Every AI agent your business deploys eventually calls an MCP server, and every MCP server eventually calls an API. 42Crunch answers the five questions a security leader, a regulator, and a board all ask before that chain goes into production: what's out there, how exposed it is, whether it's compliant, whether the code behind it is safe, and whether it would hold up under attack.
Gartner expects task-specific AI agents to be built into 40% of enterprise applications by the end of 2026, up from under 5% in 2025 — and every one of those agents ultimately acts through an API or an MCP server. That's the last mile: the point where an autonomous decision becomes a real call against enterprise data and systems, with or without a human watching. Security spend is racing to keep up — Gartner puts the market for securing AI itself at roughly $2.8 billion in 2026, growing to an estimated $4.8 billion by 2027. 42Crunch was built for exactly that gap, extending a decade of API security-by-design down to the MCP layer instead of bolting on a new, separate tool.
Source: Gartner, "Gartner Forecasts the Market for Securing AI Will Reach Almost $5 Billion in 2027," press release, August 26, 2026.
"MCP's rapid proliferation has outpaced the development of its security model." — NSA
42Crunch answers each one from the same machine-readable contract — no separate tool and no separate process per concern.
Most enterprises can't produce a complete list of the MCP servers running against their data — spun up by a developer testing a new agent, bundled inside a vendor integration, or left behind after a proof of concept nobody decommissioned. You can't govern what you can't see.
Continuous, zero-touch discovery inventories every MCP server across the enterprise as it appears, and auto-generates a contract for each one — every tool, resource, and prompt it exposes — so security teams get an organization-wide baseline without waiting on developers to document anything by hand.
See how MCP Discovery works →Showing up in an inventory isn't the same as being safe. Without a consistent way to score risk, security teams are left eyeballing configs and hoping nothing was missed — until an agent does something it shouldn't have.
Every MCP server is deterministically scored against agent-facing threats — prompt injection, tool poisoning, data exfiltration, tool shadowing — and protocol-level risk, ranked on one dashboard. Posture becomes a number you can track over time, not a feeling.
See the posture dashboard →The EU AI Act, NIST AI RMF, ISO/IEC 42001, and a growing list of geography-specific regulations and standard frameworks all now touch how AI systems must be governed — and for most organizations, compliance evidence for MCP and AI agents still gets assembled by hand, after the fact, under deadline pressure.
Every audit and scan computes compliance posture against the framework itself in real time, mapping each finding to the specific control it breaches and exporting a versioned, audit-ready report — for regulators, auditors, and customer security reviews, across every geography you operate in.
See AI Regulatory Compliance →AI coding assistants generate APIs and MCP tools faster than any team can manually review them — and the same assistant, used across hundreds of engineers, reproduces the same vulnerability in every interface it touches, at a scale no human review process was built to catch.
Guardrails run at design and build time, driven by the same machine-readable contract the platform uses everywhere else — catching insecure patterns in AI-generated code before it merges, not after it's already running in production.
See AI Coding Guardrails →A server can look compliant on paper and still fail the moment someone actually tries to break it. Without active testing against known attack classes, that gap stays invisible until it's exploited — and by then it's an incident, not a finding.
Every server is tested against the OWASP MCP Top 10 — the industry's own benchmark for MCP-specific risk — with deterministic, repeatable results usable as evidence, not a one-time pen-test finding that goes stale the day it's filed.
See OWASP MCP Top 10 Protection →42Crunch already secures the API layer for some of the most demanding, highly regulated environments in the world — the same deterministic engine now extends to MCP.
Guardrails are embedded at design and build time, driven by the same machine-readable contract — an OpenAPI contract for APIs, an MCP Contract for MCP servers — all the way through to runtime enforcement.
Vulnerabilities get caught before release rather than found — and paid for — in production. A design-time fix is dramatically cheaper than the same fix after release.
Every assessment is contract-driven and repeatable — the same server or API evaluated twice returns the same score and the same findings, usable as audit evidence rather than a best-effort AI opinion.
IDE, CI/CD, gateway, container, and SIEM/SOC integrations mean security runs where development already happens, not as a separate gate bolted on afterward.
Customers report a sharp drop in false-positive alerts after adopting contract-driven scanning — precious attention goes to real risk, not triage.
Point the 42Crunch MCP scan at any MCP server and receive a scored report against the OWASP MCP Top 10 and your regulatory frameworks in under 60 seconds. No agent, no deployment, no commitment required.