Technical Controls & Assurance
Testing, red-teaming, bias auditing, validation, and the technical assurance work that proves an AI system does what it should.
Endpoint AI Governance: The Controls Your Network Cannot See
AI moved onto the device, and gateway based controls stopped being sufficient without anything visibly breaking. This guide explains why desktop assistants, coding agents, local models and tool servers produce no inspectable traffic, what evidence actually sits on an endpoint, what each control layer can and cannot see, and how the gap connects to ISO/IEC 42001, the NIST AI RMF and the EU AI Act.
Bias Auditing and Fairness Testing: A Practical Method
A bias audit is a measurement exercise with a policy decision hidden inside it. This covers selection rates and impact ratios worked through, where the four-fifths convention comes from and what it does not mean, and what independence actually requires.
AI Red-Teaming and Model Testing: A Practical Method
Model validation asks whether an AI system works. Red-teaming asks whether it can be made to misbehave. This guide separates the two disciplines, maps them to the EU AI Act, NIST AI RMF, OWASP, and MITRE ATLAS, and lays out a program you can run and evidence.