Enforce contextual access controls
Implement safeguards to limit AI agent system access based on context and declared objectives
Control activities
Configuring contextual access controls for AI agents. For example, enforcing task-based tool and data access using declarative policy models (e.g. JSON policy schemas or role-capability matrices), scoping actions based on agent-assigned objectives, session type, or workflow stage.
Implementing privilege limiting for autonomous behavior. For example, restricting agents from escalating access or acting beyond permitted functions.
Deploying monitoring and enforcement mechanisms. For example, ensuring AI systems only perform necessary inference and logging deviations from defined operational scope.
Defining automatic restriction triggers. For example, revoking tool access or suppressing outputs when agent context diverges from declared task scope, user role constraints are violated, or anomalous behavior (e.g. lateral tool access or excessive data usage) is detected in real time.
Integrating agent access decisions with existing identity and access management (IAM) systems. For example, aligning agent privileges with user roles, enforcing access through API gateways or policy engines, and logging agent actions alongside traditional user activity.
Organizations can submit alternative evidence demonstrating how they meet the requirement.
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