Agentic AI for Proactive Software Supply Chain Security
Analysis
This paper addresses the critical and growing problem of software supply chain attacks by proposing an agentic AI system. It moves beyond traditional provenance and traceability by actively identifying and mitigating vulnerabilities during software production. The use of LLMs, RL, and multi-agent coordination, coupled with real-world CI/CD integration and blockchain-based auditing, suggests a novel and potentially effective approach to proactive security. The experimental validation against various attack types and comparison with baselines further strengthens the paper's significance.
Key Takeaways
- •Proposes an agentic AI framework for proactive software supply chain security.
- •Combines LLMs, RL, and multi-agent coordination for vulnerability mitigation.
- •Integrates with real-world CI/CD environments (GitHub Actions, Jenkins).
- •Employs blockchain for integrity and auditing.
- •Demonstrates improved performance compared to baseline approaches.
“Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines.”