Compliance & Governance

The definitive security risk taxonomy for MCP servers. 10 categories covering prompt injection, tool poisoning, command injection, data exfiltration, privilege escalation, and more.

Security risks specific to agentic AI applications. MCP Sentinel is the first tool to map detection rules to both MCP and Agentic Top 10 frameworks.

Adversarial Threat Landscape for AI Systems. ATLAS techniques mapped to MCP-specific detection patterns covering LLM prompt injection, data leakage, context poisoning, and agent manipulation.

The NIST AI Risk Management Framework provides standards for trustworthy AI. MCP Sentinel covers GOVERN and MEASURE functions through audit trail and human oversight rules.

Information security management standard. MCP Sentinel maps to 10 Annex A controls covering audit logging, access control, cryptography, supplier relationships, and system security.

AI Management System standard. MCP Sentinel covers human-in-the-loop requirements and AI transparency controls.

European regulation on artificial intelligence. MCP Sentinel covers Article 12 (record-keeping), Article 14 (human oversight), and Article 15 (robustness and cybersecurity).

Coalition for Secure AI threat model for MCP. MCP Sentinel covers 9 of 12 threat categories including authentication, authorization, tool safety, and supply chain integrity.

Multi-Agent Evaluation and Security Testing for Robust Operations. Layered security model for AI agent systems covering trust, isolation, observability, and governance.