AI Governance & Data Privacy
Responsible AI governance, algorithmic risk assessment, and ethical deployment frameworks for organizations building or adopting AI systems in regulated industries, plus GDPR and CCPA data privacy compliance. Governance that holds up when your examiner, sponsor bank or board asks how it works.
AI Is Moving Faster Than Governance
Organizations across financial services, healthcare, and technology are deploying AI systems that make consequential decisions about people. Credit decisions, fraud detection, hiring algorithms, clinical recommendations. The models are powerful. The governance often is not.
Regulators are catching up. The EU AI Act, NIST AI Risk Management Framework, OCC model risk guidance, and state-level algorithmic accountability laws are creating a patchwork of requirements that organizations need to navigate now, not later.
We help organizations build AI governance that is practical, defensible, and aligned to both current regulatory expectations and the ethical obligations that come with deploying systems that affect real people.
AI Governance and Data Privacy Services
Three service areas, one standard: every system and every data flow should have an owner, a documented decision behind it, and evidence that it works as intended. GDPR and CCPA engagements are delivered through the practice led by Jameelah Haadee, J.D., M.S.
AI Governance Programs
The policies, roles and oversight that let you approve, run and retire AI systems deliberately, not by default.
- AI governance framework design
- AI ethics policy development
- EU AI Act readiness assessment
- NIST AI RMF implementation
- Board and committee AI governance advisory
- Responsible AI training and culture
AI Evaluation & Model Risk
AI evaluation that shows each system does what you say it does, tested against real output and monitored after launch.
- Algorithmic risk assessment
- Model risk management (SR 11-7 alignment)
- Vendor AI due diligence
Data Privacy
Privacy programs built to the obligations that apply to you, with controls that are tested, not just written.
- GDPR and CCPA data privacy compliance programs
- Data privacy control testing
- Data governance framework design
Inventory, Govern, Evidence
Every engagement follows the same lifecycle, whether it starts with one vendor model or an enterprise AI and privacy program.
- 01
Inventory
Find every AI system, model and vendor tool in use, including the ones embedded in platforms you already run, and the personal data each one touches. Record who owns each system, who it affects and what it decides.
- 02
Govern
Set risk tiers, approval standards, accountability, human override and escalation paths, aligned to the frameworks and privacy obligations that apply to you. Put reporting in front of the committee or board that owns the risk.
- 03
Evidence
Test and document so someone outside the project can follow it: evaluation results, validation records, monitoring against defined thresholds, decision logs and privacy control testing. Evidence your examiner, sponsor bank and board can read.
Where Clients Start
The signal that it is time to govern AI or privacy properly is usually specific. Start where you are.
A sponsor bank or examiner asks about your AI
A diligence request or exam request list asks which models you use, who approved them, how they were tested and how they are monitored. You need the inventory and the evidence, not a slide.
Sponsor bank readinessA board asked to approve an AI deployment
The board or risk committee is being asked to sign off on a new AI system and wants to know what it is approving, who is accountable and what happens when the system is wrong.
Five questions before you deployAn AI vendor inside a core process
A vendor tool now scores alerts, screens customers or drafts decisions. It needs diligence, validation and monitoring that treat it as your risk, not only the vendor's.
BSA/AML and transaction monitoring complianceA new privacy obligation
Customers in California or the European Union, a new use of customer data, or a new vendor can bring GDPR or CCPA obligations into scope, and you need a program that can show it is working.
Five Questions Before You Deploy
Who does this system affect?
Every AI system makes decisions that land on real people. Before deployment, you need to map the human impact. Who benefits? Who bears the risk? Are the affected populations represented in your training data and testing protocols?
Can you explain how it works?
Explainability is not optional. Regulators, boards, and the people affected by AI decisions deserve to understand how those decisions are made. If your model is a black box, your governance is incomplete.
What happens when it is wrong?
Every model produces errors. The question is whether you have designed for failure. Escalation paths, human override protocols, error correction mechanisms, and redress processes must be built before deployment, not after.
Who is accountable?
AI governance requires clear lines of accountability. Model owners, risk committees, compliance oversight, and board reporting. If no one is accountable for the system, no one is governing it.
How will you know it is working as intended?
Working as intended is not a subjective aspiration. It is a testable property. A model that performed well at launch can drift once it meets live data, new products or new customers. Evaluation against defined metrics and thresholds, and ongoing monitoring reported to the people accountable for the system, are the minimum. Your organization needs to define what working as intended means for each use case and test against that definition.
AI Evaluation Against Real Output
Governance is only as credible as the evidence behind it. For AI, that evidence comes from evaluation. Define what a system is supposed to detect or decide, run it against live data, and tune it against what it actually produces. Then write the reasoning down so an examiner, bank partner or board can follow it.
It is the same discipline behind our independent BSA/AML review and testing work: a control has not been shown to work until it has been tested against real activity.
Regulated Financial Services Client
AI-assisted anomaly detection over live transaction and third-party data, with detection logic defined and tuned against real output.
Who Leads This Practice
AI Governance & Data Privacy Practice Lead
Jameelah Haadee, J.D., M.S.
Leads delivery of Ethixera's GDPR, CCPA and AI governance engagements
Jameelah leads Ethixera's AI Governance & Data Privacy practice, advising organizations on responsible AI deployment, intellectual property strategy, and data governance frameworks aligned to NIST AI RMF, SR 11-7, and emerging global standards including the EU AI Act.
With dual expertise in intellectual property law and AI governance, Jameelah works with clients at the intersection of innovation and regulation, helping organizations capture the upside of emerging technology without inheriting the downside risk. Her engagements range from AI governance program design to model risk governance and IP strategy for AI-developed innovations.
Victor B. George, JD, Ethixera's founder and principal, supports the practice's testing and evidence work: AI governance and evaluation, and U.S. and U.K. data privacy control testing. He has discussed the governance challenges organizations face when deploying AI in regulated industries as a guest on AI or Not, E021 with Pamela Isom.
Regulator-facing experience: direct engagement with FinCEN, state banking departments, FDIC and OCC exam teams and the DOJ, plus Big Four and national-firm validation work.
Reading for Boards and Fintech Teams Deploying AI
The five questions every board should ask before approving AI deployment
For boards and risk committees asked to approve an AI system.
Board questions before AI deploymentThe partner bank compliance conversation fintech founders keep avoiding
For fintech founders preparing for, or already inside, a sponsor bank relationship.
Partner bank compliance for fintechsQuestions we hear
Can AI clear our alerts?
AI can help work the queue. It can score and prioritize alerts, pull together the context an analyst needs, and suggest a disposition. It should not be the one that decides. A person with the authority to close an alert or escalate it makes the call, and the case record has to show why, including what the model recommended and whether the analyst agreed. Examiners and bank partners generally expect documented decisioning they can follow. The model itself also needs to be validated, monitored and governed under your model risk framework like any other model in the process.
Will our sponsor bank review how we use AI?
Generally, yes. Banks are expected to manage the risks of their third-party relationships, including the fintech programs they sponsor, so AI used inside your program typically falls within that oversight. If AI touches onboarding, transaction monitoring, sanctions screening, fraud or customer decisions, expect questions about what each model does, who approved it, how it was tested, how it is monitored and who can override it. We help you build that inventory and evidence before the request arrives. See sponsor bank readiness for the rest of the diligence pack.
Do you handle GDPR and CCPA compliance?
Yes. GDPR and CCPA data privacy compliance is part of this service line, and engagements are delivered through our AI Governance & Data Privacy practice, led by Jameelah Haadee, J.D., M.S. Work ranges from building a privacy compliance program (data inventory and mapping, notices, handling of individual rights requests, vendor oversight and retention) to testing whether privacy controls operate as designed. Scope comes first. Some of your data may fall under the Gramm-Leach-Bliley Act rather than the CCPA, for example, and that legal call stays with your counsel.
Which AI frameworks do you align to?
The NIST AI Risk Management Framework, SR 11-7 and OCC model risk guidance, and the EU AI Act where it applies to your systems or markets, with GDPR and CCPA for the personal data those systems use. We start from what your regulator, your bank partner and your markets expect, then map controls once so one set of evidence answers several frameworks instead of running a separate program for each.
Is Ethixera a law firm?
No. Ethixera Advisory is a compliance advisory firm, not a law firm, and does not provide legal advice. We design, test and help run AI governance and data privacy programs, and we work alongside your counsel wherever a question calls for legal judgment, such as how a privacy law or the EU AI Act applies to you.
Deploying AI? Build the Governance First.
Whether you are evaluating AI vendors, building internal models, or responding to regulatory expectations, we help you build governance that is practical, defensible, and aligned to your obligations.