• Work directly with operational units to understand their challenges, constraints, and needs within their operating context before implementing control requirements.
• Apply systems thinking to understand how controls interact across processes, platforms, and teams, moving from “this is the control requirement” to “this is how it should operate in practice.”
• Design and embed GRC practices into system and process development from inception, ensuring governance mechanisms feel natural within product and engineering workflows rather than burdensome.
• Translate GRC requirements into practical, actionable solutions with stakeholder experience as a primary consideration, ensuring appropriate governance is in place without hindering operational velocity.
• Analyse delivery workflows, decision latency, and control friction across digital products and platforms to identify systemic risk, operational complexity, and value leakage.
• Architect reusable control patterns and implement real-time risk and control telemetry using data pipelines, APIs, and analytics platforms to enable continuous automated monitoring and assurance-by-design.
• Replace retrospective compliance validation with embedded, continuous assurance mechanisms that demonstrate measurable risk outcomes.
• Implement governance for AI-enabled systems, including model lifecycle controls, decision accountability, data provenance, and human-in-the-loop assurance.
• Champion the cultural shift toward proactive GRC engineering. Manage key stakeholder relationships across product engineering teams, legacy policy units, and executive leadership to align governance initiatives with broader product roadmaps.