Identity Security Copilot With Knowledge Graph Query Mediation
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Solution Overview
Problem
Identity governance and administration systems face challenges in managing complex, multi-faceted identity data, requiring technical expertise to access and interpret data for effective decision-making, and lack user-friendly interfaces for non-technical users.
Innovation Solution
A knowledge graph-enhanced AI copilot platform that converts natural language queries into graph-compatible queries using Retrieval-Augmented Generation and Large Language Models, providing intelligent insights and allowing users to visualize and manage identity security data through customizable dashboards and reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigid web interfaces and query languages are used to access identity data, then data access control is maintained, but user accessibility and ease of operation deteriorate for non-technical users
Solution Approach 1:
The patent introduces an AI copilot as an intermediary layer between users and the complex identity governance data. The copilot accepts natural language queries from non-technical users, translates them into appropriate graph queries against the identity knowledge graph, and returns simplified results. This mediator eliminates the need for users to directly interact with rigid web interfaces or learn query languages, while maintaining controlled data access through the underlying graph query mechanism.
2Measurement precision
If complex query languages are required to access identity data, then data retrieval precision is maintained, but user operation difficulty increases
Solution Approach 1:
The patent replaces the mechanical system of manual query language construction with an AI-based natural language processing system. The AI copilot uses large language models to understand user intent, convert natural language questions into precise graph queries, and retrieve accurate data from the identity knowledge graph. This substitution maintains data retrieval precision while eliminating the operational complexity of learning and writing query languages.
3Ease of operation
If AI copilot with natural language processing is implemented, then user accessibility is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the AI copilot system into distinct functional modules: a natural language processing component that receives and interprets user queries, a query translation component that converts natural language into graph queries, and a result presentation component that formats and returns results. This segmentation allows each module to be optimized independently and facilitates maintenance and scaling, reducing the practical complexity despite the advanced capabilities provided.
Data Source
AI summary
A copilot platform for identity security governance and lifecycle management, used for capturing the complexity and relatedness of identity security data. The copilot platform integrates Knowledge Graphs and Large Language Model to enhance data exploration and understanding. The LLM converts natural language queries into Cypher queries, enabling interaction with graph databases. The copilot platform includes query annotation to facilitate LLM for recognized entities and for enduring necessary correctness to those entities if required and that increases overall accuracy of the Copilot. The LLMs and data metrics are used to summarize the data for the end user. The copilot platform uses an AI system for interacting with a user to learn about the state of user identity security, take action when required and, given the complexity of IGA data, including information on differentiated dashboards and custom reports, for allowing the user to visualize and manage the information effectively.