Your audit log says the service account did it

AI coding agents create an accountability gap because they can act autonomously through credentials that identify neither the human who initiated them nor the agent’s runtime decisions, making attribution, authorization, and targeted revocation difficult. Ory Agent Security, now generally available, addresses this by treating the human and the AI agent as separate authenticated principals, enforcing permissions on individual tool calls, and preserving delegation records that link agent actions to the responsible user. Its policy model grants durable identities such as users, roles, and installations while allowing blocks at installation, session, or sub-agent levels, with explicit denials overriding grants. The product begins in observe mode to record permissions that would have been denied without disrupting workflows, then allows administrators to move projects into centrally controlled enforcement mode based on observed behavior. It produces OpenTelemetry-compatible traces containing identity, tool, decision, and workspace context while excluding sensitive command contents, source code, credentials, and request data. It also analyzes shell commands beyond their top-level tool names, enabling controls over programs invoked through pipelines, wrappers, or command substitution, though it currently cannot distinguish read-only from write operations within a program’s arguments. Ory Agent Security supports multiple coding-agent harnesses and Python and TypeScript SDK integrations, positioning identity-based controls and auditability as necessary foundations for governing AI agents in production environments.

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