Why do AI agents create new IAM and PAM challenges?

Because the model can trigger actions at runtime, privilege is no longer just a provisioning issue. Teams must control what tools the agent can reach, what data it can see, and which actions require gating or audit approval. That makes runtime access control central to agent governance.

Why This Matters for Security Teams

AI agents do not behave like conventional users or service accounts. They can decide at runtime which tools to call, which data to inspect, and which workflows to chain together, which means IAM and PAM controls must move from static entitlements to continuous decision-making. That shift is why agent risk shows up first in access paths, not just in model output.

Current guidance from OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework points to runtime governance, context-aware authorization, and auditable action control as baseline expectations for autonomous systems. NHIMG research shows the practical gap clearly: in the AI Agents: The New Attack Surface report, only 52% of companies can track and audit the data their agents access, leaving the rest with limited visibility during incident response and compliance review.

This matters because PAM was built for human sessions with predictable intent, while agents can escalate through tool chains, prompt injection, or indirect data exposure without any human-like pattern. In practice, many security teams encounter agent overreach only after sensitive data has already been queried, copied, or acted on, rather than through intentional privilege design.

How It Works in Practice

Agent governance works best when identity, authorization, and execution are split into separate control layers. The agent should have a workload identity that proves what it is, then receive short-lived credentials only for a specific task, and finally be checked again at the moment an action is requested. That is very different from assigning a broad role and assuming the agent will stay inside it.

Practitioners are increasingly combining workload identity standards such as SPIFFE with runtime policy engines and ephemeral secrets. In this model, the agent authenticates as a workload, not as a person, and policy is evaluated at request time using context such as the target system, sensitivity of the data, execution history, and whether a human approval is required. That approach aligns with the direction of CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix, both of which treat dynamic behavior and chained actions as central risk drivers.

• Use just-in-time credentials with strict TTLs so access expires when the task ends.
• Gate high-risk actions, such as deletion, payment, privilege grant, or data export, behind step-up approval.
• Bind tool access to the minimum action set needed for the current task, not the agent’s general purpose.
• Log every tool call, policy decision, and credential issuance event for later audit.

NHIMG’s The 2024 Non-Human Identity Security Report found that only 19.6% of security professionals feel strongly confident in managing non-human workload identities, which explains why many environments still rely on long-lived secrets and manual exceptions. These controls tend to break down when agents operate across hybrid, multi-cloud, or SaaS-heavy environments because the authorization boundary is fragmented and policy decisions cannot keep pace with tool chaining.

Common Variations and Edge Cases

Tighter agent access control often increases operational overhead, so organisations must balance security gains against latency, approval burden, and integration complexity. That tradeoff is most visible in environments where agents are embedded in business workflows and cannot wait minutes for a human to approve every action.

Best practice is evolving, but there is no universal standard for how much autonomy should be permitted before a human review is mandatory. Some teams allow read-only discovery without approval, then require JIT elevation for write actions, while others segment agents by risk tier and data classification. The right model usually depends on whether the agent can reach production systems, financial records, or identity infrastructure.

Agentic attack paths also differ by use case. A coding agent, a support chatbot, and a workflow orchestrator may all need different tool permissions, different audit depth, and different revocation triggers. NHIMG case studies such as CoPhish OAuth Token Theft via Copilot Studio and Replit AI Tool Database Deletion show how quickly an overly broad tool grant can become an incident.

These controls become hardest to sustain when secrets are reused across agents, approval logic lives outside the policy engine, or runtime telemetry is too weak to reconstruct who approved what and why.

Related resources from NHI Mgmt Group

• Why do AI agents create new risk in non-human identity management?
• Why do AI agents create more IAM risk than ordinary developer tools?
• Why do AI agents increase non-human identity risk in existing IAM programmes?
• Why do AI agents create more risk when they reuse existing credentials?