Policy & Guardrails

This section implements dynamic security controls that allow AI agents to negotiate permissions and justify actions while maintaining security boundaries through policy engines and human-in-the-loop approvals.

Key Practices

• Embed policy engines (OPA/Cedar) in MCP wrappers
• Enable “explain” queries for AI agents to justify risky actions
• Implement cost and sensitivity thresholds for human approval
• Design negotiation protocols for security decisions
• Create audit trails for policy decisions and overrides

Implementation Guide

This section will provide:

• Policy engine integration patterns
• Negotiation protocol design and implementation
• Human approval workflow automation
• Risk scoring and threshold configuration
• Policy testing and validation procedures

Risk Mitigation

Addresses the limitations of static security controls by enabling context-aware decision making while preventing AI agents from bypassing security boundaries through social engineering or policy confusion.