SVP, Technology Risk & AI Governance

The SVP, Technology Risk & AI Governance is the enterprise leader accountable for enabling the responsible, secure, and scalable use of artificial intelligence. This executive will set the vision, operating model, policies, guardrails, and oversight needed to accelerate AI adoption while managing model, data, privacy, cybersecurity, ethical, compliance, and third-party risks. The role requires a proven technology risk executive. Deep experience leading enterprise technology, cybersecurity, data, controls, and regulatory risk programs is a foundational expectation and the platform from which this leader will build and mature AI governance.

The primary mandate is to translate that risk leadership into practical AI governance that protects the enterprise without slowing responsible innovation. The leader will serve as a trusted advisor and independent risk partner to senior executives, governance committees, and the Board, working across Technology, Information Security, Data, Legal, Compliance, Internal Audit, Model Risk, Procurement, and Business Operations.

These responsibilities are spread across several functional areas as outlined below.

Own and mature the enterprise AI governance strategy, framework, operating model, policies, standards, and decision rights. Establish practical guardrails and risk-based approval paths for AI development, acquisition, deployment, monitoring, material change, and retirement. Oversee AI risk assessments spanning model performance, data quality, privacy, cybersecurity, intellectual property, bias, fairness, transparency, explainability, human oversight, and regulatory compliance. Lead governance for generative AI, machine learning, large language models, agentic AI, and other emerging AI capabilities.

Maintain comprehensive AI inventories, use-case classifications, lifecycle controls, exception processes, and evidence of accountable ownership. Define enterprise metrics, key risk indicators, dashboards, and escalation protocols that provide a clear view of AI risk exposure and control effectiveness. Monitor changes in AI regulation, supervisory guidance, industry standards, and market practices, and translate them into timely enterprise action. Advise business and technology leaders on how to innovate responsibly, balancing speed, client and employee value, and risk.

Design and maintain an integrated AI risk and control framework aligned with enterprise risk management and existing technology, data, privacy, cybersecurity, compliance, and model risk programs. Establish testing, validation, ongoing monitoring, issue management, remediation, and continuous controls monitoring practices for AI solutions. Create clear accountability for AI outcomes across business owners, developers, vendors, control functions, and governance bodies. Oversee risk management for third-party AI providers, embedded AI capabilities, data sources, and foundation models, including due diligence and ongoing monitoring.

Identify emerging AI risks early and drive coordinated mitigation across the enterprise.

Lead and continuously strengthen the enterprise Technology Risk Management framework, policies, standards, risk appetite, key risk indicators, reporting, and escalation processes. Provide independent oversight and effective challenge across technology strategy, cloud adoption, cybersecurity, data governance, digital transformation, third-party technology, and operational resilience. Drive integration of technology and AI risks into enterprise risk assessments, RCSA, compliance, audit, issue management, and control assurance programs.

Present clear risk insights, trends, decisions, and recommendations to senior leadership, governance committees, regulators, auditors, and Board-level stakeholders.

Ensure AI and technology risk programs align with applicable regulatory expectations and recognized industry frameworks. Serve as the senior management liaison for examinations, audits, and regulatory reviews involving AI, technology, cybersecurity, data, and operational resilience. Partner with Legal, Compliance, Privacy, Information Security, Internal Audit, Model Risk, and Data Governance to build sustainable and coordinated oversight. Clarify decisions, ownership, and escalation across governance forums so that accountability is…