for: Centralized threat intelligence to normalize intel from many sources and turn it into repeatable workflows.

Pricing: By inquiry. 

Review: “Cyware TIP is very good [at] ingestion of threat intelligence. [I] especially [like] having the Threat Intel Feed ROI dashboard and visibility.”

6. Anomali ThreatStream

This threat intelligence platform is built to enrich security telemetry and push context into detection and response workflows. Anomali ThreatStream is designed to eliminate the need for analysts to have separate research silos. 

Main features: 

• Threat context that “travels” with alerts and investigations
• Automation-ready intelligence outputs to support operational workflows 
• Curated enrichment designed to improve detection fidelity 

Best for: SOC teams looking to operationalize intelligence at scale through workflow integrations. 

Pricing: By inquiry. 

Review: “It is intuitive, easy to use, and customizable per operational needs.”

7. Recorded Future

Recorded Future is an AI-driven threat intelligence platform designed to deliver real-time, actionable intelligence about supply chain exposure and emerging campaigns. It plugs into existing security operations to prioritize threats that could impact cloud environments and downstream data management systems.

Main features:

• Real-time intelligence summaries and risk scoring
• Alerts on emerging infrastructure and credential exposure signals
• Integrations to push intel into detection and response workflows

Best for: A real-time intelligence layer for larger, complex environments. 

Pricing: By inquiry. 

Review: “I appreciate that Recorded Future offers a comprehensive set of tools for cybersecurity operations teams, including vulnerability and identity intelligence.”

8. EclecticIQ

Next up is an AI-embedded threat intelligence platform designed to reduce analyst overload by centralizing threat data and prioritizing what’s most relevant to your business. EclecticIQ is an analyst-centric platform, generating insights that can be shared across security operations. 

Main features:  

• Centralizes and normalizes threat data to reduce noise and analyst fatigue
• AI-powered prioritization and contextual insights to help teams focus on critical threats
• Analyst workflows for investigation and reporting

Best for: An analyst-centric platform prioritizing intelligence for security workflows. 

Pricing: By inquiry. 

Review: “For organizations having mid to large-scale networks. EIQ is a decent solution to serve the purpose.”

9. CrowdStrike Falcon Intelligence

CrowdStrike Falcon Intelligence delivers adversary-focused intelligence designed to help teams understand who’s targeting them and what techniques they’re using. It integrates well with CrowdStrike’s broader Falcon platform, but can also be used to enrich and accelerate investigations across other tools.

Main features: 

• 265+ adversary profiles, plus context-aware indicators to prioritize threats
• Dark web monitoring and vulnerability intelligence to support proactive defense
• Premium option includes additional capabilities such as fraud monitoring

Best for: Teams already running CrowdStrike Falcon that want to embed intel into SOC workflows. 

Pricing: By inquiry. 

Review: “Falcon Adversary Intelligence delivers timely, relevant insights with clear context around threat actor behavior.”

10. GreyNoise

GreyNoise focuses on internet-wide scanning and exploitation activity. Aka, the ‘noise’ that can flood SOC queues and bury the signals that actually matter. 

Main features:

• Search and enrichment capabilities to investigate IPs at speed and support bulk lookups 
• Products like configurable blocklists for operational use cases
• Intelligence designed to separate benign internet noise from real threats

Best for: Teams wanting to reduce false positives and triage noisy external activity. 

Pricing: By inquiry. 

Review: “Having a strong GreyNoise security team that is directly involved with prioritizing threats is a wonderful addition to this solution.”

Pairing Threat Intelligence with Time-Bound Access

The best threat intelligence tools help you see what’s happening sooner and understand what the risks mean in your environment. However, they can’t automatically stop damage once an identity or credential is compromised. In cloud and SaaS environments, identity and access decisions ultimately determine the blast radius.

Apono is the enforcement layer that operationalizes what threat intel reveals by controlling access for human users and NHIs with automated, Just-In-Time permissions. With auto-expiring access and pre-approved, time-bound break-glass flows, teams can respond fast without leaving standing privileges behind.

If you are ready to turn threat intelligence into real containment, start with Zero Standing Privileges. Download the ZSP Checklist, or book a personalized demo to see Apono in action.

Privileged access solutions are often evaluated on control strength and connectivity. Can they broker access? Can they restrict entry points? Can they capture activity?

Those questions matter. But they overlook something that ultimately determines whether least privilege holds in practice.

Does the system make it easy for engineers to get the right access, at the right time, without friction?

Because if it does not, behavior adapts.

Engineers are problem solvers. When access becomes an obstacle instead of an enabler, they optimize around it. That is where developer experience becomes a security issue.

Where DevX Friction Starts

Engineers usually know what they need to accomplish. The friction begins when translating that goal into the right access request inside a privileged access solution.

In many environments, the privileged access solution becomes another system engineers must navigate rather than an extension of their workflow.

That friction tends to show up in familiar ways:

• Uncertainty about which permission set maps to the task
• Context switching into a separate UI within the privileged access solution
• Delays during debugging or incident response
• Repeating requests because the initial scope was incorrect

Individually, these issues seem minor. Repeated enough times, they shape behavior.

If a privileged access solution makes requesting access uncertain or disruptive, engineers begin asking for broader access up front to avoid getting blocked later. Certainty starts to outweigh precision.

That drift does not begin with weak policy.
It begins with friction inside the privileged access solution itself.

Where StrongDM’s Model Creates Developer Friction

StrongDM is built around proxying and brokering connections to infrastructure. That approach can be effective for controlling entry points into databases, clusters, and servers.

However, its access model relies heavily on predefined access constructs.

Static Definitions in a Dynamic Environment

Permissions and mappings must be created in advance, and engineers must know which access definition applies to their task.

In modern cloud environments, that alignment rarely stays perfect for long.

When definitions are too narrow, engineers get blocked and must submit additional requests. When definitions are broadened to reduce friction, they stop being tightly scoped. The tension between usability and least privilege becomes ongoing rather than occasional.

Over time, predictable patterns emerge:

• Engineers request broader access up front
• Access definitions expand to reduce repeated friction
• Least privilege becomes harder to enforce consistently

Workflow Friction in Privileged Access Solutions

Engineers increasingly expect access to fit into the tools they already use. Slack, CLI, internal developer portals, and AI-driven interfaces are now part of daily engineering life. A modern privileged access solution should integrate directly into those workflows — not sit outside of them.

In many StrongDM deployments, Slack-based workflows are not universally available and are typically tied to enterprise-tier plans. For many teams, the privileged access solution still requires stepping into a separate interface to request access.

During routine work, that extra step may be manageable. During production incidents, it becomes disruptive because it forces context switching at exactly the wrong time.

StrongDM also does not provide MCP integrations or AI-assisted request guidance. Engineers are expected to know exactly what resource and permission level they need. The privileged access solution does not assist in translating intent into the right scope.

When engineers are unsure which resource or permission set maps to their task within the privileged access solution, the process turns into trial and error.

They request access, realize it is insufficient, and resubmit.

To avoid repeating that loop, they begin asking for broader access up front, preferring certainty over precision.

Each extra step pulls them further out of their workflow and makes over-requesting feel like the safer option.

Over time, friction inside the privileged access solution shapes behavior. Engineers optimize for speed and predictability instead of least privilege — not because they disregard security, but because the access experience makes precision harder than it needs to be.

Ready to see how a modern privileged access solution compares? Evaluate Apono vs. StrongDM in our side-by-side comparison.

How Apono Aligns Access With Developer Workflows

Apono keeps the same foundational principle. Access must be requested. Guardrails must exist. Privileges must expire.

The difference is how the experience is delivered.

Engineers can interact with Apono through Slack, Teams, CLI, Backstage, a dedicated portal, and AI-driven interfaces including MCP integrations. The request process happens inside the workflow instead of outside it.

When engineers are unsure what to request, the system provides guidance. Instead of forcing users to translate intent into a precise permission set under pressure, Apono helps map goals to the appropriate scope.

Access is dynamically provisioned at runtime under policy guardrails rather than relying solely on static, pre-created definitions. Privileges align closely with the task at hand and expire automatically when the work is done.

If work runs longer than expected, extensions can occur within policy without forcing engineers to restart the request process. That reduces the incentive to ask for longer or broader access up front.

The result is not a looser system. It is a system engineers are more likely to use correctly.

Make the Secure Path the Fastest Path

At their core, engineers are problem solvers. If getting the right access they need to achieve their goals becomes a problem, then they will find ways around it.

It is up to security teams to put in place the tools and processes that will enable engineers to move faster without compromising on their security priorities. When requests are intuitive, guided, and time-bound by design, least privilege becomes practical instead of aspirational.

Evaluate Your StrongDM Model

If you’re currently using StrongDM, the question isn’t whether it brokers access.

It’s whether it eliminates standing privilege without creating workflow friction.

Book a 30-minute Session to evaluate your privileged access solution:

• Compare static vs. dynamic access models
• Identify areas where standing privilege persists
• Assess whether your current setup supports Zero Standing Privilege
• Explore how modern Cloud PAM integrates directly into engineering workflows

🎁 Qualified StrongDM customers receive a $200 Amazon gift card after completing the session.

Today, we’re excited to announce that Apono Assistant is now available in Slack.

Apono Assistant is Apono’s AI-powered access assistant, built to help engineers request the right Just-in-Time access using natural language — especially in the moments where access forms fall short and users aren’t sure what to request.

Now, that same AI experience is available directly in Slack, so engineers can get the access they need without leaving the tools they already rely on every day.

Access requests break when users don’t know what to ask for

Access forms work great when requests are repeatable and users know exactly what they need. But the most painful access moments are the ones that aren’t repeatable — when a user is blocked by an error and doesn’t know which resource or permission level will fix it.

That’s when users start guessing:

• “Do I need read-only or write?”
• “Which database is this service using?”
• “Is this the right AWS resource?”
• “Should I request admin just to unblock myself?”

And when users guess, the outcomes are rarely ideal. They either:

• ping admins with questions,
• interrupt teammates to troubleshoot permissions,
• submit multiple trial-and-error requests,
• or request overly broad access “just in case.”

The result: more friction for engineers, more overhead for admins, and more risk for security teams.

Why we built Apono Assistant

Apono Assistant was designed to solve one core problem: users often don’t know what to request.

When users can’t translate intent into a precise request, access workflows slow down and organizations end up with more “give me admin” requests driven by frustration rather than actual need.

How it works

Using Apono Assistant in Slack is simple:

1. A user messages the Apono bot in Slack
2. The assistant responds in a thread and guides the user based on their request
3. The assistant suggests relevant resources (especially helpful when users aren’t sure what to search for)
4. The user can preview the request and submit it directly from Slack

Once the request is created, it connects to the same request and notification experience users already get today so they can easily track what they requested and what’s pending.

Why this matters: faster access, lower risk, less admin load

Traditional access request forms are great when requests are repeatable. Which is usually when users know exactly what they need and want the same thing every time.

When forms fail, the access experience becomes slower and noisier and least privilege breaks down.

Apono Assistant closes that gap.

Because it’s built with a “DevOps brain,” the assistant can help users understand:

• which resource is relevant,
• what level of permission is needed,
• and what access scope is appropriate for the task.

This helps organizations reduce risk by making it easier to do the right thing:

• access stays scoped
• access stays time-bound
• and users avoid requesting unnecessary admin privileges

At the same time, it reduces the burden on admins by handling many of the “what should I request?” questions that otherwise become manual overhead.

What’s next: Apono AI in more tools engineers love

Slack is only the beginning.

We’re working on bringing Apono Assistant — our AI layer for access — into more tools engineers love and rely on, including the CLI. The goal is simple: wherever engineers work, Apono should be there to help them request the right Just-in-Time access quickly, safely, and without friction.

Experience Apono Assistant in Slack

Access should be fast when it needs to be — and precise when it matters most.

Apono Assistant in Slack helps engineers request the right Just-in-Time access using natural language, directly where they already work. It reduces guesswork, minimizes overly broad requests, and keeps access scoped, time-bound, and fully auditable.

If you’re looking to reduce admin overhead, eliminate unnecessary standing privileges, and make least privilege easier to follow in practice — not just in policy — we’d love to show you how it works.

Book a demo to see Apono Assistant in Slack in action and explore how AI can streamline access across your organization.

This week, we released our 2026 State of Agentic AI Risk Report, a global survey of 250 senior cybersecurity leaders examining how enterprises are approaching agentic AI as it moves closer to production.

The findings point to a clear reality. While AI agents are advancing quickly, security leaders are deliberately slowing adoption. In fact, 98% of respondents say security and data concerns have already slowed deployments, added scrutiny, or reduced the scope of agentic AI initiatives.

This is not resistance to innovation. It is a response to risk.

A Different View From the Security Seat

To product and engineering teams, agentic AI represents acceleration. Agents can deploy infrastructure, triage incidents, modify configurations, and interact directly with production systems. The productivity upside is undeniable.

From the CISO’s perspective, the question is different: what happens when autonomous systems inherit today’s privilege sprawl?

In our study, 100% of respondents agreed that an attack targeting agentic AI workflows would be more damaging than a traditional cyberattack. That level of alignment reflects a shared understanding that automation multiplies both efficiency and impact.

If a human with excessive privileges can cause damage, an autonomous system with the same privileges can do so faster and at greater scale.

A Real-World Warning of Agentic AI Risk

Recent events have reinforced those concerns.

According to reporting by Engadget, a 13-hour AWS outage was reportedly triggered by Amazon’s own internal AI tools making unintended changes to production systems. Regardless of the technical specifics, the lesson is clear. When AI-driven systems operate directly in live environments without tightly scoped guardrails, the consequences can ripple quickly.

For CISOs, this was not surprising. It validated what many already believed: autonomous systems are not just another application layer. They are actors with privileges. And poorly governed privileges create outsized risk.

The Preparedness Gap

The slowdown in adoption becomes clearer when viewed through a readiness lens.

Only 21% of organizations in our study say they feel prepared to manage attacks involving agentic AI or autonomous workflows. That gap between ambition and operational readiness is fueling concern around agentic AI risk at the executive level.

Security leaders are not evaluating AI in isolation. They are assessing it against environments already strained by accumulated privilege and fragmented visibility. Over time, human and non-human identities have accrued access well beyond actual usage. Cloud policies are scattered across platforms. Standing permissions persist long after their purpose has expired.

Within that reality, agentic AI risk is not hypothetical. It is structural.

Autonomous agents operate at machine speed. When introduced into environments burdened with excessive standing access, they amplify the impact of every overprivileged role and every blind spot in visibility. The blast radius does not grow incrementally. It expands exponentially.

Agentic AI does not create entirely new categories of risk. It accelerates and scales the identity and access weaknesses that already exist.

That is the foundation CISOs are being asked to trust.

What CISOs Are Actually Asking For to Reduce Agentic AI Risk

The hesitation we’re seeing is not about stopping AI adoption. It is about redefining the privilege model that supports it.

Security leaders recognize that agentic AI risk is not just a model accuracy issue. It is an access issue. When autonomous systems operate with standing privileges, the blast radius expands exponentially.

CISOs are looking for a structural shift in how access is granted and governed. The conversation is moving from patching entitlements to redesigning guardrails.

At a strategic level, that shift looks like this:

• From standing privilege to just-in-time, task-bound access
• From static, pre-created roles to dynamic controls that understand intent
• From fragmented IAM systems to unified governance across human and non-human identities
• From broad permissions granted for convenience to scoped access aligned to sensitivity and risk

This is not a tooling adjustment. It is a mindset change.

Agents should not inherit permanent authority. They should operate within clearly defined boundaries, receiving the minimum access necessary to complete a defined objective, with automatic revocation built in.

When those guardrails are in place, autonomy becomes sustainable. Without them, caution is not only rational, it is responsible.

A Pivotal Moment

Agentic AI is forcing organizations to confront identity and privilege gaps that have existed for years. The difference now is speed. What was previously manageable human risk becomes amplified when systems can act independently and continuously.

The friction reported by 98% of respondents between accelerating AI adoption and meeting cybersecurity priorities is not a philosophical disagreement. It is a signal that legacy privilege models were not built for autonomy.

Organizations that invest now in reducing standing privileges and implementing dynamic, intent-aware access controls will be positioned to move forward with confidence. Those that do not will continue to hesitate as AI initiatives approach production.

The agentic future is not on hold. It is waiting for the right guardrails.

Ready to Benchmark Your Agentic AI Risk?

The 2026 State of Agentic AI Risk Report reveals:

• Where security leaders feel most exposed
• The identity gaps slowing AI deployment
• How prepared organizations are to manage agentic AI risk
• The access model shifts CISOs are prioritizing now

If AI agents are entering your environment this year, your privilege model must evolve with them.

A handful of services quietly redeploy. No one directly manages the traditional network perimeter. But somewhere along the way, an API key ends up in the wrong place. The reality of modern cloud security is that new identities are created fast, and permissions are granted broadly to keep things moving. 

Over time, these permissions collect unused rights and drift away from least privilege. APIs get abused through over-privileged identities, long-lived credentials, and permissions nobody remembers approving. 

In fact, the numbers reflect what many teams already feel. 57% of organizations experienced an API-related data breach in the past two years, and 73% of those victims suffered three or more incidents. Modern cloud identity management can’t stop at human users. It must treat non-human identities with the same context and Zero Trust mindset, because nowadays, they’re the ones doing most of the work.

What is cloud identity management?

Cloud identity management is the process of controlling who and what can access cloud resources, under what conditions, and for how long. 

It covers both authentication (proving an identity) and authorization (what that identity can do) across cloud platforms, SaaS apps, APIs, and infrastructure. Unlike on-premises IAM, where servers and users tend to remain stable, cloud environments change constantly. 

Static roles and quarterly reviews don’t cut it. Identity and access management needs to be driven by policy and enforced as things happen. The challenge is that most activity in the cloud comes from non-human identities (NHIs), such as service accounts, workload roles (including Kubernetes service accounts), API tokens, and CI/CD identities. They’re easy to over-provision and often never cleaned up. Unlike humans, NHIs cannot perform Multi-Factor Authentication. If an API key for an NHI is stolen, the attacker has immediate access.

It’s also how permission sprawl sneaks in. A CI/CD job is granted broad permissions to ship something once, and months later, it’s still running with the same access (even though nobody has thought about that project in ages). 

When you manage cloud identity well, access becomes time-bound, contextual, and auditable. The goal is straightforward: enforce least privilege continuously, and make elevated access expire by default.

Why should you prioritise cloud identity management?

As cloud environments sprawl across accounts, services, and pipelines, identity becomes the easiest way in. Once inside, attackers often use over-permissioned identities to move laterally across cloud accounts. Long-lived access keys and static workload roles make this especially dangerous. Compromised credentials may remain valid for months without detection, which underscores the value of protecting CI/CD pipelines.

Getting rid of standing access matters more than betting you’ll never leak a key. The problem is, most teams still manage access through tickets and manual approvals. DevOps becomes the bottleneck, and people start taking shortcuts just to ship.

A big part of the mess comes from NHIs. CI jobs, bots, API keys, and workload roles are often given “temporary” access to unblock work, but the permissions remain because nobody owns the cleanup. If you want identity controls that actually hold up at cloud scale, NHIs need owners, guardrails, and regular review just like human users. In offensive security, non-human identities are especially attractive targets because they often carry broad, unattended access.

The better model is time-bound, contextual access that’s easy to audit. When requests and approvals are automated with clear policies, access becomes consistent and predictable. Engineers get what they need without turning security into a ticket queue.

10 Best Practices for Cloud Identity Management

1. Start With Centralized Visibility Across All Identities (Human + Machine)

There is a saying that goes “you can’t secure what you can’t see.” In most cloud environments, identity data is typically fragmented across cloud providers, SaaS tools, CI/CD systems, and internal platforms. Human users are only a small part of the picture. 

Centralizing identity visibility brings all identities into a single control plane, making it clear who or what has access to which resources, and why. This clarity forms the very foundation of detecting over-privilege, identifying dormant access, and responding quickly when something appears to be wrong. Unlike spreadsheet-based access tracking, centralized visibility gives you real-time answers.

You can implement centralized identity visibility by creating an inventory of all human users and NHIs and assigning ownership to each.  Inventory should pull data from cloud IAM systems, Kubernetes clusters, CI/CD platforms, SaaS tools, and secrets managers. With this data, you’ll be able to flag unused permissions and continuously monitor for newly created identities. 

2. Replace Standing Privileges With Just-In-Time Access

Standing access may be convenient, but it’s also one of the biggest sources of risk in cloud environments. Long-lived permissions dramatically increase blast radius when credentials are compromised. 

Just-in-time (JIT) access flips the model on its head. Permissions are granted only when needed and expire automatically. JIT relies on short-lived credentials rather than static keys, reducing the window of exposure if access is misused.

You can reduce the attack surface by default while also aligning with zero-trust principles. Cloud-native access management platforms make time-bound access the norm rather than an exception.

The same model applies to non-human identities, such as CI jobs and workload roles. Rather than “permanent admin just in case,” JIT makes elevated access an exception with an expiration time. 

3. Implement Least Privilege as a Continuous Process 

You do not “finish” least privilege. Cloud environments are constantly evolving, and their access requirements change accordingly. Hence, high-performing teams treat least privilege as an ongoing process as part of a Zero Trust operating model.

Cloud native access management platforms make refining policies and adjusting permissions manageable. Time-bound and context-aware controls keep least privilege current as infrastructure changes.

4. Automate Access Workflows to Remove Manual Bottlenecks 

Manual access approvals slow everyone down. There are tickets piling up, engineers waiting in line, and security teams gatekeeping rather than partnering. 

Automated access workflows remove this friction. All it takes is policy-driven approvals and self-service requests to get engineers the access they need without bypassing controls.

As a solution, Apono enables this through native integrations with tools like Slack, Microsoft Teams, and CLI workflows, so access requests fit naturally into how teams already work. Compared to ticket queues, self-serve workflows are faster and easier to govern.

5. Enforce Contextual Access Policies Based on Role, Environment, and Risk 

Not all access requests carry the same risk. Risk signals may include unusual access times, unfamiliar locations, untrusted devices, or requests targeting production resources. 

For example, production access should be stricter than staging. On-call engineers shouldn’t be evaluated the same way as interns.

Context-aware policies use role, environment, time, and risk signals to decide what’s allowed. That keeps controls strong where they matter, without slowing work everywhere else.

6. Secure and Right-Size Access for Non-Human Identities

Non-human identities now dominate cloud environments, and they’re often the least controlled. Static permissions assigned to workload identities become dangerous as systems evolve. 

It’s vital to onboard non-human identities with fine-grained, time-bound entitlements. Workload identities receive only the permissions they need, only when they need them. This best practice significantly reduces long-term exposure while preserving automation. 

Every non-human identity should have a clear owner, defined purpose, and expiration policy. Permissions should be reviewed whenever pipelines change or services are decommissioned to prevent orphaned identities from persisting indefinitely. Treat NHIs like production infrastructure: provision, monitor, rotate, and retire on purpose; NHI management makes the biggest difference here.

7. Integrate Access Management Directly Into Developer Workflows

Security controls that live outside developer workflows are often ignored or bypassed. Effective cloud identity management meets developers where they are. 

Cloud-native integrations and API-driven access controls allow identity governance to scale alongside infrastructure automation. When access requests, approvals, and audits are integrated into CI/CD pipelines and chat tools, compliance becomes a natural byproduct of normal work, rather than an additional task. 

The most effective implementation best practice is to enforce identity policies directly within CI/CD pipelines, ensuring that access decisions align with deployment automation.

8. Use Break-Glass and On-Call Flows for Controlled Emergency Access

Emergencies happen. The goal isn’t to block access, but to control it. Break-glass access allows engineers to respond quickly during incidents while maintaining accountability. Well-designed on-call and emergency flows ensure access is temporary, logged, and reviewed after the fact. This best practice strikes a balance between speed and governance. Break-glass keeps emergency access fast, scoped, and auditable.

9. Continuously Audit Permissions and Monitor for Drift 

Without automation, permissions drift is inevitable. Continuous auditing keeps access aligned with policy over time. Audit logs should capture who requested access, why it was approved, what changed, and when access expired or was revoked. Taking these steps simplifies the collection process for audits such as SOC 2, GDPR, HIPAA, and CCPA.

10. Apply Zero-Trust Principles Across All Cloud Environments 

Zero Trust should be viewed as an operating model, rather than a product, where every request is treated as untrusted until proven otherwise. Zero-trust verification should be continuous, incorporating identity, device, and workload trust, as well as real-time risk signals.

Every access request must be verified, scoped, and time-bound, regardless of its origin. Applying zero-trust principles across multi-cloud and SaaS environments ensures identity remains the security perimeter everywhere, not just on paper. This approach reflects the shift beyond the traditional perimeter, where every request is evaluated on context rather than network location. When combined with automation and visibility, Zero Trust strengthens security without slowing teams down.

Where Identity-First Security Becomes Operational

Cloud security doesn’t fail because teams don’t care. It fails because identity has outgrown the tools and processes built to manage it. 

Human and non-human identities now drive every deployment, every API call, and every production change. Treating access as static infrastructure in a dynamic cloud is no longer defensible.

Apono was designed to solve this gap between cloud scale and access control. By automating just-in-time access, eliminating standing permissions, and applying zero-trust principles across human and non-human identities, Apono helps teams regain control of cloud access without slowing engineers down. That means fewer tickets, fewer permanent privileges, and cleaner audit trails by default.

Ready to eliminate standing access, reduce identity risk, and keep developers moving fast? Book a demo with Apono and see how cloud-native, just-in-time access works in under 15 minutes.

Report finds growing tension between AI acceleration goals and security readiness as autonomous systems move toward production

NEW YORK — February 2026 — Apono, the cloud-native Privilege Access Management platform securing human and agent identities, today released The 2026 State of Agentic AI Cyber Risk Report, a global study examining how enterprises are approaching agentic AI adoption amid rising security concerns. The report finds that while organizations broadly believe in the potential of AI agents and autonomous systems, security readiness is emerging as a primary constraint on scale.

The findings are based on a global survey of 250 senior cybersecurity professionals from organizations with 250 or more employees across North America, Europe, and the Middle East and Africa. The research was conducted in December 2025 by an independent market research firm and focused on how security and technical leaders assess risk, readiness, and accountability as agentic AI moves closer to production environments.

The report highlights a growing tension inside organizations as agentic AI moves closer to production. While executive and technical leadership often champion AI agents as a driver of efficiency and competitive advantage, accountability for AI-related cyber risk remains concentrated with CISOs and security teams — placing security leaders in the role of gatekeepers.

“Cybersecurity leaders are actively slowing agentic AI adoption,” said Rom Carmel, CEO and co-founder of Apono. “There’s a lot of talk about AI agents rapidly taking over enterprise workflows, but the data in our report shows that this simply isn’t the case. On the ground, CISOs are pressing the brakes.”

Ofir Stein, CTO and co-founder of Apono, added: “Organizations are still struggling to secure human access at scale. Expecting CISOs to greenlight broad autonomy to agents without mature identity and access controls in place isn’t realistic. Until those foundations are in place — and our data shows they largely aren’t — agentic AI deployment will continue to be deliberately constrained, regardless of current industry sentiment.”

Key findings from the report include:

• 98% of respondents say security and data concerns have already slowed deployments, added review steps, or reduced the scope of agentic AI and autonomous system projects.
  
• 77% report moderate slowdowns or added scrutiny, while 21% cite significant delays or reduced project scope.
  
• 100% agree that attacks targeting agentic AI workflows would be more damaging than traditional cyberattacks.
  
• Only 21% say their organization feels prepared to manage attacks involving agentic AI or autonomous workflows.
  
• 98% report friction between accelerating AI adoption and meeting cybersecurity priorities.
  

These findings stand in contrast to broader market narratives suggesting rapid, near-term replacement of traditional software by AI agents. While experimentation with agentic AI is underway across many organizations, the report shows that CISOs are pressing the brakes as systems approach production, citing the need for stronger controls around identity, access, and permissions before autonomy can safely scale.

Going Forward

While agentic AI capabilities continue to advance with the adoption of LLMs acting on behalf of engineers, and enterprise adoption patterns evolve, the report underscores a clear takeaway: organizations are not rejecting AI agents, but they are demanding stronger security foundations. Addressing long-standing gaps in identity governance, privileged access, and visibility is increasingly viewed as a prerequisite for confident, sustained adoption.

The 2026 State of Agentic AI Cyber Risk Report is available on Apono’s website. Download your copy here to explore the full findings.

About Apono

Apono provides Zero Standing Privilege access for cloud infrastructure, databases, Kubernetes, SaaS, and operational resources. By automating access privilege provisioning based on intent, risk, and operational context, Apono helps organizations such as Intel, HPE, and Workday enforce Zero Trust without slowing down engineering, operations, or incident response.