Topic starter
25/10/2026 11:01 am
TL;DR: Autonomous AI agents can execute code, invoke APIs and move data in milliseconds, making detection-only security too slow to stop harmful actions before they propagate, according to Codenotary. Runtime governance has to assess identity, permissions, prompt history and execution context in real time, or the control plane arrives after the damage.
Editorial analysis by NHI Mgmt Group, based on content published by Codenotary: “AI Agents Need Runtime Security, Not Just Detection”.
Key questions
Q: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.
Q: Why do detection-only controls fail against autonomous AI agents?
A: Because autonomous agents can complete harmful steps before a human ever sees the alert.
Q: What are the signs that an AI agent access model is becoming too permissive?
A: Warning signs include agents routinely reaching data outside their intended task, repeated approval exceptions, broad retrieval paths across unrelated systems, and outputs that expose information the user did not explicitly need.
Practitioner guidance
- Define runtime authorisation for agent actions Require each high-risk agent action to be evaluated against identity, permission scope, destination and data sensitivity before execution continues.
- Instrument execution graphs for multi-agent workflows Record task delegation, tool invocation, data handoff and downstream agent triggers so you can see how one compromise can spread.
- Scope permissions to the task, not the agent Remove standing write access where possible and grant only the smallest permission set needed for the current action or session.
Bottom line: Autonomous AI agents compress the time available for human response, which makes alerting alone an inadequate control model.
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