AI Governance
Most organizations adopted AI before they had any plan to govern it. AI governance is the work of getting ahead of that: knowing what systems you have, what risks they carry, and what controls are in place.
The AI Oversight Gap
Models are in production, employees are feeding sensitive data into ChatGPT, and the vendor stack is full of "AI-powered" tools nobody evaluated. Boards are asking questions nobody can answer yet.
What You Get
A complete AI governance program built for your organization, not a generic template.
AI System Inventory
Catalog every model, API integration, AI-powered SaaS tool, and employee using generative AI. Classify each by risk tier based on data sensitivity, decision impact, and failure consequences.
Enterprise AI Policy
Acceptable use policies, procurement standards, development guardrails, data handling requirements, and vendor evaluation criteria written for your specific risk appetite and regulatory exposure.
Bias Auditing & Fairness Testing
Test AI outputs for discriminatory patterns across protected classes. Evaluate model explainability and establish ongoing monitoring for performance degradation and concept drift.
Vendor AI Assessment
Scrutinize third-party AI vendors on training data practices, bias testing methodology, security posture, and contractual protections. Most vendor AI assessments are either missing or a checkbox exercise.
Board-Level Reporting
Executive AI briefings that translate technical risk into business language. What is the exposure, what controls are in place, and what is the maturity trajectory.
Compliance Roadmap
Map obligations across EU AI Act, NIST AI RMF, ISO/IEC 42001, state legislation, and sector-specific rules. Build the documentation and controls regulators expect to see.
How It Works
AI governance is not a one-time audit. It is a continuous program that grows with your AI portfolio.
Discovery & Assessment
Shadow AI Discovery
Find every AI system in the organization, including the ones nobody told you about. Employees using ChatGPT, copilots embedded in dev tools, AI features auto-enabled in SaaS products.
Risk Classification
Tier every system by what data it touches, what decisions it influences, who is affected by outputs, and what happens when it is wrong. This classification drives every control decision downstream.
Vendor AI Due Diligence
Evaluate every vendor claiming AI capabilities. Training data provenance, bias testing evidence, security architecture, data retention, and whether their contractual protections actually mean anything.
Regulatory Exposure Mapping
Determine which AI regulations apply based on your jurisdictions, industry, use cases, and the risk tiers of your systems. Build a compliance obligations matrix.
Program & Controls
Policy Framework
Draft enterprise AI policies covering acceptable use, procurement, development guardrails, and data handling. Written for your organization, not copied from a template library.
Bias & Fairness Testing Program
Establish testing protocols for discriminatory patterns, build monitoring for concept drift and performance degradation, and create remediation workflows when issues are found.
Compliance Implementation
Build the documentation, controls, and processes that regulators expect. Impact assessments, transparency disclosures, human oversight mechanisms, and audit trails.
Ongoing Governance
Regulatory landscape tracking, periodic reassessment of AI systems, policy updates as your usage evolves, and board-ready reporting on AI risk posture.
Regulatory Coverage
The AI regulatory surface area is expanding fast and enforcement is starting. I track the landscape so you do not have to.
EU AI Act
NIST AI RMF
ISO/IEC 42001
State AI Laws
SEC AI Disclosures
Healthcare AI Rules
Financial Services AI
Hiring & Employment AI
Standalone or Integrated
AI governance works as an independent engagement or as a natural extension of security and privacy leadership. AI risk is security risk, AI data handling is privacy compliance.
Standalone
You already have security and privacy leadership. You need someone who knows AI governance specifically.
vCISO + AI Governance
Unified security and AI governance. AI threat modeling, adversarial risk, and governance as part of your security program.
vCPO + AI Governance
Privacy and AI governance overlap heavily: data minimization, consent, automated decision-making rights, impact assessments.
Get Ahead of It
Before the Regulators Do
The organizations figuring out AI governance now will have a regulatory head start and a defensible position when something goes wrong. The ones waiting will be scrambling.