AI agents complicate IAM and PAM because they can make decisions, chain tools, and act faster than human review cycles can respond. They also blur the line between authentication and authorization, since the same identity may trigger multiple actions after a single approval. That means organizations need policy, telemetry, and revocation designed for autonomous behavior, not just human login events.
Why Traditional IAM and PAM Struggle with AI Agents
AI agents are not just another workload with a login. They are autonomous software entities with execution authority, tool access, and the ability to chain actions after a single approval. That breaks the assumptions behind static IAM roles and many PAM designs, which were built around predictable human sessions, not goal-driven behavior that can branch at runtime. The practical issue is not only access, but intent: an agent may start with one authorised task and then use the same privileges in ways no reviewer expected.
That is why current guidance increasingly points to agent-specific governance models such as the OWASP NHI Top 10 and the OWASP Agentic AI Top 10, both of which treat agentic misuse as a primary risk rather than an edge case. In the SailPoint report AI Agents: The New Attack Surface, 80% of organisations said their agents had already acted beyond intended scope. In practice, many security teams encounter this only after an agent has already taken an unexpected path, rather than through intentional design.
How JIT Credentials, Workload Identity, and Runtime Policy Change the Model
For AI agents, the safer pattern is to issue the minimum identity and authority needed for the specific task, then revoke it as soon as the task ends. That means just-in-time credential provisioning, short-lived secrets, and workload identity as the primitive, not a broad user-style account. A static RBAC role cannot express whether an agent should retrieve a file, call an API, or write to a ticketing system at 2:13 a.m. because those decisions depend on runtime context, tool chain, and intent.
Best practice is evolving toward intent-based authorisation, where policy evaluates what the agent is trying to do at the moment it tries to do it. That may include policy-as-code, step-up approval for high-risk actions, and tight telemetry around every tool invocation. NIST’s NIST AI Risk Management Framework and NIST Cybersecurity Framework 2.0 both support the idea that governance must be continuous, measurable, and auditable. On the implementation side, workload identity patterns such as SPIFFE or OIDC tokens are useful because they prove what the agent is, not just what secret it happened to hold. That matters when secrets are copied into tool chains or exposed in logs, a risk reinforced by NHIMG research such as the Moltbook AI agent keys breach and the DeepSeek breach. These controls tend to break down in flat environments where agents inherit legacy admin rights and every tool is reachable from the same standing credential.
Where the Real Governance Gaps Show Up
Tighter control often increases operational overhead, so organisations must balance faster automation against higher policy and review cost. The hardest cases are multi-agent workflows, long-running jobs, and environments that still rely on shared service accounts. There is no universal standard for this yet, but current guidance suggests separating agent identity from human identity, setting very short TTLs on secrets, and revoking access when a task completes rather than when a session times out.
Another common gap is assuming PAM alone can solve the problem. PAM is still useful for high-risk elevation, but it does not by itself explain why an agent should have access in the first place, or what exact intent is allowed. The relevant question is whether the action fits the current objective, risk level, and trust context. That is why the CSA MAESTRO agentic AI threat modeling framework and the OWASP Top 10 for Agentic Applications 2026 both emphasise tool abuse, excessive authority, and poor containment. NHIMG’s AI LLM hijack breach analysis also shows how quickly exposed credentials can be weaponised once an agent or its related secrets are reachable. In practice, the weak point is usually not authentication itself, but the first downstream action that no one expected the agent to take.
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 | A01 | Agentic apps need controls for tool misuse and excessive authority. |
| CSA MAESTRO | MAESTRO models agent threat paths, privilege spread, and control gaps. | |
| NIST AI RMF | GOVERN | AI RMF govern function fits accountability for autonomous agent behavior. |
Constrain agent tool access at request time and log every high-risk action.
