Graph-Based Identity Security Agent
This challenge focuses on building an intelligent agent system capable of analyzing complex user, system, and access relationships to identify security risks. You will construct a knowledge graph representing an enterprise's identity landscape, then leverage Gemini 3 for advanced reasoning over this graph. Your primary task is to develop a graph-based agent workflow using LangGraph to traverse and query this knowledge graph. The agent will integrate with LlamaIndex for Retrieval-Augmented Generation (RAG) to fetch relevant context from the graph, and utilize MCP-enabled tools to interact with simulated enterprise identity systems (e.g., Active Directory, HR databases). The agent should be able to perform extended thinking with adaptive reasoning budgets to detect anomalous access patterns, privilege escalations, and provide risk assessments.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a graph-based agent using LangGraph, Gemini 3, and LlamaIndex RAG for identity security analysis, integrating MCP tools.
How work is evaluated
The agent will be evaluated on its ability to accurately identify pre-defined identity-related security risks within a given knowledge graph, provide clear explanations for the risks, and suggest appropriate mitigation strategies. Evaluation will also cover the efficiency of its graph traversal and reasoning process.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Develop Model Context Protocol (Model Context Protocol)-enabled tools for interacting with simulated identity systems.
Implement extended thinking with adaptive reasoning budgets for complex risk assessments.
Construct an enterprise identity knowledge graph using LlamaIndex.
Integrate Gemini 2.5 Pro for advanced reasoning and anomaly detection over the knowledge graph.
Design and implement a graph-based agent workflow with LangGraph for identity risk analysis.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The agent will be evaluated on its ability to accurately identify pre-defined identity-related security risks within a given knowledge graph, provide clear explanations for the risks, and suggest appropriate mitigatio...
Challenge input
{'knowledge_graph_data': {'nodes': [{'id': 'user_alice', 'type': 'user', 'roles': ['admin']}, {'id': 'server_db', 'type': 'system', 'security_level...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'identified_risks': [{'risk_type': 'DormantAdmin', 'description': 'User Bob has admin privileges but no recent activity.', 'mitigation': 'Revoke p...
- At least 80% of actual hidden risks in the input graph are correctly identified.
- MitigationQuality target: 85
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- At least 80% of actual hidden risks in the input graph are correctly identified.
Metrics
- MitigationQuality · Target 85
- ReasoningDepth · Target 8
Workflow tasks
- Task 1
Input: {'knowledge_graph_data': {'nodes': [{'id': 'user_alice', 'type': 'user', 'roles': ['admin']}, {'id': 'server_db', 'type': 'system', 'security_level': 'confidential'}], 'edges': [{'source': 'user_alice', 'target': 'server_db', 'relation': 'accesses'}], 'query': 'Identify dormant high-privilege accounts.'}}
Output: {'identified_risks': [{'risk_type': 'DormantAdmin', 'description': 'User Bob has admin privileges but no recent activity.', 'mitigation': 'Revoke privileges or disable account'}], 'reasoning_path': ['...']}
Execution harness: Python
Recipe freeze marker
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