AI Governance Lead

The AI Governance Lead will own and evolve the enterprise AI governance framework— policies, standards, guardrails, and operating mechanisms —to enable responsible AI adoption at scale. This role partners closely with Security, Privacy, Compliance, Legal, Risk, Audit, Data Governance, and Technology to create clear, usable controls that accelerate delivery while containing risk . You will define and operationalize AI risk tiering , oversee governance patterns and reusable enablement assets (e.g., templates, checklists, control mappings), and ensure AI solutions align to responsible AI principles, regulatory expectations, and internal policy requirements. Key Responsibilities 1) Policy Ownership & Governance Frameworks Draft, maintain, and operationalize enterprise Responsible AI policies , standards, and procedures (e.g., scope, definitions, approvals, roles/accountabilities, documentation requirements). Translate policy into practical delivery guidance (playbooks, decision trees, “how-to” guides) that teams can adopt without slowing down product velocity. 2) AI Risk Tiering, Controls & Guardrails (Enablement with Containment) Design and continuously improve an AI risk tiering model (e.g., Tier 0–4) based on factors such as impact, autonomy, data sensitivity, regulatory exposure, and customer/member risk. Define control sets by tier (e.g., human-in-the-loop requirements, testing depth, monitoring frequency, model governance artifacts, approval gates). Establish governance patterns that teams can reuse (approved prompts/patterns, model cards, data sheets, red teaming, evaluation protocols, safe deployment architectures). Build “ fast paths ” for low-risk use cases and tighter governance for higher tiers—balancing scale and safety. 3) Cross-Functional Stakeholder Collaboration Serve as the primary convener across Security, Privacy, Compliance, Legal, Risk Management, Audit, and Data Governance to align on expectations and integrate controls. Lead working sessions to resolve ambiguity, drive decisions, and translate stakeholder needs into implementable governance requirements . Partner with platform and engineering leaders to embed governance into tools and workflows (e.g., intake forms, CI/CD gates, logging/monitoring, policy-as-code where feasible). 4) Governance Operations & Portfolio Oversight Define and run governance decisioning forums (e.g., risk review boards, architectural review checkpoints, tier adjudication). Implement a governance intake and review process for AI solutions (including evaluation of risk tier, required artifacts, and control readiness). Track and report governance KPIs: adoption of standards, compliance rates, exceptions, time-to-approval, post-deployment incidents, drift/monitoring health. 5) Responsible AI Assurance (Validation, Monitoring, Audit Readiness) Establish requirements and templates for: bias/fairness evaluation, explainability, robustness/safety testing, privacy impact assessment, and security threat modeling. Ensure production AI systems have adequate monitoring, logging, and incident response processes (including escalation paths and rollback plans). Maintain documentation and evidence to support internal/external audits , regulatory inquiries, and executive reporting. 6) Change Management & Workforce Enablement Create training and communications that drive consistent governance adoption across product, engineering, and business teams. Build communities of practice and “ governance champions ” within delivery teams to scale the operating model. Core Competencies Policy craftsmanship : clear, implementable policy writing and standards design Risk-based thinking : tiering, controls mapping, and pragmatic decisioning Influence & facilitation : ability to align diverse stakeholders and drive outcomes Operational rigor : metrics, governance cadence, audit readiness Enablement mindset : scalable patterns and “paved roads” that accelerate safe adoption #J-18808-Ljbffr The Cigna Group