TL;DR: P0 Security finds that agentic AI applications are managing cloud resources, secrets, and builds through shared tokens, long-lived credentials, and hardcoded service accounts that remain outside standard IAM reviews. Governance must shift from reviewing stable access to governing dynamic machine actors whose effective privilege changes across runtime context.
At a glance
What this is: P0 Security argues that agentic AI systems are behaving like privileged non-human identities, but most remain governed as if they were static automation.
Why it matters: IAM, IGA, and PAM teams need to treat agentic software as an identity class with lifecycle, scope, and audit requirements, or risk blind spots that access reviews and static policies will not catch.
👉 Read P0 Security's analysis of agentic AI identity governance and NHI blind spots
Context
Agentic AI applications are software actors that can plan and execute actions at runtime, rather than following a fixed script. In identity terms, that means they behave like non-human identities that may hold tokens, service accounts, or API credentials while making decisions that change their effective access.
The governance problem is not simply that these systems are automated. It is that their access patterns are dynamic while many identity programmes still assume stable, reviewable privilege. That creates a mismatch between how access is granted, how it is actually used, and how control owners think it is being managed.
P0 Security's article frames this as an NHI governance blind spot: the actor is non-human, the authority is delegated, and the lifecycle is often unmanaged. That is atypical only in speed and scale, not in kind; the same identity failures seen with service accounts now appear in more adaptive software.
Key questions
Q: What breaks when AI systems are governed like static applications?
A: Lifecycle drift breaks the model. AI systems change through training, fine-tuning, updates, and retirement, so static controls miss where risk enters and where access should end. That creates blind spots for data exposure, connector reuse, and post-deployment misuse.
Q: Why do shared service accounts create risk for AI agents?
A: Shared service accounts hide which agent made a request, so they collapse accountability and make incident response slower. They also turn credential exposure into a single compromise path for multiple workloads. For agentic systems, every identity should be attributable to a specific workload or delegated task.
Q: How do IAM teams measure whether AI agent access is under control?
A: IAM teams should measure whether they can describe every agent action path, every context source, and every parallel execution pattern in audit terms. If reviewers cannot reconstruct what the agent could do and how much it could do at once, the programme is not yet governing the agent, only observing it. Auditability should include tool calls, retrievals, and downstream writes.
Q: When should organisations treat an AI system as a non-human identity?
A: Treat an AI system as an NHI when it can authenticate, request tools, or perform actions without direct human supervision. At that point it needs inventory, lifecycle, least privilege, monitoring, and revocation controls just like other machine identities.
Technical breakdown
Why agentic AI behaves differently from traditional automation
Traditional automation executes predefined steps when a trigger fires. Agentic AI systems can choose actions at runtime, combine tools, and change execution paths based on context, which means the access problem is no longer just 'what task does the job run' but 'what can the actor decide to do next'. That runtime variability expands the blast radius of any credential or permission mistake because privilege is exercised dynamically, not only at provisioning time. The important distinction is not sophistication, but decision latitude: once the software can adapt its own sequence, static role assumptions become less reliable as a control boundary.
Practical implication: move identity review from script-level permissions to runtime authority and tool-use scope.
Shared tokens and hardcoded service accounts create NHI governance debt
Agentic systems commonly run on shared tokens, long-lived credentials, or hardcoded service accounts. Those mechanisms are familiar in NHI estates, but they become more dangerous when a system can invoke cloud APIs, trigger builds, or modify secrets without a human deciding each step. The core issue is not just possession of credentials. It is that one set of credentials can represent many machine actions across multiple contexts, making ownership, audit, and offboarding harder. When identity is shared, accountability becomes diluted and lifecycle controls lose precision.
Practical implication: inventory every agent credential as an NHI and tie it to a named owner, expiry, and revocation path.
Why access review logic breaks when machine actions are dynamic
Access review assumes privilege is stable long enough to be observed, understood, and certified. Agentic behaviour breaks that assumption because the actor can acquire, combine, and exercise access in ways that vary by task, context, and runtime decision. A review cycle may see the entitlement, but not the actual sequence of actions the actor can produce with it. That is why static least-privilege policy is necessary but insufficient: effective access in an agentic workflow depends on paths, not just roles. Governance must therefore account for transitive action chains, not only assigned permissions.
Practical implication: model effective access paths and not just assigned roles when validating agent permissions.
Threat narrative
Attacker objective: The objective is to use delegated machine authority to alter infrastructure, expose secrets, or expand access faster than governance processes can contain it.
- Entry occurs when an agent is granted a shared token, long-lived credential, or hardcoded service account that can reach infrastructure, build, or secret management APIs.
- Escalation follows when the agent combines those entitlements with runtime decisions to trigger workflows, modify configurations, or interact with downstream services beyond the original human intent.
- Impact lands when the resulting chain of actions changes infrastructure state, exposes secrets, or creates overprivileged machine access that is difficult to attribute and remove.
Breaches seen in the wild
- Hugging Face Spaces breach 2024: Unauthorised access to Hugging Face Spaces may have exposed secrets users stored for AI apps; tokens were revoked and org tokens removed.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Agentic AI identity governance exposes an NHI control gap, not a new category of user. These systems are still non-human identities in operational terms, but they act with enough runtime variability that static IAM assumptions no longer hold cleanly. The practical lesson is that the governance model must follow the actor's behaviour, not the interface label.
Shared credentials are the central governance debt in agentic deployments. When several machine processes or agents rely on the same token or service account, ownership, auditability, and offboarding all weaken at once. That is why unmanaged NHI sprawl becomes a policy failure, not just an inventory problem.
Access review is the wrong primary control when the actor can recompose privilege on demand. Reviews work best when entitlements are stable, attributable, and human-paced. Once an agent can chain tools and actions dynamically, the more relevant question is whether the runtime path itself is constrained and observable.
Runtime authority is now the real identity boundary for agentic systems. The article points to a shift from provisioning-centric governance to decision-centric governance, where the key question is not only who has access but what that actor can do once execution starts. Practitioners should treat tool use, credential scope, and context as a single control plane.
Agentic AI governance will converge with NHI governance before it converges with traditional application security. The strongest similarities are not in code execution, but in lifecycle, credential management, and blast-radius control. Teams that already govern service accounts well will adapt faster than those still depending on periodic access certification alone.
From our research library:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
- Read next: Agentic AI Identity Guide
What this signals
Agentic access is becoming an identity problem before it becomes a security architecture problem. The fastest way for teams to lose control is to let AI systems inherit human-era assumptions about static privilege, then discover too late that those assumptions collapse at runtime. That is why agentic AI identity governance has to be designed into the programme, not layered on afterward.
Runtime authority is the control point that matters most for autonomous software actors. Once an agent can chain tools and actions dynamically, periodic review becomes a lagging indicator. Teams need to govern credential issuance, scope, and context together, because that is where actual blast radius is created.
Agentic credential sprawl is already at scale. 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey. The gap suggests many programmes are entering adoption faster than they are building lifecycle controls.
For practitioners
- Audit agent credentials as first-class NHIs Create a complete inventory of shared tokens, hardcoded credentials, service accounts, and API keys used by agentic systems. Assign ownership, expiry, and revocation responsibility for each one.
- Separate agent authority by task and environment Prevent a single agent identity from carrying the same access across development, test, and production. Scope credentials to the narrowest environment and workflow the agent genuinely needs.
- Move reviews to effective access paths Review the actions an agent can actually reach through chained APIs, build systems, and secret stores, not just the role name attached to the credential.
- Bind every agent to lifecycle controls Require issuance, rotation, expiration, and offboarding for agent credentials on the same schedule discipline used for other privileged NHIs, with clear service ownership.
Key takeaways
- Agentic AI systems create an NHI governance problem because they act with delegated authority while still relying on shared tokens, long-lived credentials, and hardcoded service accounts.
- The main failure is not visibility alone but the mismatch between dynamic machine behaviour and identity controls designed for stable, reviewable privilege.
- Teams need lifecycle ownership, scoped runtime authority, and effective access-path review if they want to govern agentic systems without losing control.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | Agentic systems rely on shared tokens and hardcoded credentials for machine authentication. |
| NHI-05 — Overprivileged NHI | The article highlights overprivileged agent access across cloud, build, and secret systems. | |
| NHI-07 — Long-Lived Secrets | Long-lived credentials are a central risk in the agentic access model described here. | |
| Recommendation — Review machine authentication paths and replace brittle shared credentials with governed identity issuance. Constrain agent entitlements to the minimum access needed for each runtime task. Shorten credential lifetime and rotate agent secrets on a governed schedule. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | The article describes how exposed machine credentials can be reused across systems and tasks. |
| Recommendation — Map agent credential abuse to credential access and lateral movement detection coverage. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The core issue is how permissions are granted and constrained for agentic actors. |
| Recommendation — Apply entitlement governance to agent identities and verify effective access against intended scope. | ||
Key terms
- Agentic AI Identity: The complete set of credentials, permissions, and governance controls applied to an autonomous AI agent, covering authentication, authorisation, action logging, and access revocation. Distinct from traditional NHI because agent identities are often ephemeral, delegated, and multi-hop.
- Legacy Access Path: A legacy access path is an older permission route that remains active after newer, tighter controls have been added. These paths are dangerous because they are easy to forget, often poorly monitored, and frequently become the easiest route for attackers once modern workflows are hardened.
- Runtime authority: Runtime authority is the permission an AI system has while it is actively deciding and acting, not just when it is approved. In governance terms, it is the point where access, tool use, and action scope become operational, which is why build-time review alone cannot prove safety.
- Delegated Machine Identity: A non-human identity that acts on behalf of an organisation or another system with defined authority. In agentic environments, this identity may chain actions across tools and services, so lifecycle, approval, and revocation need to reflect the delegation path as well as the technical account itself.
What's in the full article
P0 Security's full analysis covers the operational detail this post intentionally leaves for the source:
- Examples of how agentic software uses cloud, build, and secret-management access in practice
- The article's discussion of runtime autonomy versus traditional automation in identity terms
- Guidance on why access review, remediation, and lifecycle controls must change for machine actors
- The source's treatment of context-aware enforcement, just-in-time access, and policy-based governance
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on June 24, 2026.
Updated on October 7, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org