Designation : AI Governance Lead – Enterprise AI Governance, Risk & Responsible AI

Position Summary

The AI Governance Lead will be responsible for defining and implementing the enterprise AI governance framework, ensuring that all AI initiatives comply with regulatory requirements, internal policies, security standards, and ethical AI principles.

The role requires close collaboration with Digital, Data, Risk, Compliance, Information Security, Legal, Internal Audit, Enterprise Architecture, and Business teams to establish governance processes across the entire AI lifecycle—from use case approval and model development to deployment, monitoring, and retirement.

This is a strategic leadership role suited for professionals with expertise in AI governance, model risk management, enterprise risk, regulatory compliance, and data governance within Banking, NBFC, Financial Services, or regulated industries.

Key Responsibilities Enterprise AI Governance Strategy

- Develop and implement the Enterprise AI Governance Framework.
- Define AI governance policies, standards, and operating procedures.
- Establish governance across the complete AI lifecycle.
- Develop AI approval and review processes.
- Define governance metrics and reporting mechanisms.
- Establish enterprise-wide Responsible AI principles.
- Build an AI Governance Centre of Excellence.

AI Risk Management

Develop and implement governance controls for:

- AI model risk assessment
- Operational risk
- Regulatory risk
- Reputational risk
- Technology risk
- Third-party AI risk
- Data privacy risk
- Cybersecurity risk
- Model drift and performance degradation
- Hallucination and misinformation risks in Generative AI

Conduct periodic AI risk reviews and recommend mitigation strategies.

AI Policy & Standards

Define enterprise standards for:

- AI development
- Model validation
- Model approval
- AI deployment
- Prompt governance
- LLM usage
- Data quality
- Model monitoring
- AI documentation
- AI change management
- Model retirement

Develop standard operating procedures for all AI initiatives.

Responsible AI

Lead implementation of Responsible AI practices including:

- Fairness
- Explainability
- Transparency
- Accountability
- Human oversight
- Bias identification and mitigation
- Ethical AI decision-making
- AI usage guidelines
- Responsible GenAI adoption

Establish processes for independent validation and periodic review of AI models.

Regulatory Compliance

Ensure AI initiatives comply with:

- RBI regulations and supervisory expectations




- Digital Personal Data Protection (DPDP) Act requirements
- Internal Risk and Compliance policies
- Information Security standards
- Enterprise Architecture standards
- Data Governance policies
- Vendor risk management requirements

Track emerging AI regulations and advise leadership on governance implications.

AI Model Governance

Establish governance for:

- Model inventory and registry
- Model documentation
- Model approval workflows
- Validation processes
- Version management
- Performance monitoring
- Explainability reports
- Periodic model review
- Model retirement

Ensure all production AI models have appropriate approvals, documentation, and monitoring.

Generative AI Governance

Develop governance specifically for:

- Large Language Models (LLMs)
- Prompt Engineering
- Prompt Libraries
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Multi-Agent Systems
- Enterprise Knowledge Assistants
- AI Copilots

Define controls for:

- Prompt security
- Data leakage prevention
- Hallucination management
- Output validation
- Human-in-the-loop approvals
- Sensitive data handling
- AI content review

AI Audit & Assurance

- Partner with Internal Audit to establish AI audit methodologies.
- Prepare AI systems for internal and external audits.
- Maintain audit-ready documentation.
- Ensure compliance with governance standards.
- Track audit findings and drive closure of observations.

AI Governance Committees

Establish and manage:

- AI Steering Committee
- AI Governance Review Board
- Model Approval Committee
- AI Ethics Committee

Prepare governance dashboards and executive reports for senior leadership.

Vendor Governance

Evaluate and govern AI vendors by:

- Reviewing AI architecture and security practices
- Conducting due diligence
- Assessing regulatory compliance
- Monitoring vendor performance
- Managing contractual AI obligations
- Ensuring secure use of third-party AI platforms

AI Awareness & Training

- Develop enterprise AI governance awareness programs.
- Train business and technology teams on Responsible AI.
- Publish governance guidelines and best practices.
- Promote ethical AI usage across the organization.

Stakeholder Management





Work closely with:

- Chief Risk Officer
- Chief Compliance Officer
- Chief Information Security Officer
- Internal Audit
- Legal
- Enterprise Architecture
- Data Governance Team
- AI Engineering Team
- Data Scientists
- Business Heads
- External Regulators
- Technology Partners

Educational Qualifications Mandatory

- Bachelor's degree in Engineering, Computer Science, Information Technology, Data Science, Law, Risk Management, or related discipline.

Preferred

- Master's degree in Technology, Artificial Intelligence, Cyber Security, Risk Management, Business Administration, or related discipline.

Preferred Certifications

- ISO 42001 Lead Implementer/Auditor (AI Management Systems)
- Microsoft Responsible AI
- Certified Information Systems Security Professional (CISSP)
- Certified Information Security Manager (CISM)
- Certified Risk Management Professional (CRMP)
- COBIT
- TOGAF
- ISO 27001 Lead Auditor
- NIST AI Risk Management Framework training
- AI Governance or Responsible AI certifications

Experience

- 12–16 years of experience in Technology Governance, Risk, Compliance, Information Security, Data Governance, Model Risk Management, or AI Governance.
- Minimum 5 years in governance roles within Banking, NBFC, Financial Services, Insurance, or other regulated industries.
- Experience implementing governance frameworks for AI, analytics, or enterprise technology platforms.
- Experience working with senior leadership, regulators, auditors, and cross-functional teams.

Required Technical Knowledge AI & Machine Learning

Strong understanding of:

- Artificial Intelligence
- Machine Learning
- Generative AI
- Large Language Models
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Machine Learning lifecycle
- Model monitoring
- Prompt engineering concepts
- Explainable AI

Governance & Risk

Expertise in:

- Enterprise Risk Management
- Model Risk Management
- AI Governance Frameworks
- Data Governance
- Regulatory Compliance
- Policy Development
- Internal Controls
- Audit Management
- Third-Party Risk
- Operational Risk

Regulatory Knowledge

Valuable understanding of:

- RBI regulatory expectations
- DPDP Act
- Information Security standards
- Data privacy regulations
- Cybersecurity frameworks
- Enterprise Governance Frameworks

Enterprise Technologies

Understanding of:

- Cloud Computing
- APIs
- Data Lakes
- CRM Platforms
- Digital Lending Platforms
- Enterprise Data Platforms
- Identity & Access Management
- Security Architecture

📌 Chief Manager - AI Governance Lead (India)
🏢 Antal International
📍 India