Adaptive Application Security Testing with AI Automation

Abstract

Conventional security testing methods can fall short in the fast changing threat landscape of the present day in terms of their fluid properties of modern apps. Adaptive Application Security Testing (AAST), a dynamic approach that changes testing strategies in actual time based on their application activity, user behaviors & newly found vulnerabilities, is investigated in this article. Aiming at increasing flexibility by means of the integration of ML algorithms that constantly learn from their security events, code changes & user interactions, the study offers an AI-based automated system. In reaction to contextual indicator such as the latest feature deployments or aberrant behavior this adaptive solution begins security tests, therefore making testing more flexible & more efficient than static or scheduled testing. By combining dynamic application security testing (DAST), static application security testing (SAST), & actual time behavioral analysis, the AI framework helps to identify their improved vulnerabilities, hence reducing faulty positives & human employment. Emphasizing increases in detection rate, response time & more general system resilience, a case study of a banking application shows the ability of the model to reveal their complex security vulnerabilities neglected by conventional methodologies. Important findings highlight how well adaptive testing may improve their security protocols by matching testing activities with actual world usage patterns, hence streamlining development processes. The consequences for the sector are more significant, pointing from irregular, reactive testing to continuous, intelligent security validation included into the DevSecOps process. This change helps companies to reduce their remedial costs, proactively protect against the latest vulnerabilities, and speed up safe software deployment