Head – AI Governance

Job Purpose :-

 

To establish and operate an enterprisewide AI Governance framework that ensures Artificial Intelligence systems across BGIL are responsibly adopted, riskclassified, approved, monitored, and auditable, in alignment with Group practices, the Company’s risk appetite, regulatory obligations, and governance standards, while enabling effective senior management and Board oversight of AIrelated risks.

 

 PRINCIPAL ACCOUNTABILITIES

 

1. AI Governance Framework & Lifecycle Oversight

• Establish, maintain, and continuously enhance the AI Governance framework covering the full AI lifecycle, including usecase identification, approval, deployment, monitoring, change management, suspension, and decommissioning.
• Conduct pre-deployment AI risk assessment and define model documentation standards
• Ensure a comprehensive enterprise inventory of AI systems, including machine learning, generative AI, autonomous systems, orchestration layers, hybrid decision engines, and AI embedded in thirdparty products.
• Work in close coordination with Business Heads, Head – IT, Head – Data Governance, Business Functions, DPO, Legal, and Compliance to ensure governance requirements are embedded consistently across AI initiatives.

2. AI Risk Classification, Approvals & Escalation

• Define and operate a riskbased AI classification framework (Low / Medium / High) based on customer impact, regulatory sensitivity, degree of autonomy, and materiality.
• Define prohibited/restricted AI use cases. Also incorporate human in the loop Vs autonomous decision classification.
• Establish approval thresholds, governance signoffs, and escalation triggers for AI use cases, including formal decision rights for suspension or emergency stop of highrisk AI.
• Escalate material AI risks, incidents, and repeated control failures to appropriate Risk Forums, ERMC, and Board Committees.

3. Governance Integration with Enterprise Risk Frameworks

• Integrate AI governance requirements into overall Enterprise Risk Management Framework, Operational Risk Management, Information & Cyber Security Framework, Data Privacy, ThirdParty Risk Management, Model / Decision Risk, and Business Continuity frameworks.
• Ensure clear RACI structures, with:
• Business owners accountable for AI outcomes
• IT, Business Process Owners, DPO, and other execution functions responsible for related controls
• AI Governance retaining oversight and assurance responsibility
• Coordinate closely with IT, Data Governance, Compliance, Legal, and Internal Audit to ensure consistency in risk reporting and governance outcomes.

*Note: AI Governance does not own or execute controls, model development or operations. It is independent from business and IT delivery functions.

4. AI Risk Monitoring & Incident Governance

• Govern enterpriselevel monitoring of AIspecific risks, including bias, drift, ethics, explainability gaps, misuse, and unintended outcomes.
• Oversee AI incident governance, ensuring structured classification, rootcause analysis, corrective action tracking, and escalation in line with policy expectations.
• Coordinate incident governance with Head Cyber Security, DPO, IT, Legal, Compliance, and Business Heads, other stakeholders including the Group Company as applicable.

5. ThirdParty & Outsourced AI Governance

• Govern risk assessment, due diligence, and lifecycle oversight of thirdparty AI models, platforms, APIs, datasets, and embedded AI solutions in collaboration with Head Cyber Security, DPO and other related stakeholders.
• Ensure AIspecific contractual governance in coordination with Procurement, Legal, IT, and Compliance, including audit rights, incident notification, change management, and exit / BCP provisions for customercritical AI.
• Assess model transparency and use of sub processors.

6. Auditability, Explainability & Regulatory Readiness

• Ensure auditability, traceability, and explainability of AIdriven decisions/purpose, particularly those impacting customers or regulated outcomes.
• Mandate independent validation for highrisk AI systems prior to production release, with documented evidence packs and governance signoff.
• Act as the central governance interface for external audits and regulatory inspections relating to AI usage.

7. Capability Building & Awareness

• Drive rolebased awareness and training on AI governance, risks, and regulatory expectations across Business, IT, Data Governance, Security, Privacy, Legal, Compliance, and Risk functions.

 

Educational Qualifications

 

Postgraduate qualification in Technology Governance, Financial Modelling/Risk Management, Law, Data Governance, Engineering, Computer Science, or Management

 

Key Skills :

 

• Professional certifications in IT Governance, Model Development & Management, Data Science, Risk Management, Information Security, Data Privacy, Technology Governance, or related areas will be an added advantage
• Seniorlevel experience in AI Governance, IT Security, Governance, Enterprise risk management, regulatory compliance, or technology risk, preferably within a regulated financial services environment.
• Strong understanding of AI / GenAI risks, governance frameworks, explainability, and regulatory expectations
• Experience in testing model and systems.