Ojasveer955/Agentic-Cloud-Security-and-Remediation-Framework

The project represents a modern 'Agentic' approach to Cloud Security Posture Management (CSPM), combining graph-based attack path analysis (Neo4j) with LLM-driven reasoning (LangGraph). While the technical approach is sound and reflects current industry trends toward 'autonomous SOCs,' the project currently has minimal traction (1 star, 0 forks) and functions as a personal prototype. From a competitive standpoint, it faces existential threats from two directions: established CNAPP vendors like Wiz and Orca, who already offer sophisticated attack path visualization, and AWS itself, which is aggressively integrating AI (Amazon Q) into its security services. The defensibility is low because the core logic—mapping AWS resources to a graph and querying an LLM for remediation—is a pattern that is becoming standard in cybersecurity engineering. Without a proprietary dataset of exploit chains or a massive library of verified remediation playbooks, it remains a reproducible reference implementation rather than a defensible product.