Proactive Cybersecurity Methodology: An AI-Assisted Framework for Continuous Source-Code Vulnerability Analysis and Remediation

Kateryna Kuznetsova

Citation: Kateryna Kuznetsova, "Proactive Cybersecurity Methodology: An AI-Assisted Framework for Continuous Source-Code Vulnerability Analysis and Remediation", Universal Library of Innovative Research and Studies, Volume 01, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Amid the exponential acceleration and increasing complexity of software development, orthodox Application Security practices demonstrably fail to keep pace. The divergence between the velocity of continuous integration and deployment (CI/CD) and the inertia of manual security reviews accrues security technical debt and escalates the risk of compromise. This work presents a comprehensive, reproducible methodology for designing and deploying an AI-assisted framework intended to automate the full vulnerability-management lifecycle in source code. The proposed framework’s architecture comprises four pivotal modules: a continuous scanning module; a large-language-model (LLM)–based analysis and prioritization module; a patch (fix) generation module; and a proactive validation module. A step-by-step implementation protocol, a metric system for evaluating efficacy, and a risk analysis for the application of generative artificial intelligence were delineated. The scientific contribution lies in a systematic approach to self-healing code, in which AI evolves from an assistant proposing candidate remedies to an autonomous agent capable of performing the entire security cycle, detection through integration of remediations. This article targets DevSecOps engineers, security architects, lead developers, and technical managers responsible for embedding automated vulnerability management into CI/CD pipelines and for adopting LLM solutions for automatic patch generation and validation.


Keywords: AI-Assisted Security, CI/CD Integration, LLM, Automatic Patch Generation, Proactive Validation, Shift-Left Security.

Download doi https://doi.org/10.70315/uloap.ulirs.2024.0102010