AI-Driven Automation in Software Quality Assurance: Enhancing Security and Efficiency in U.S. Critical Infrastructure

In an era of digital transformation, ensuring the security, reliability, and efficiency of software systems in critical U.S. industries is more vital than ever. This groundbreaking research delves into how AI-driven automation is revolutionizing software quality assurance (QA), enabling predictive failure detection, self-healing test automation, and AI-augmented security testing. Through real-world case studies from leading sectors—including healthcare, finance, and government services—this study showcases how AI-powered frameworks accelerate software testing, fortify cybersecurity, and ensure compliance with stringent regulations like HIPAA and NIST cybersecurity standards. With AI reducing software failure rates by 45%, improving fraud detection in banking by 40%, and cutting government processing times by 40%, the findings highlight a paradigm shift in software validation. As AI continues to evolve, this paper also examines emerging challenges, including explainable AI (XAI), regulatory compliance, and AI’s adaptability in dynamic software ecosystems. This research is an essential read for technology leaders, policymakers, and cybersecurity professionals seeking to fortify the digital backbone of U.S. critical infrastructure through cutting-edge AI-driven QA automation.