Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
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.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →AI data governance fails when the classification engine runs on documents and not on prompts. The data lake is sorted, the AI request path is not. Article walks through the prompt-level classification, lineage, and disclosure architecture that satisfies the regulators asking new questions about model inputs.
Read post →AI compliance certification has shifted from a nice-to-have to a procurement gate. Customers ask vendors for ISO 42001 or NIST AI RMF alignment, SOC 2 with AI extensions, and per-decision audit evidence. Article walks through what to prepare, in what order, and where each certification meets the runtime evidence requirement.
Read post →AI agent security is the operational practice of constraining autonomous agents to act only within delegated authority and producing per-decision audit records that survive regulatory review. The NIST three-pillar framework names the architecture. Application logs and model guardrails do not satisfy it.
Read post →NIST Pillar 1 names verified agent identity as the foundation of the AI agent identity and authorization framework. Per-agent identifiers, delegated authority from the authorizing user, and structured propagation to the model API call are the production requirements. Static service credentials fail the test.
AI agent authorization is the per-request decision about whether a specific caller, against a specific resource, under a specific policy, is allowed to act. NIST calls it delegated authority. Most enterprise AI deployments solve authentication and skip authorization.
Read post →