305 posts on compliance & regulation.
Article 26 of the EU AI Act puts operational obligations on the deployer of a high-risk AI system. The deployer must monitor operation, suspend use under specific risk conditions, keep automatically generated logs, and inform the provider and authorities. The mandate takes effect August 2, 2026.
Article 19 of the EU AI Act sets the operational floor for log retention from high-risk AI systems at six months and specifies what the automatically generated logs must contain. The mandate takes effect August 2, 2026. Application logs fail the structural test.
Article 13 of the EU AI Act requires providers of high-risk AI systems to design them so deployers can interpret outputs, understand limitations, and exercise human oversight. The mandate takes effect August 2, 2026. Generic model cards fail the test.
Annex III of the EU AI Act lists the eight categories of AI systems classified as high-risk. Inclusion in Annex III triggers the full obligations of Articles 8 to 27 from August 2, 2026. Most enterprise teams are inside the scope without realizing it.
AI model governance fails when it sits at the model registry layer alone. Model cards and versioning catalog the asset. Per-request enforcement governs how the model is actually used. Article walks through the runtime layer most model governance programs leave out.
AI governance training fails when it gets delivered as a single all-hands course. Each role inside the enterprise needs different content. Article walks through the role-specific training tracks the regulators and auditors expect, and where the curriculum meets the runtime evidence requirement.
AI governance fails when no single role owns the per-decision audit trail. The CISO, CRO, General Counsel, CTO, and platform engineering each hold a slice. Article walks through the seven stakeholder roles, what each owns, and where the handoffs break in practice.
AI governance software splits into policy-building, inventory, and runtime enforcement. Most products in the category cover policy and inventory and leave runtime evidence to whatever the engineering team builds. Article walks through the architectural layers and what to ask vendors before signing.
Most AI governance policies are written for the auditor but cannot be evaluated at the request layer. A policy that lacks classification rules, identity definitions, and enforcement decision points is prose, not control. Article walks through what the policy has to specify to be enforceable.
AI governance certifications split into institutional certifications like ISO 42001 that examine the AI management system and professional certifications like IAPP AIGP that credential individuals. Article walks through what each program covers and where certification meets per-decision evidence.
AI governance audits turn on per-decision evidence. The auditor asks who initiated each request, what data was involved, what policy applied, and what the outcome was. Application logs collapse under those questions. Article walks through what an audit actually examines and the architecture that survives it.
AI ethics committees set principles. AI governance translates those principles into per-decision enforcement and audit records. Article walks through the seam between the two functions and what each one has to produce so a regulator can trace a principle to the decisions made under it.