An AI audit of FreeBSD is being conducted to analyze and potentially improve the operating system. This effort explores the application of artificial intelligence in evaluating system code and architecture.
The content provides an engineering guide on how to technically audit a vendor's AI system by monitoring its external API calls. It details how to use `mitmproxy` to detect if the system relies on third-party LLMs like OpenAI, Anthropic, or Google Gemini, or if it's self-hosted/rule-based.
As enterprise AI adoption grows, continuous monitoring of system performance becomes crucial. An "Enterprise AI Audit Checklist" and real-time quality scoring are essential to ensure accuracy and prevent model degradation post-deployment.
The article discusses the evolution of the STEM BIO-AI auditing system from a scoring mechanism to an integrated local audit system. It addresses the challenge of making AI audit tools practical for engineers to run, gate, inspect, and integrate into their workflows.
This technical post-mortem explains how a TypeScript RPC library (tRPC) was erroneously classified as High Risk by an AI-powered code audit tool under the EU AI Act. The false positive stemmed from the tool misinterpreting data serialization as machine learning components due to overlapping vocabulary.
This content explores the demand for AI audit services within the Small to Medium Business (SMB) market in Reno/Sparks, Nevada. It specifically targets companies with 20-100 employees and less than $5 million in revenue, highlighting a niche market for AI governance and compliance.