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AI Risk Governance Lead
Oversees enterprise-wide risk frameworks for artificial intelligence systems, ensuring AI deployments meet regulatory standards and ethical guidelines across financial services and regulated industries
Key Responsibilities:
- Lead governance initiatives around AI, large language models, machine learning, and advanced analytics within enterprise environments
- Design and perform risk assessments for AI-powered systems, ensuring risks are identified early and properly mitigated
- Develop risk policies, model governance documentation, and oversight frameworks aligned with NIST AI RMF, ISO standards, and data privacy regulations
- Serve as strategic partner to Legal, Risk, Information Security, Data Science, and Engineering teams on AI risk matters
- Promote ethical AI practices including algorithmic bias prevention, data provenance tracking, and privacy-by-design principles
- Define and report on key performance indicators and risk metrics for AI governance program effectiveness
- Build organizational AI risk awareness through training sessions and workshops for leadership and technical teams
Skills & Tools:
- AI risk mitigation frameworks (NIST AI RMF, ISO 27001/27701, IEEE AI Ethics)
- Machine learning operations (MLOps) and AI lifecycle management tools
- Regulatory compliance frameworks (GDPR, CCPA, financial services regulations)
- Risk assessment and model governance platforms
- AI explainability and fairness auditing tools
- Data governance and privacy management systems
- Enterprise risk management software
- Strong presentation and stakeholder management skills
- Executive communication and policy development abilities
Where This Role Has Appeared:
- HireNow Medical Solutions (Financial Services, New York, NY, $150k-$165k, July 2025)
Variants & Related Titles:
- AI Governance Manager
- AI Risk Officer
- AI Ethics and Compliance Lead
- Model Risk Management Lead
- AI Policy and Standards Manager
Why This Role Is New:
AI Risk Governance Lead emerged in 2023-2024 as financial institutions and regulated companies began deploying AI at scale and faced new regulatory scrutiny. The role addresses the gap between traditional enterprise risk management and the unique challenges posed by AI systems, including algorithmic bias, model explainability, and data provenance requirements that didn't exist in conventional software risk frameworks.
Trend Insight:
As regulators worldwide implement AI-specific governance requirements and high-profile AI failures make headlines, enterprises are creating dedicated leadership roles to ensure their AI investments don't become compliance liabilities or reputational risks.
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