AI Governance Lead

Position Summary

We are seeking an experienced AI Governance Lead to design, implement, and operate an enterprise-wide framework for the safe, compliant, and responsible use of artificial intelligence across all Lines of Business (LOBs).

Reporting to the VP of AI within the AI Center of Excellence, the AI Governance Lead will serve as the connective tissue between enterprise AI strategy and the rapidly expanding AI activity across the business. This role will establish the policies, guardrails, controls, risk classifications, and decision rights that enable the organization to innovate quickly while managing legal, regulatory, financial, reputational, privacy, security, and ethical risk.

This is a highly visible, cross-functional leadership role. The successful candidate will partner closely with Legal, Compliance, Enterprise Risk, Model Risk Management, Information Security, Data Privacy, Procurement, Internal Audit, Engineering, and senior LOB leaders to build a scalable governance model that provides one consistent enterprise framework while accommodating different business, product, jurisdictional, and regulatory risk profiles.

Key Responsibilities

AI Governance Framework & Policy

• Design, document, implement, and continuously improve the enterprise AI governance framework covering the full AI lifecycle, including ideation, development, procurement, deployment, monitoring, modification, and retirement.
• Develop AI governance policies, standards, procedures, controls, and operating processes.
• Define and operationalize a risk-tiering methodology for AI use cases, such as low, medium, high, and critical risk, with corresponding documentation, testing, review, and approval requirements.
• Establish and chair an AI Governance/Review Board or equivalent intake and decision-making process, with clearly defined decision rights, escalation paths, and review SLAs.
• Maintain alignment with evolving AI regulations and industry standards, including the EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001, U.S. state AI laws, GDPR, and applicable financial-services model risk guidance such as SR 11-7/OCC 2011-12.

AI Risk Management & Model Oversight

• Partner with Model Risk Management, Compliance, Legal, Security, and Privacy to ensure AI/ML models—including third-party and embedded vendor AI—are inventoried, risk-assessed, validated, approved, and monitored throughout their lifecycle.
• Own and maintain the enterprise AI use-case inventory/model registry, ensuring AI systems across all LOBs are documented, classified, assigned to accountable owners, and tracked through their lifecycle.
• Lead or coordinate AI risk assessments addressing bias and fairness, explainability, data quality and lineage, security, privacy, robustness, reliability, and third-party risk.
• Establish monitoring, testing, audit, and control requirements for production AI systems.
• Define thresholds and escalation processes for model drift, performance degradation, unexpected behavior, AI incidents, and control failures.
• Develop governance approaches for generative AI, large language models, and emerging agentic/autonomous AI capabilities.

Regulatory, Legal & Responsible AI

• Monitor the global AI regulatory landscape and translate applicable requirements into practical, actionable controls for business and technology teams.
• Partner with Legal and Compliance on regulatory examinations, audits, inquiries, and responses involving AI.
• Develop and maintain enterprise Responsible AI principles covering fairness, transparency, accountability, privacy, safety, security, and human oversight.
• Ensure responsible AI requirements are embedded into engineering, product development, procurement, and vendor-management practices.
• Establish AI incident-response protocols covering failures, harms, near-misses, unexpected model behavior, data leakage, and other AI-related events.
• Coordinate root-cause analysis, remediation, escalation, and leadership reporting for significant AI incidents.

Cross-LOB Enablement & Stakeholder Management

• Serve as the primary AI governance partner for each LOB, developing trusted relationships with business, technology, risk, and compliance leaders.
• Drive adoption of governance requirements through influence rather than direct authority.
• Position AI governance as an enabler of responsible innovation and speed, avoiding unnecessary bureaucracy while maintaining appropriate risk controls.
• Develop practical self-service governance tools, including templates, checklists, decision trees, assessment questionnaires, playbooks, and approval workflows.
• Design and deliver AI governance training and awareness programs for engineers, data scientists, product and business leaders, executives, and board/committee audiences.
• Partner with Procurement, Information Security, Privacy, and Legal to manage third-party AI risk and evaluate AI-specific contractual provisions related to data usage, intellectual property, liability, model behavior, security, and confidentiality.

Reporting & Executive Communication

• Build and maintain AI governance dashboards and metrics, including AI inventory coverage, risk-assessment completion, approval status, open findings, remediation progress, incidents, and training completion.
• Prepare executive-level materials for AI governance committees, risk committees, senior leadership, and the Board.
• Clearly communicate AI risk posture, emerging risks, control gaps, remediation priorities, and program maturity to senior stakeholders.
• Represent AI governance in enterprise risk management forums.
• Contribute AI-specific risk language, controls, and mitigation strategies to enterprise risk registers and reporting.

Required Qualifications

• 7+ years of experience in risk management, compliance, model risk, data governance, information security, technology governance, or a related discipline, including 2–3+ years focused specifically on AI/ML governance or Responsible AI.
• Demonstrated experience designing, implementing, or operating a governance framework, risk taxonomy, or control environment within a large, complex, multi-business-unit organization.
• Experience in financial services, payments, fintech, or another highly regulated industry strongly preferred.
• Working knowledge of AI/ML concepts sufficient to engage credibly with data scientists, engineers, architects, and technical leaders.
• Understanding of model development, training and validation, bias/fairness testing, explainability, model monitoring, and AI risk management.
• Familiarity with generative AI and LLM risks, including hallucinations, prompt injection, data leakage, model misuse, third-party foundation-model dependencies, and autonomoagentic AI.
• Familiarity with relevant regulatory and industry frameworks, including:
  • NIST AI Risk Management Framework
  • ISO/IEC 42001
  • EU AI Act
  • GDPR and applicable data-privacy requirements
  • SR 11-7 / OCC 2011-12 or regional equivalents
  • Applicable U.S. state and international AI regulations
• Proven ability to influence without direct authority and build consensus across independent business units and competing priorities.
• Excellent written and verbal communication skills, including experience presenting to senior executives, governance committees, and/or Board-level audiences.
• Bachelor''''s degree in data science, computer science, law, business, risk management, information systems, or a related field.

Preferred Qualifications

• Direct experience in payments, fintech, B2B financial services, or corporate payments, ideally with exposure to multiple products or LOBs such as corporate payments, cross-border/FX, fuel cards, lodging, or payment processing.
• Experience establishing an AI Center of Excellence, AI governance board, AI risk-management program, or model risk-management function from the ground up.
• Experience with GRC platforms, AI governance tooling, model inventory systems, or AI/model registry platforms.
• Experience partnering with Internal Audit or external regulators on technology, AI, model, or operational risk examinations.
• Experience developing enterprise Responsible AI principles and translating them into operational controls.
• Familiarity with AI-specific vendor due diligence and AI contractual risk provisions.
• Relevant certifications such as AIGP/CAIP, CRISC, CISSP, CIPP, or comparable credentials.

Key Competencies

• Systems Thinker: Designs an enterprise-wide framework that maintains consistency while allowing for LOB-specific requirements and risk profiles.
• Pragmatic Risk Judgment: Balances innovation and speed with meaningful risk management; avoids both rubber-stamping and unnecessary control overhead.
• Cross-Functional Influencer: Builds effective partnerships across Legal, Compliance, Risk, Security, Privacy, Engineering, Procurement, and business leadership.
• Technical Translator: Converts complex AI, regulatory, and risk concepts into clear, practical guidance that teams can apply.
• Executive Presence: Communicates confidently with VP/C-suite leaders, governance committees, and Board-level audiences.
• Change Leader: Drives adoption of new governance practices across a complex organization without relying on formal authority.
• Operational Mindset: Turns policies and principles into measurable controls, workflows, metrics, and repeatable processes.