Published Paper Details:

The necessity for comprehensive and scalable security solutions has increased as software development increasingly uses CI/CD pipelines. Traditional security practises sometimes fall behind rapid development cycles, leaving weaknesses for unscrupulous actors to exploit. Adding AI-based security mechanisms to CI/CD pipelines can enable proactive threat detection, automated vulnerability assessments, and continuous monitoring without slowing down development. This study discusses AI-based security in CI/CD pipelines and its advantages. This article examines the constraints of typical security techniques in CI/CD settings, where quick deployment frequently clashes with extensive security testing. AI-based security technologies use machine learning algorithms to evaluate massive volumes of data, detect risks in real time, and react to new attack vectors faster than previous techniques. Automation of security testing across the development lifecycle is a major benefit of AI-based security in CI/CD pipelines. By combining AI-driven static and dynamic code analysis, anomaly detection, and automated penetration testing, enterprises can make security a priority throughout development. Traditional security systems have high false positive rates, but these technologies can learn from fresh data and improve accuracy. By detecting and responding to threats in real time, AI-based security helps improve CI/CD pipeline responsiveness. These solutions utilize AI models based on historical data to detect vulnerabilities and automatically roll back deployments or apply security updates. This preemptive technique limits attackers' window of opportunity and the danger of delivering tainted code into production. AI-based security in CI/CD pipelines ensures continuous monitoring and audit trails, supporting industry norms and laws. AI-driven solutions can log security actions, provide compliance reports, and notify teams of policy violations. This is useful in highly regulated businesses where security criteria must be met. However, CI/CD pipelines with AI-based security face issues. AI model complexity, training dataset size, and adversarial assaults on AI systems must be handled. Integration also demands development teams to choose security above speed and creativity. In conclusion, AI-based security transforms CI/CD pipeline protection in fast-paced software development. AI can improve security while preserving agility by automating security procedures, allowing real-time threat detection, and supporting compliance.