How We Audit Local AI Telemetry

Our multi-stage security audit inspects binaries, monitors network traffic, and analyzes source code to verify zero-telemetry claims.

WHY IT MATTERS

Vendor privacy statements often contradict actual background binary behavior during operation. A rigorous technical audit guarantees true data privacy for self-hosted AI deployments.

GO DEEPER

• Static code analysis: Scan GitHub source code for embedded tracking libraries and telemetry SDKs.
• Dynamic network capture: Monitor Wireshark and eBPF network sockets during model inference.
• Binary string extraction: Inspect compiled binaries for hardcoded analytics URLs and tracking tokens.
• Air-gap testing: Run tools in isolated sandbox environments to identify broken dependencies.
• Dependency verification: Validate third-party libraries for hidden telemetry and license risks.
• Automated scanning: Run ScanCode and static analyzers across model execution frameworks.

THE BOTTOM LINE

Empirical network and binary verification is the only reliable way to confirm vendor privacy claims.