Why do autonomous agents create more risk than traditional application accounts?

Why This Matters for Security Teams

Autonomous agents are not just another service account with a new label. They can decide what to do next, invoke tools in sequence, and expand their own operational reach while a task is still in progress. That changes the security problem from provisioning an identity once to governing behaviour continuously. This is why current guidance increasingly points to runtime controls, as reflected in the OWASP Agentic AI Top 10 and NHIMG’s analysis in OWASP NHI Top 10.

The practical risk is not only compromise, but scope drift. A traditional application account usually has a predictable call pattern, while an agent may touch systems that were never part of the original workflow, especially when prompts, tool outputs, or external data influence its next action. The difference matters because access reviews are built for stable entitlements, not dynamic execution. In practice, many security teams encounter agent overreach only after the agent has already acted outside its intended scope, rather than through intentional design.

How It Works in Practice

Security teams should treat autonomous agents as workload identities with constrained execution authority, not as people or long-lived apps. The key is to make authorization runtime-aware, with decisions based on the task, context, target system, and policy state at the moment of access. That is a better fit than static RBAC alone, because role assignments do not capture what an agent is trying to do at a specific step in a workflow.

Best practice is evolving toward a combination of workload identity, JIT credentials, and policy-as-code. Workload identity frameworks such as SPIFFE-style attestation or short-lived OIDC tokens help prove what the agent is at runtime. JIT provisioning narrows the blast radius by issuing ephemeral secrets only for a specific task, then revoking them when the task ends. Real-time policy engines, including approaches discussed in the NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework, can evaluate whether a tool call is allowed in the current context.

• Issue credentials per task, not per deployment.
• Bind each agent to a distinct workload identity.
• Log tool use, prompts, and downstream actions together for auditability.
• Revoke access automatically when the task, session, or policy condition ends.

NHIMG research on agentic applications shows why this matters: rogue agent behaviour is already common enough that it cannot be treated as an edge case, and the attack surface expands as organisations add more autonomous workflows. Related findings are detailed in AI Agents: The New Attack Surface report and OWASP Agentic Applications Top 10.

These controls tend to break down when agents are allowed to call legacy systems that only support coarse, persistent credentials because the system cannot evaluate intent at request time.

Common Variations and Edge Cases

Tighter runtime control often increases operational overhead, requiring organisations to balance safety against latency, integration complexity, and developer friction. That tradeoff is especially visible in multi-agent environments, where one agent may delegate to another, or where tool chains cross teams and clouds.

There is no universal standard for this yet. Current guidance suggests using stricter policy gates for agents that can write, move money, change infrastructure, or access sensitive data, while allowing less intrusive controls for read-only or bounded retrieval tasks. In some environments, short-lived secrets and intent-based authorisation are enough. In others, especially where agents can chain tools or escalate through indirect dependencies, stronger isolation and approval checkpoints are needed.

Edge cases also matter. An agent embedded in a human-in-the-loop workflow may look safer, but once it can pre-stage actions, summarise results, or request additional permissions, the risk profile changes. The same is true for agents operating across SaaS platforms, because session tokens, delegated scopes, and cached secrets can outlive the original decision. NHIMG’s broader guidance on non-human identity risk and incident patterns is a useful reference point in Top 10 NHI Issues and Ultimate Guide to NHIs. The practical rule is simple: the more autonomous the agent, the less acceptable static entitlements become.

Related resources from NHI Mgmt Group

• Why do autonomous agents create more lateral movement risk?
• Why do AI agents create a different access-risk profile than traditional applications?
• Why do AI agents create new risk in non-human identity management?
• Why do AI agents create more risk when they reuse existing credentials?