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Govern AI agents with external RBAC, tracing, and policy compliance evaluations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Warden" yet — see the docs or source repo.
Design an RBAC policy for my MCP toolchain: the development agent can only read logs and deployment status, but cannot modify production configuration. Provide roles, a permission matrix, and example policies.
A clear role-permission design with boundaries and executable policy examples.
Help me plan how to use OpenTelemetry to trace each MCP tool call made by an AI agent, including key spans, log fields, error tags, and audit query suggestions.
An observability and audit plan describing what traces and logs should be collected.
Create an LLM-as-judge evaluation suite for my AI agent to verify it always follows access policies. Include test scenarios, scoring criteria, and violation examples.
An evaluation plan for compliance testing that helps uncover overreach and policy bypass issues.
Gate MCP agent actions through compliance policies before execution.
Enforce runtime tool-call policies and security scans for AI agents.
Secure MCP servers with policy checks, redaction, access control, and audit logs
Intercept and block MCP tool calls with YAML policies for safer AI agents.
Add OAuth auth, scope enforcement, and delegation auditing to MCP agents.
Manage notes securely with policy controls, approvals, and prompt injection detection.