They complicate them because IAM and PAM were built around stable identities, human-paced approvals, and entitlements that are reviewed after use. Agentic systems compress decision-making into runtime, which means access can be consumed, combined, and discarded before a review cycle ever sees it. That makes static privilege models incomplete for autonomous execution.
Why Traditional IAM and PAM Struggle with Autonomous Agents
agentic ai changes the control problem from “who can log in?” to “what can this software entity do, right now, while pursuing a goal?” Traditional IAM and PAM assume stable human roles, scheduled approvals, and access that can be reviewed after the fact. That model breaks when an agent can chain tools, call APIs, invoke MCP-connected services, and consume privileges in seconds. Current guidance suggests treating agents as a distinct workload class, not as enhanced users, which is why OWASP Agentic AI Top 10 and NIST AI Risk Management Framework both push teams toward runtime controls and accountability.
NHIMG research shows the problem is not theoretical: in SailPoint’s OWASP NHI Top 10 research, 80% of organisations said their AI agents had already acted beyond intended scope. That matters because the failure mode is often silent overreach, not obvious compromise. In practice, many security teams encounter this only after an agent has already accessed data or executed actions that no human reviewer explicitly approved.
How Runtime Authorisation, JIT Credentials, and Workload Identity Change the Model
The practical answer is to move from static entitlement assignment to intent-based authorisation. Instead of granting an agent broad standing access, the policy engine evaluates the request at execution time, using task context, data sensitivity, destination system, and risk. That is where CSA MAESTRO agentic AI threat modeling framework and OWASP Top 10 for Agentic Applications 2026 are directionally aligned: evaluate the action, not just the identity label.
That runtime model usually includes four controls:
- Just-in-time credential provisioning for a single task or session.
- Ephemeral secrets with short TTLs and automatic revocation on completion.
- Workload identity for cryptographic proof of what the agent is, using mechanisms such as SPIFFE or OIDC rather than long-lived shared keys.
- Policy-as-code so approvals are evaluated in real time, not deferred to a quarterly access review.
This is also where IAM and PAM need to meet Zero Trust Architecture, because an agent may be trusted for one operation and denied for the next. NHIMG’s AI LLM hijack breach coverage reinforces why static secrets are dangerous: attackers do not need much time once they find them. These controls tend to break down in multi-agent pipelines with shared memory and delegated tool use because the authorisation boundary moves faster than the review boundary.
Where the Edges Break: Shared Secrets, Long-Lived Access, and Multi-Agent Chains
Tighter control often increases operational overhead, requiring organisations to balance security against latency, developer friction, and automation reliability. There is no universal standard for this yet, but best practice is evolving toward short-lived, context-scoped access for agents that act autonomously. That tradeoff becomes hardest in environments that still depend on long-lived API keys, service accounts reused across workloads, or broad PAM vaulting for non-interactive systems.
Edge cases also appear when an agent operates across several tools with different owners. A single task may require access to code, ticketing, cloud infrastructure, and customer data, and each hop can expand the blast radius if entitlement checks are only done at the perimeter. NHIMG’s Moltbook AI agent keys breach and Top 10 NHI Issues both point to the same lesson: secrets sprawl and over-privileged non-human accounts are the conditions most likely to defeat legacy controls.
For practitioners, the next step is not to “make PAM smarter” in isolation, but to align agent governance with NIST AI Risk Management Framework, NIST Cybersecurity Framework 2.0, and NHI-specific controls. That is the only sustainable path when the identity is autonomous, the access is ephemeral, and the decision to act happens at runtime, not in a queue.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Addresses runtime risks from autonomous agent tool use and overreach. | |
| CSA MAESTRO | Focuses on threat modeling for autonomous agent behavior and tool chaining. | |
| NIST AI RMF | Governs accountability, mapping, and risk treatment for AI systems. |
Assign ownership for agent decisions and document risks, controls, and review triggers in the AI RMF.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 6, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
