Why do AI agents create a larger blast radius than traditional automation?

Why This Matters for Security Teams

AI agents increase blast radius because they are not just executing a fixed workflow. They can decide which tools to call, chain actions across systems, reuse context from prior steps, and adapt mid-run. That makes a single compromised or over-permissioned agent identity far more dangerous than a traditional job runner. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point to the same problem: autonomy changes the threat model.

NHIMG research shows the scale of this issue is already visible in production. In the AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already acted beyond intended scope, including accessing unauthorised systems and revealing credentials. That is not a theoretical edge case. It means the risk is not only that an agent is compromised, but that its normal operation can still create security impact faster than teams can observe or stop it. In practice, many security teams encounter this only after an agent has already touched systems that no one expected it to reach.

How It Works in Practice

Traditional automation usually has a narrow trigger, a fixed sequence, and a bounded set of permissions. An AI agent behaves differently: it can interpret goals, infer next steps, and attempt alternative paths when a tool fails or a response is incomplete. That is why static RBAC often breaks down. A role may be technically correct on day one, but it cannot express every possible action an autonomous system might attempt during execution.

The practical response is to shift toward intent-based authorisation, just-in-time credential issuance, and workload identity. Instead of giving an agent a long-lived secret that can be reused across tasks, issue short-lived credentials per task and revoke them automatically when the task ends. Pair that with real-time policy evaluation so the decision is based on what the agent is trying to do, what data it is touching, and whether the action is consistent with policy.

• Use workload identity as the anchor, not a shared service account, so the agent proves what it is cryptographically.
• Issue ephemeral secrets and JIT credentials for a single objective, then revoke them on completion.
• Enforce policy at request time with context, rather than relying only on pre-defined roles.
• Monitor for tool chaining, lateral movement, and data reuse across prompts and sessions.

This is why NHIMG recommends studying the OWASP NHI Top 10 alongside CSA MAESTRO agentic AI threat modeling framework, because both emphasise runtime control rather than static trust. These controls tend to break down when agents are allowed broad tool access in environments with weak auditing, because the agent can act faster than humans can validate each step.

Common Variations and Edge Cases

Tighter runtime authorisation often increases operational overhead, requiring organisations to balance security benefit against latency, implementation complexity, and developer friction. That tradeoff matters most in high-volume agent workflows where every action cannot be manually reviewed.

There is no universal standard for exactly how much autonomy to permit, so current guidance suggests tiering agent privileges by task risk. Low-risk retrieval tasks may tolerate broader read access, while write actions, payment flows, infrastructure changes, and secret handling should use much stricter controls. The same logic applies to multi-agent systems: each agent should have only the minimum toolset and data scope needed for its role, not the combined permissions of the whole workflow.

Edge cases appear when agents operate across legacy systems, SaaS tools, and human-in-the-loop approval chains. In those environments, static RBAC can leave hidden privilege paths open, especially if the agent can call APIs that bypass the user interface. The AI LLM hijack breach and the DeepSeek breach both reinforce the same lesson: exposed secrets and broad credentials turn agent flexibility into a platform-wide problem, not a single-user one. Best practice is evolving, but the direction is clear: constrain agent authority at runtime, not just at onboarding.

Related resources from NHI Mgmt Group

• 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?
• Why do AI agents create more IAM risk than ordinary developer tools?