Keywords
CI/CD Security
Explainable AI
Federated Learning
Adversarial Attacks
Compliance
How to Cite
Abstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines have revolutionized software delivery by enabling rapid iterations and reducing release cycles. However, this acceleration of deployment also magnifies risks: insecure code can propagate more quickly, compromised dependencies can infiltrate multiple environments, and automated release processes broaden the attack surface. Traditional, rule-based defenses struggle in such dynamic environments.
This paper introduces an AI-Enhanced DevSecOps framework that strengthens pipeline security through the integration of Explainable AI (XAI), Federated Learning (FL), adversarially resilient malware detection, and AI-augmented defenses against social engineering. By combining these techniques with DevSecOps principles, organizations can achieve both resilience and compliance with regulations such as the GDPR, NIST SP 800-53, and the EU’s Digital Operational Resilience Act (DORA). Real-world case studies, including SolarWinds, Mirai Botnet, and phishing simulation research, demonstrate the necessity and practical benefits of such integration. Results highlight measurable improvements in detection accuracy, mean time to resolution, and overall trust in automated security decisions.
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