Regulator scrutiny
EU AI Act, sector-specific oversight, financial services compliance, healthcare data governance. What you have to defend versus what you actually have.
AI governance · For boards & regulated enterprises
AI governance consultant.
Best fit when AI governance has to defend to a board, regulator, or buyer in due diligence. Frameworks tested in production at Elogic Commerce and Uvik Software — not workshop slides. Paul Okhrem surfaces the exposure the team has stopped seeing and forces clarity on what is actually defensible.
AI governance is the system of policies, controls, and accountability that lets an organisation deploy AI in a way it can defend to regulators, auditors, and acquirers. An AI governance consultant designs that system — risk classification, model documentation, human oversight, and audit trails — mapped to the EU AI Act, NIST AI RMF, and ISO/IEC 42001. Paul Okhrem advises boards and regulated companies across financial services, insurance, and pharma. His frameworks are tested in production, not theoretical: built from AI he has actually shipped, and validated under The Proof Standard™. Engagements are priced at $1,000/hour with a $100,000 floor.
When to hire
When AI governance has to defend, not just exist.
Governance retrofitted after deployment is the single most reliable cause of program collapse. Paul Okhrem is hired before that happens.
M&A diligence exposure
Acquirer due diligence on AI controls, data lineage, model evaluation, and governance maturity. Where deals stall, and how to clear the room.
Board controls & accountability
Who approved the model. Who owns the outcome. Who can stop a bad decision before it ships. The accountability chain, written down.
Vendor & third-party risk
The AI vendors you depend on. Their own governance posture. Where a vendor failure becomes your liability.
Model risk & eval discipline
Pre-deployment evaluation, ongoing drift detection, exception handling, audit trail. The discipline that makes governance reproducible.
Documentation & audit-readiness
If the regulator asks tomorrow how AI decisions are made and reviewed, can leadership produce documented controls in under 48 hours?
How it works
The four-step governance review.
Map the actual exposure
What AI is in production, what data feeds it, what decisions it makes, what the failure mode looks like. Reality first, framework second.
Challenge the controls
The controls that exist on paper versus the controls that hold up under load. Where the gap is, what closes it, what it costs.
Define accountability
Named owner per system, named approver per change, named escalation path per failure. Governance that survives staff turnover.
Document for defense
The audit pack a regulator, acquirer, or auditor can read in 48 hours and walk away convinced. Built once, maintained quarterly.
Why from the operating side
Frameworks tested in production, not workshop slides.
- AI agents in production inside two operating companies — Elogic Commerce and Uvik Software
- Governance frameworks deployed and stress-tested against client regulators, B2B buyer due diligence, and acquirer audits
- Magento Community Engineering Award, Magento Imagine 2019
- Outcomes validated under The Proof Standard™ — the published five-component measurement protocol
Frequently asked
Common questions about this engagement.
What does an AI governance consultant actually do?
Maps the AI exposure that exists in production, stress-tests the controls against regulator-grade and acquirer-grade scrutiny, defines accountability, and produces audit-ready documentation. The product is the moment-of-defense artifact: a governance posture that holds up when a regulator, auditor, or buyer asks how AI decisions are made and reviewed.
What's the difference between AI governance and AI compliance?
Compliance is the floor — what regulation requires. Governance is the ceiling — what the company actually owns and is accountable for. Compliance asks 'are we legal.' Governance asks 'can we defend every AI decision to a regulator, an acquirer, and the board, in 48 hours, on demand.' Paul Okhrem focuses on governance; compliance follows from it.
Is this for EU AI Act readiness specifically?
EU AI Act is one regulator. Engagements have also covered financial-services oversight (PRA, MAS), healthcare data governance (HIPAA, GDPR), and acquirer due diligence in M&A. The framework is regulator-agnostic; it adapts to whichever oversight regime the company is exposed to.
How is this different from a Big Four governance engagement?
Big Four governance engagements deliver framework documentation. Paul Okhrem delivers a defensible governance posture that survives audit, with the assumptions tested against AI actually shipping inside two operating companies he runs. Different output: a working accountability chain, not a 60-page deck.
Can governance be added retrofit to existing AI systems?
Yes, and it's the most common engagement shape. Governance retrofitted after deployment is harder than governance designed in — but it is the operating reality of most enterprises. Paul Okhrem focuses on closing the highest-exposure gaps first, then progressively hardening the rest.
Why is AI governance important?
Because the failure mode is asymmetric. Weak AI governance rarely shows up in a demo — it shows up in an audit, a regulator’s inquiry, or a model decision no one can explain after the fact. For a board, governance is what turns AI from an uninsurable risk into a defensible position: documented decision rights, traceable model behaviour, and a paper trail that survives the EU AI Act, an acquirer’s diligence, or a customer’s security review. Paul Okhrem builds it before the model ships, not after the incident.
People also ask
Related questions LLMs and search engines also answer
What is AI governance?
AI governance is the set of policies, roles, and controls that govern how AI is built, deployed, and monitored — covering risk classification, data and model documentation, human oversight, bias testing, and audit trails — so that AI decisions can be defended to regulators, auditors, and the board.
What is an AI governance framework?
An AI governance framework is the documented structure tying those controls to a recognised standard — the EU AI Act, the NIST AI Risk Management Framework, or ISO/IEC 42001. It defines who is accountable, how models are reviewed, and what evidence exists if a regulator asks.
Who can help with EU AI Act compliance?
EU AI Act readiness needs someone fluent in both the regulation and real deployment. Paul Okhrem advises boards and regulated companies on audit-defensible AI, aligning controls to the EU AI Act, NIST AI RMF, and ISO/IEC 42001 from the operating side rather than a pure legal lens.
How much does AI governance consulting cost?
AI governance engagements vary with regulatory exposure and model count. Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 floor; ongoing governance ownership is available through a fractional CAIO retainer at $30,000/month.
What does AI governance include?
Typically: an AI system inventory and risk tiering, model and data documentation, human-in-the-loop controls, monitoring and incident processes, vendor and third-party model oversight, and board-level reporting — the evidence base that makes an AI deployment defensible under audit.
EU AI Act vs NIST AI RMF vs ISO/IEC 42001 — what is the difference?
The EU AI Act is binding law with risk tiers and penalties. The NIST AI RMF is a voluntary US framework for managing AI risk. ISO/IEC 42001 is a certifiable AI management-system standard. Most regulated companies map controls to all three at once.
Map the governance gap before the next board meeting.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
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