The agentic structured graph traversal framework can be transferred to Cloud Security Posture Management (CSPM) to identify 'Lateral Movement' attack paths more effectively than static graph queries.

Motivation

The paper focuses on operational incidents, but the core problem—traversing a complex dependency graph to find a source—is isomorphic to identifying how an attacker moves through cloud permissions (IAM roles) and network rules. Current security tools use static graph queries (e.g., Neo4j Cypher), which lack the semantic understanding to interpret complex, context-dependent exploit chains.

Proposed Method

Adapt the paper's graph schema to represent IAM roles, resources, and trust relationships instead of service calls. Ingest a dataset like 'CloudGoat' or 'BloodHound' data. Prompt the agent to find a path from a compromised low-privilege node to a 'Crown Jewel' (admin) node. Compare the agent's discovered paths against standard shortest-path algorithms, specifically testing for the agent's ability to identify 'logical' exploits that graph algorithms miss.

Expected Contribution

Demonstration of the framework's transferability to cybersecurity, potentially creating a new class of 'Agentic Threat Hunters' that can narratively explain attack vectors.

Required Resources

Cloud security graph datasets (synthetic or sanitized enterprise data), domain expertise in IAM/Cloud Security, and the existing traversal engine codebase.

Source Paper

Agentic Structured Graph Traversal for Root Cause Analysis of Code-related Incidents in Cloud Applications

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