VP, Generative AI Risk Oversight

Essential Responsibilities

• Engage the Information Technology organization in reviewing and assessing AI risk management practices and controls for AI/GenAI/Agentic AI solutions, ensuring risks are understood, measured, and managed in alignment with business objectives and risk appetite.
• Risk assessment and control design: Perform or oversee AI risk assessments (use case, model, vendor, and process), documenting inherent/residual risk, control effectiveness, and remediation plans.
• GenAI and LLM risk controls: Recommend guardrails for prompt/response safety, content moderation, jailbreak/prompt injection defenses, RAG data hygiene, retrieval security, and output verification.
• Agentic AI controls: Establish policies and technical controls for tool access, authorization, least privilege, action confirmation, rate limiting, sandboxing, memory management, and Human-in-the-Loop approval for high-impact actions.
• Model risk management: Partner with Model Validation/Analytics teams to define validation expectations (performance, robustness, bias/fairness, explainability, drift) and ensure independent review where required.
• Security and privacy alignment: Coordinate with Security and Privacy teams on data classification, access control, encryption, secrets management, secure SDLC, privacy-by-design for AI solutions.
• Regulatory and policy alignment: Maintain alignment with relevant standards and regulations (as applicable) and ensure internal AI policies and procedures are current and auditable.
• Third-party and vendor risk: Conduct due diligence on AI vendors (foundation models, platforms, data providers) including security posture, data usage terms, IP considerations, and model transparency/documentation.
• Incident response and issue management: Oversee triage, containment, communications, and lessons learned.
• Monitoring and KRIs: Define and track AI risk metrics (e.g., safety events, policy violations, drift, override rates, hallucination indicators, agent action errors) and report insights to governance forums.
• Training and enablement: Deliver risk guidance and training to product and engineering teams; promote responsible AI practices and documentation discipline.
• Audit readiness: Ensure evidence collection, control testing support, and documentation standards to satisfy internal audit, regulators, and customer due diligence inquiries.
• Perform formal assessments of technology risks using common processes within Risk Management, including Targeted Reviews, Concurrent Reviews and Continuous Monitoring.
• Attend and represent 2LOD at multiple Technology Strategic planning sessions including but not limited to: Responsible AI Working Group.
• Manage risks and issues within the Synchrony’s enterprise governance application (eGRC).
• Perform other duties and/or special projects as assigned.
• Support 2nd Line of Defense processes such as the Enterprise Risk Assessment (ERA) and Risk and Control Self-Assessment (RCSA) processes.

Qualifications/Requirements

• Bachelor’s degree in Risk Management, Information Systems, Computer Science, Data Science, Engineering, Mathematics/Statistics, Cybersecurity, or a related field (or equivalent practical experience).
• 6+ years in technology risk, model risk management, information security risk, compliance, operational risk, or assurance roles in a regulated bank or financial institution.
• 1+ years supporting AI/ML or data-driven systems (or demonstrably equivalent exposure) in a regulated bank or financial institution.
• Working knowledge of supervised/unsupervised learning, evaluation metrics, overfitting, data leakage, bias/fairness concepts, drift, and monitoring.
• Familiarity with AI agents that plan and use tools (APIs), including risks introduced by autonomy (goal misalignment, unsafe actions, cascading failures) and control patterns (scoped tools, approvals, sandboxing).
• Demonstrated experience creating risk assessments, control frameworks, KRIs/KPIs, and remediation plans; ability to articulate risk in business terms and quantify impact/likelihood where possible.
• Strong capability to produce clear governance documentation (policies, standards, procedures, risk assessments, controls) and present to senior stakeholders.
• Evidence of ongoing learning in AI risk, security, privacy, and compliance (courses, workshops, internal enablement, or certifications).
• Ability and flexibility to travel for business as required.