Description
You will own Cyber Architecture and Solutions's relationship with GSK's business functions, R&D and the Enterprise AI programme , the bridge between the technical AI function and the teams that use and build AI.
You lead AI use-case intake and triage, assess proposals against security and architecture requirements, and route feasible cases into the Cyber Architecture and Solutions delivery pipeline.
The AI ML and Cloud Security team designs and builds the agentic framework, a multi-agent platform moving security services across the Chief Security Office from rule-based to AI-driven operation.
Key Responsibilities:
- Own Cyber Architecture and Solutions's relationship with GSK business functions, R&D and the Enterprise AI programme
- Lead AI use-case intake and triage: assess GenAI, RAG and agentic proposals against AI architecture standards and route the feasible ones into delivery
- Classify use cases for EU AI Act exposure and apply the NIST AI RMF risk framing
- Own the intake decision , proceed, redesign or refuse , on every AI use case GSK's business units and R&D bring to the function
- Represent the Cyber Architecture and Solutions security position in GSK's AI governance forums and in Responsible AI decision-making
- Run business-side shadow AI discovery and bring unsanctioned tools into governance by making the governed path faster
- Hold the security position with R&D and business leadership when a funded AI programme cannot proceed as designed
Basic Qualifications:
- Degree in a relevant discipline
- Significant experience in cyber security, AI governance or technology risk, including leading teams
- Experience in AI Business Facing, owning the business-facing relationship for an enterprise AI programme
- Leading AI use-case intake and triage , assessing business and R&D AI proposals against security and architecture requirements and routing feasible use cases into delivery
Preferred Qualifications:
- EU AI Act, NIST AI RMF and ISO/IEC 42001 awareness in a regulated setting
- AI risk assessment and risk-based prioritisation of use cases , and automation of governance checks
- Experience telling an R&D leadership team that a funded AI programme cannot proceed as designed
- Regulated industry experience, ideally pharmaceutical (GxP)
- AI governance or privacy certification such as IAPP AIGP or CIPP/E