Read-Only Is a Trust Level, Not a Feeling: How to Govern AI Ops Agents Before They Remediate Production

AI incident response governance emphasizes a structured approach where read-only diagnostics and remediation tools are distinctly separated by trust levels to minimize risks associated with autonomous changes in production environments. The process involves a lifecycle for ops agents that includes querying, correlating data, hypothesizing, validating, and recommending before remediation, with most incidents concluding at the recommendation stage unless higher trust policies are in place. Trust is classified by operation risk, not subjective assessments, ensuring that tasks like reading logs are low-risk while actions like changing infrastructure require stringent controls. The enforcement model assesses each tool call by agent identity, intent, and environment to determine trust ceilings, with higher-risk operations necessitating explicit approval and detailed audit trails for accountability. This framework, supported by tools like Permit, allows enterprises to implement scalable, defensible control planes across platforms such as Azure, Microsoft Graph, GitHub, and Kubernetes, ensuring that AI ops agents operate within clearly defined boundaries and under human-in-the-loop supervision where necessary.

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