AI Enablement & Governance - AI Security & Controls Lead

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Great Place to Work Thanks to the work of every colleague, Alight has received multiple awards of recognition including “Great Place to Work” for the past 7 years and Fortune’s “Best Companies to Work For.” To learn more about our company culture and awards .We invite you to join our team!  Learn more at  The AI Enablement & Governance – Security & Controls Lead enables secure, responsible, and scalable AI adoption by defining, implementing, and evaluating AI‑specific security and risk controls across the AI lifecycle.

This role serves as a bridge between AI engineering, information security, privacy, and third‑party risk teams, ensuring that incremental AI risks introduced by models, training data, RAG architectures, and autonomous or semi‑autonomous agents are appropriately controlled by design.

The role partners closely with AI Engineering, Third‑Party Supplier Governance, Information Security, Privacy, and Risk teams to identify AI‑specific control gaps, define practical control requirements, support secure implementation, and evaluate effectiveness. The focus is on AI‑specific security concerns—not replacing existing security programs, but extending them thoughtfully for AI.

Key Responsibilities AI Security, Policy, Standards & Guidance Partnering directly with AI Engineers & Developers, Information Security and governance teams to define AI-specific security and risk management standards covering AI/ML models, RAG solutions, and agentic architectures.

Translating enterprise security principles and risk frameworks into AI‑appropriate guidance, addressing topics such as, model access control and abuse prevention, prompt and context security, data leakage, memorization, and inference risks, agent autonomy boundaries and safeguards

Define AI runtime monitoring and incident response expectations, aligned to (and extending as needed) existing incident response playbooks.

Ensuring AI security guidance remains aligned with evolving technology patterns, external expectations, and internal architectures, and external expectations (e.g. NIST AI RMF/CSF, NYDFS AI Cybersecurity, ISO/IEC 42001)
Contributing to the broader AI policy hierarchy by ensuring security requirements are clearly mapped to AI governance policies, controls and standards.

Third‑Party AI & Model Risk Support Partnering with third‑party risk and supplier governance teams to Identify AI‑specific risks introduced by vendors, models, platforms, and APIs. Defining AI security control expectations for vendors and managed services

Supporting evaluation of vendor AI security posture,…