Topic starter
27/10/2026 7:09 pm
TL;DR: Agentic AI deployments need continuous visibility, controlled autonomy and accountability as agents move from recommendations to executing actions in production, according to Codenotary, with monitoring becoming insufficient once systems operate at machine speed. The governance challenge is no longer whether agents can act, but whether organisations can prove what they did, why they did it and who remains responsible.
Editorial analysis by NHI Mgmt Group, based on content published by Codenotary: “Codenotary Joins NVIDIA Inception to Accelerate Innovation in Agentic AI Security”.
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 autonomous AI agents increase security risk compared with read-only assistants?
A: Because risk comes from authorised action, not just generated output.
Q: What are the signs that an AI application has too much authority?
A: A common sign is that a single model response can write to databases, send messages, start workflows, or spend resources without a separate check.
Practitioner guidance
- Define agent identity before deployment Assign every autonomous agent a unique, managed identity so actions can be attributed, authorised and revoked across its lifecycle.
- Constrain agent permissions to the task Use task-scoped access and explicit policy checks so agents can only reach the systems and actions required for the current workflow.
- Log tool use and decision paths Capture each agent's tool calls, resource interactions and action sequence so security teams can reconstruct behaviour after an incident or anomaly.
Bottom line: Autonomous agents change the security problem from monitoring software to governing software that can act.
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