The core issue is the AI-SAST tool's lack of context, leading to low-value alerts (false positives). The most effective solution is to enhance the AI's capability rather than reverting to manual or less intelligent methods. Implementing context-aware vulnerability prioritization uses more sophisticated machine learning models that can analyze not just the code itself, but also its surrounding environment. This includes factors like the deployment configuration (e.g., container settings), data flow analysis, and whether a vulnerable function is actually reachable in production. This approach directly addresses the problem by making the AI smarter, allowing it to prioritize genuinely critical risks and suppress irrelevant findings.

A. Increase the sensitivity threshold of the AI-SAST engine: This would likely generate even more alerts, exacerbating the problem of alert fatigue and false positives, not reducing it.

B. Transition to manual code review for all deprecated libraries: This is highly inefficient, costly, and negates the primary benefit of using an automated DevSecOps tool for rapid scanning.

C. Utilize standard Regex-based patterns to filter out library alerts: This is a regression to older, less effective rule-based methods that lack the sophistication to understand context, which is the root of the problem.

1. Ghaffarian, S. M., & Shahriari, H. R. (2017). Software vulnerability analysis and discovery using machine learning and data mining techniques: A survey. ACM Computing Surveys (CSUR), 50(4), 1-36. Section 5 discusses the future direction of using ML for "vulnerability prioritization" based on contextual factors. https://doi.org/10.1145/3092566

2. National Institute of Standards and Technology (NIST). (2021). Defending Against Software Supply Chain Attacks. (NIST SP 800-218). This document emphasizes the need for automated tools to analyze software components (like libraries) and assess risk, a process that is optimized by context-aware AI.

3. Carnegie Mellon University, Software Engineering Institute (SEI). (2022). Using AI and ML for Application Security. Blog. The SEI discusses how AI/ML is advancing beyond simple pattern matching in SAST to include contextual understanding to improve the accuracy and prioritization of findings.