MCP Security: Defending the New AI Attack Surface
AI agents are a new attack surface. MCP is the control plane.
A concise guide to MCP risks, agent permissions, and the guardrails teams need in production.
Format
Coverage
12 Chapters
Audience
Security Teams
What You'll Learn
A practical security guide for teams moving from AI demos to production agents.
Treat MCP as privileged
Treat MCP like production infrastructure, not a convenience layer.
Understand the attack paths
See how servers, prompts, and over-scoped tools create real risk.
Ship with guardrails
Use least privilege, boundaries, telemetry, and policy checks by default.
Who It's For
Written for the people building and defending agent-powered systems right now.
The book is grounded in real security operations, enterprise integrations, and the operational lessons that appear when agents can read, decide, and act across production systems.
Security engineers and SOC analysts defending AI-enabled environments
Platform engineers and architects adopting MCP-based automation
AI/ML engineers building agents with access to real systems
CISOs and technical founders evaluating governance for autonomous workflows
Inside The Book
The main themes covered across the 12 chapters.
Threat Modeling MCP
How to identify assets, entry points, and blast radius when agents can act across multiple systems.
Supply Chain and Tool Risk
Why malicious or backdoored MCP servers can quietly exfiltrate data and how to vet them before deployment.
Least Privilege and Runtime Hardening
How to scope tools, isolate execution, restrict egress, and enforce policy outside the model.
Monitoring and Secure Adoption
How to instrument MCP activity, feed telemetry into the SOC, and roll out agent systems safely over time.
Download The Book
Start securing AI agents before MCP becomes a blind spot in your environment.
MCP Security: Defending the New AI Attack Surface gives security teams, founders, and engineers a concrete framework for deploying MCP-based systems with stronger trust boundaries and better observability.