A Neo4j-based knowledge graph and conversational AI layer that gives enterprises real-time visibility into users, roles, permissions, and access risk.
The client is an enterprise identity and access governance function looking to replace fragmented, manual oversight with a connected, AI-powered view of who has access to what, and why.
MoreYeahs built EKG (Enterprise Knowledge Graph), an AI-powered Identity Governance and Access Management platform that connects identity data from multiple enterprise systems into a single graph, giving the organization visibility into users, roles, permissions, and the business processes they touch.
Identity and access data lived in fragmented systems, which made detecting Segregation-of-Duties (SoD) violations a manual, time-consuming, and largely reactive exercise.
Fragmented Identity Data — User, role, and permission data was scattered across multiple systems with no unified view.
Manual SoD Detection — Segregation-of-Duties violations were caught through manual review rather than continuous monitoring.
Limited Contextual Insight — Traditional governance tools offered little context on how access related to actual business processes, and were difficult for non-technical stakeholders to use.
MoreYeahs built EKG on Neo4j, centralizing identity and access data from sources including Microsoft Entra ID and SAP S/4HANA into a connected knowledge graph, and adding a conversational AI layer on top so governance teams could query access and risk insights in natural language.
Connected Identity Graph — Neo4j-based graph modeling unified user, role, permission, and business-process relationships across systems.
Automated SoD & Role Analysis — The platform continuously detects Segregation-of-Duties conflicts and analyzes role and permission overlap rather than relying on periodic manual audits.
Access Lineage & What-If Simulation — Analysts can trace how access was granted over time and simulate the effect of proposed role or permission changes before making them.
Conversational Governance Assistant — An LLM-powered assistant answers natural-language questions about access and risk and surfaces graph visualizations directly.
The platform combines graph database technology, enterprise identity systems, and conversational AI.
By replacing manual review with a connected, queryable graph of identity and access data, the platform shifted SoD and access-risk detection from a reactive, after-the-fact exercise to a proactive one.
Proactive Risk Detection — Access risk and SoD conflicts are surfaced automatically rather than discovered during periodic audits.
Faster Audit Readiness — Centralized, connected access data improved audit and compliance readiness.
Accessible Governance — Conversational access to graph insights let non-technical stakeholders query risk data directly, without needing to write graph queries themselves.
The engagement reframed identity governance from a compliance checkbox into a proactive, AI-assisted discipline the organization could rely on day to day.
From Reactive to Proactive — Continuous graph-based monitoring replaced point-in-time manual audits.
Stronger Security Posture — Connected visibility into access relationships reduced blind spots that fragmented systems had created.
A Reusable Governance Foundation — The knowledge graph and conversational layer give the organization a foundation that can extend to new identity sources as the enterprise grows.
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