IdentityAI.
SailPoint IdentityAI embeds machine learning into identity security—surfacing risky and anomalous access so enterprises can hold least-privilege at scale. I built the interfaces and data visualizations that turned that model output into something a security team could read, trust, and act on.
Least privilege, when there are too many identities to reason about by hand.
Cloud migration, work-from-anywhere, and app sprawl exploded the number of identities and access points enterprises have to govern—well past what human-centric review can keep up with. IdentityAI applies machine learning (and, later, generative AI) to that firehose: access insights from attributes, roles, history and entitlements; access recommendations from peer-group analysis; and role modeling that keeps access right as the org changes. The hard product problem underneath it is trust—a recommendation nobody understands is a recommendation nobody follows.
Built the interfaces that let customers interpret machine-learning-driven insights across identity data, entitlements, access history, and user activity—turning model output into decisions a human could actually make.
Developed dashboards and interactive visualizations for anomalous access behavior, risk scoring, and contextual identity-governance insights.
Created dynamic D3-based charts to represent suspicious access patterns and near-real-time anomaly-detection signals—the kind of viz a security team lives in.
Designed and implemented accessible UI components that integrated with the backend identity-analytics systems.
Contributed to a reusable design system that improved UI consistency, accessibility, and development velocity across product surfaces.
// This is where identity and I first got properly entangled—the start of nearly six years in the space.
// Figures published by SailPoint for IdentityAI (2024). My contribution was the frontend and visualization layer during its early days (2017–18)—not these later customer outcomes.