Elisity

Microsegmentation Without the Pain

Enterprise networks generate enormous volumes of traffic metadata — but raw flows and IP addresses don't answer the question that matters for Zero Trust: who is accessing what? Traditional microsegmentation requires agent deployment across every endpoint and extensive network redesign, creating friction that slows adoption and leaves gaps in coverage.

The challenge: enforce granular, identity-centric access controls at scale — without touching endpoints, without redesigning the network, and without creating a management burden that collapses under its own complexity.

IdentityGraph™: From Metadata to Identity

As Engineering Product Owner, I led the technical roadmap for Elisity IdentityGraph™ — a system that transforms raw network metadata into multi-dimensional identity profiles. Rather than asking "what IP is this?", IdentityGraph asks "who is this device, what role does it serve, and what should it be allowed to reach?"

• Roadmap decomposition: Broke the agentless microsegmentation problem into discrete, high-impact engineering sprints — prioritizing the highest-risk lateral movement paths first.
• Integration architecture: Prioritized deep integrations with Active Directory, CrowdStrike, and Armis, turning Elisity into the "central nervous system" for network-wide least-privilege access.
• Bridge building: Translated complex network security requirements from customers and field teams into precise engineering specifications — maintaining alignment between business outcomes and technical execution.

Zero Trust Without Agent Friction

A production platform that enforces identity-aware microsegmentation across enterprise networks — no agents, no network redesign, no operational paralysis. The Elisity platform integrates across the enterprise security stack, giving security teams a single, authoritative view of identity-to-network relationships and the policy engine to act on it.

The product roadmap continues to expand the IdentityGraph™ model — moving from enforcement to prediction, using identity patterns to surface anomalous access before it becomes a breach.