At a global enterprise cybersecurity platform, I am leading the product strategy for AI-enabled capabilities. The challenge is not simply integrating AI — it is doing so responsibly, at enterprise scale, within strict compliance boundaries, and in a domain where a wrong recommendation can have severe consequences.
Enterprise security teams are overwhelmed by signal volume — thousands of vulnerabilities, alerts, and misconfigurations competing for finite remediation capacity. AI offers the potential to intelligently prioritise and automate, but trust is paramount. Any AI-assisted output must be explainable, auditable, and aligned with FedRAMP and regulatory requirements.
Designed an intelligent prioritisation layer that combines CVSS scores, asset criticality, threat intelligence, and exploit likelihood into a contextualised risk score — enabling security teams to focus on what matters most rather than what is merely loudest.
Led the product design of a RAG-based knowledge system that allows security analysts to query the platform in natural language — surfacing contextualised remediation guidance, policy references, and historical resolution patterns.
Built AI-assisted remediation workflows that recommend the most effective fix path, estimate remediation effort, and surface dependencies — reducing mean time to remediate (MTTR) for common vulnerability classes.
Established an AI governance framework for the product organisation — covering data residency, model explainability, audit trails, and human-in-the-loop controls — ensuring all AI features meet FedRAMP High and enterprise procurement requirements.
AI-assisted prioritisation reduces analyst triage time by surfacing highest-impact vulnerabilities first
RAG-based query system enables natural language interaction with security data at enterprise scale
Remediation workflows reduce MTTR for high-severity vulnerabilities
AI governance framework adopted as the organisation-wide standard for all AI feature development
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