Build an AI governance framework that satisfies investors, boards, and regulators without slowing down your product teams.
AI governance is the system of accountability, policies, controls, and oversight that determines how artificial intelligence is approved, used, monitored, and retired inside your company.
Done well, it answers five questions your board, your investors, and eventually your regulators will all ask in some form:
Done badly, AI governance is a PDF nobody reads and a policy nobody enforces — while employees paste customer data into consumer chatbots and leadership finds out from an audit. The gap between those two outcomes is what this practice exists to close.
Investors and regulators are no longer asking whether you use AI. They are asking how you govern it — model selection, data handling, vendor risk, human oversight, and incident response.
For FinTech and crypto companies, the question is sharper. AI touches customer data, transaction monitoring, fraud models, and customer communications. A policy written in a vacuum won't survive diligence.
Three forces are converging at once. Enterprise customers now send AI-specific questions inside their vendor assessments: where models are hosted, whether customer data trains anything, what human review exists. Investors increasingly treat ungoverned AI as technical debt and litigation exposure in diligence. And regulators — from banking examiners to the FTC to state legislatures — are writing AI rules faster than most companies are writing policies.
The companies that handle this well do one thing differently: they treat AI governance as an extension of the security and risk program they already have, not a separate initiative. That's the approach we build.
You don't need to memorize framework numbers. You need a program that maps cleanly to them, because your board, auditors, and enterprise customers each speak a different one.
The de facto US framework, organized around four functions. Govern — accountability structures, policies, and culture. Map — knowing where AI is used and what risks each use carries. Measure — testing, validation, and monitoring of systems in use. Manage — prioritized treatment of the risks you found, with feedback loops back into governance.
We use AI RMF as the operating skeleton for most engagements because it's practical, vendor-neutral, and increasingly the reference point in US vendor questionnaires and examiner questions.
The first certifiable AI management system standard — think of it as ISO 27001 for AI. It formalizes what AI RMF describes: leadership accountability, documented policies, risk treatment, operational controls, and continual improvement, in an auditable structure.
You may not need certification. But building your program so it could be audited against 42001 pays off twice: once in diligence, where a certifiable structure reads as maturity, and again later if an enterprise customer or regulator makes it a requirement.
For FinTech companies, AI governance lands on top of SOC 2, PCI DSS, GLBA, NYDFS Part 500, and state money transmitter obligations. We integrate AI controls into your existing security program — one risk register, one policy set, one audit trail — rather than building a parallel AI bureaucracy that duplicates what your compliance team already does. That integration is where most standalone AI policies fail: they exist outside the program, so nothing enforces them. The same integrated approach covers digital asset and stablecoin security for crypto businesses, and our fractional CISO for fintech practice runs the broader program all of it lives in.
Every engagement covers these five areas, scaled to your size and risk.
You cannot govern what you have not found. We build a living inventory of every AI system touching your company: models you've built, vendors you've bought, features embedded in SaaS you already run, and the consumer AI tools your employees use with company data. Discovery combines interviews, network and SaaS signals, procurement review, and expense analysis. The inventory records owner, purpose, data touched, risk tier, and review date — and it becomes the backbone of every other control.
A governance charter that says who decides, an acceptable-use policy that says what employees can and cannot do, and data-handling rules that draw bright lines around customer data, transaction data, and confidential material. Policies are written for the people who have to follow them — short, concrete, scenario-based — and wired into onboarding and tool procurement so they're enforced, not filed. Our AI governance policy template gives you the starting structure.
Most FinTech AI is someone else's model behind an API. We build review criteria for AI vendors: training-data practices, data retention, sub-processor chains, model change notification, security certifications, and contractual remedies. New vendor questionnaires get AI sections; existing contracts get reviewed for silent model updates that change your risk profile overnight.
Where does a human check the machine's work? We define oversight points for each risk tier — from full human review of customer-facing outputs to sampled QA on internal tools — plus monitoring for drift, unusual usage, and data-leakage patterns. And because AI failures are incidents, we fold model failures, hallucination harm, and prompt-injection into your incident response plan with real escalation paths.
A quarterly AI risk narrative your directors can read in ten minutes: what's in the inventory, what changed, what's approved and what's pending, incidents and near-misses, and where regulatory exposure sits. In diligence, this package turns "we're figuring out AI" into "here's our program" — a materially different conversation.
You don't need a law degree — you need to know which expectations land on you and when.
Banking regulators expect third-party AI risk to be managed like any other vendor risk, with documentation. The FTC has been explicit that unfair or deceptive AI claims are enforcement targets — which makes your marketing language a compliance surface.
States are moving on AI in insurance, lending, and employment decisions, and several now regulate automated decision-making in consumer finance. If you operate nationally, your exposure is the sum of your states.
If you have EU customers, the AI Act's obligations phase in through 2026-2027. Classification comes first: most FinTech use cases land in limited-risk or general-purpose territory, but credit scoring and some risk pricing sit closer to high-risk tiers.
If you're public or heading there, AI materiality belongs in your cybersecurity and risk disclosure posture — the same discipline as your 10-K cybersecurity disclosures, applied to model risk.
We're advisors, not lawyers — legal counsel reviews regulatory positions. But we speak both languages, and we build the technical documentation your counsel needs to give confident advice.
A practical, ready-to-customize governance package for companies adopting AI under investor, board, or regulator pressure.
Based on 20+ years of security and governance leadership, including taking a crypto company public.
If employees use AI tools with company data, or AI features touch customer data, you need governance — the scale changes, the need doesn't. A 30-person FinTech needs a lean version: inventory, policy, approval path, and oversight. What you don't need is enterprise bureaucracy.
A policy is one of five pillars. Without an inventory, approval workflow, vendor review, and monitoring, a policy is a statement of intent — and diligence teams know the difference. The audit question is never "do you have a policy?" It's "show me it operating."
Bans fail in practice — they push usage into shadow channels you can't see, which is worse than governed use. Risk-tiered approval keeps the productivity gains while drawing hard lines around customer data and regulated decisions.
AI governance maps onto existing trust services criteria: risk assessment, change management, vendor management, and access controls. We fold AI controls into your SOC 2 program so one audit covers both — not two compliance tracks. The same holds for our digital asset security work: custody and key-management controls live inside one program, one risk register, one audit trail.
A lean foundation — inventory, charter, acceptable-use policy, vendor criteria, and board reporting — typically lands in 6–8 weeks. Deeper integrations (model validation, incident response, 42001-style auditability) run longer, scoped to your risk.
Yes — it's a specialty. AI in crypto adds custody-adjacent risks, exchange vendor exposure, and a public-market disclosure layer that generic AI consulting doesn't cover. The principal has operated inside a Nasdaq-listed crypto FinTech, not just advised one. For the security side of that world — custody, keys, stablecoin controls — see our digital asset cybersecurity practice, and our fractional CISO for fintech services covers the broader regulated-fintech program.
CTOs, CISOs, COOs, legal, and compliance teams at companies adopting AI tools and facing investor, board, or regulator questions about governance, risk, and acceptable use.
Show VCs you have a defensible AI governance posture before they ask.
Give the board a clear policy framework instead of ad-hoc rules.
Build the documentation regulators expect around AI risk and human oversight.
I work with leadership teams to build AI governance frameworks that satisfy investors, boards, and regulators without slowing down the business.
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