The NSA just made the case for a policy layer in front of MCP
The NSA published 17 pages on MCP security. We map every recommendation to where enforcement actually happens: the call path between agent and tool.
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Thought Leadership
16 posts
AI Agent Containment Starts at the Environment Layer
Anthropic showed model defences can't stand alone: Claude leaked secrets 24 of 25 times under injection. Why AI agent containment belongs at the environment layer.
Microsoft AGT Proved Deterministic Enforcement. Where the Next Problem Starts.
Microsoft's Agent Governance Toolkit shipped the cleanest validation of deterministic policy enforcement in agent security: 26.67% violations under prompt-only safety, 0% under AGT. AGT governs one runtime. The harder problem is governance across many.
Runtime Governance Belongs at the Transport Layer
Oracle has named the category: runtime governance for agentic AI. Their framework is right; their architectural assumption is wrong for most teams. PolicyLayer enforces the same five pillars at the MCP boundary.
Anthropic's MCP Playbook Is for Builders. Defenders Need the Next Layer.
Anthropic published the production playbook for MCP: 300M SDK downloads, thin tools over 2,500 endpoints, OAuth vaults. The playbook stops at the tool call. Argument-level policy is what comes next.
MCP Governance Is Table Stakes. What Comes Next?
Cloudflare's enterprise MCP launch solves discovery, access, and shadow-MCP prevention. That's the baseline. The harder question — what agents are allowed to do once they're inside — needs a different primitive.
Microsoft's Agent Governance Toolkit: 9 Packages, MCP-Blind
Microsoft's open-source toolkit: nine packages for agent policy, identity, and compliance. Review of what works — and the MCP-shaped hole teams must bridge themselves.
What Is MCP Policy Enforcement (And Why Every Agent Needs It)
MCP policy enforcement intercepts every AI agent tool call and evaluates it against deterministic rules before execution. Here's how it works and how to set it up.
Why Prompt Guardrails Fail for AI Agent Safety (And What Works Instead)
System prompts can't enforce spending limits or prevent destructive operations. Here's why prompt guardrails fail for tool-calling AI agents and what works instead.
Bain Says Every Agentic Platform Needs a Policy Layer. We Built One.
Bain & Company's agentic AI architecture framework calls for centralised policy enforcement across MCP tool calls. Intercept is the open-source implementation.
The Agent Control Problem Only Becomes Big in One World
Most teams will wrap their own dangerous tools. The real market for agent control only gets large if agents become dynamic consumers of external services the team did not fully pre-wrap.
30 MCP CVEs in 60 Days. Most Fixes Are Solving the Wrong Problem.
Security researchers filed 30+ CVEs against MCP servers in early 2026. Patching individual servers doesn't fix the structural gap. The real fix is a policy layer that works across all of them.
The Academic Case for Deterministic AI Agent Enforcement
A new research paper argues that LLMs cannot self-enforce security constraints. Intercept implements every recommendation — as open-source software you can deploy today.
What Happens When Your AI Agent Goes Rogue
What happens when your AI agent goes rogue? Six failure modes — runaway loops, spending spirals, destructive ops — and the deterministic policies that stop them.
Why AI Agent Policies Must Be Deterministic, Not Probabilistic
LLMs can't reliably self-enforce safety rules. Deterministic policy enforcement outside the model catches what prompts miss — here's the architecture.
MCP Security: Why Prompt Guardrails Aren't Enough
Prompt guardrails for MCP agents are bypassable and unauditable. Why deterministic policy enforcement at the transport layer is the real security primitive.