Deterministic policy enforcement for AI agent tool calls. It evaluates every tool call against user-defined rules before execution, with no LLM in the authorization path.
An enforcement layer that validates AI agent actions against governance policies, including path permissions and content scanning, at runtime. It enables secure, role-based execution of file operations and commands with zero token overhead by processing policies independently from the agent's context.
Policy-based governance for AI agent tool calls. YAML policies, approval gates, risk assessment, and audit logging across LangChain, OpenAI, Anthropic, and MCP.
Enforces deterministic policies on AI agent tool calls, evaluating actions against compliance modules (SOC 2, HIPAA, GDPR, etc.) and returning ALLOW, BLOCK, or CONSTRAIN decisions with an audit trail.
Provides policy-based access control, incident tracking, and compliance monitoring to govern AI agent behavior. It enables organizations to enforce security rules and maintain audit trails by validating agent actions against trust levels and pattern-based policies.
Provides real-time policy enforcement for AI coding agents by intercepting and validating their actions against organizational standards like naming conventions, security policies, and compliance rules before execution. Prevents violations through immediate feedback and auto-correction suggestions.