We publish measured results and original analysis — not marketing claims. Everything here is reproducible: we tell you the method, then the numbers.
Detection-accuracy benchmark
Security tools are only as good as what they actually catch. We benchmark our own detection the honest way: plant a fixture of known issues alongside clean controls, run the real collector/adapter against it, and measure recall (planted issues detected) and false positives (findings in clean files). No estimates.
| Capability | What's measured | Recall | False positives |
|---|---|---|---|
| Endpoint sensor — discovery | API keys, MCP configs, AI CLIs/SDKs, local skills across a planted $HOME | 100% | 0 |
| Source Code Analysis (SAST) | Planted vulns vs clean controls (semgrep pack) | 100% | 0 |
| Secret & Credential Scan | AWS/GitHub/Slack/Stripe/OpenAI keys + private keys | 100% | 0 |
| Dependency / Supply-chain (SBOM) | Vulnerable + typosquatted packages vs safe pins | 100% | 0 |
| Container & IaC posture | Misconfigurations vs hardened baselines | 100% | 0 |
| AI-BOM & License/Provenance | Model files, pickles, copyleft licences, unpinned revisions | 100% | 0 |
Each capability was tuned iteratively — baseline → measure → fix → re-measure — and the benchmark caught real bugs along the way (e.g. a missed ~/.mcp.json at the home root, an unscanned .pem extension). Full methodology and per-iteration results are in the reports:
The Shadow AI Risk Index — methodology
A repeatable way to score an organisation's agentic exposure. The Index combines five weighted surfaces into a 0–100 score, so posture is comparable over time and across business units:
| Surface | What it captures |
|---|---|
| Unsanctioned AI | Agents, CLIs, SDKs and apps running without approval, weighted by reach. |
| MCP & tool exposure | MCP servers and the blast-radius of the tools they expose (file, network, credential access). |
| Secret exposure | API keys and tokens reachable by AI processes, weighted by provider and scope. |
| Egress & data flow | Sensitive data paths to external AI endpoints; DLP coverage. |
| Governance gap | Attribution, offboarding, and framework-control failures (OWASP LLM / NIST AI RMF / EU AI Act). |
The Index is the scoring model behind the platform's posture score. We're publishing the framework openly so teams can reason about agentic risk consistently — the full specification ships with the flagship report below.
The State of MCP Security 2026 In progress
Our flagship landscape report — the MCP threat taxonomy (tool poisoning, confused deputy, slopsquatting, over-scoped tools, tool-output injection), what the public advisory record shows, the Shadow AI Risk Index specification, and our measured detection results. It draws on public advisories and incident reports and our own benchmarks — no invented telemetry. Get notified on release →