Leveraging AI techniques for security in DevOps pipelines

In the rapidly evolving field of software development, the integration of release engineering (DevOps) practices has become crucial for efficient and reliable software delivery. However, this integration often leads to new security challenges, necessitating innovative solutions. The rise of Large Language Models (LLMs) in artificial intelligence offers a promising avenue for addressing these challenges. LLMs, known for their ability to understand and generate human-like text, can be pivotal in analyzing code, detecting vulnerabilities, and automating security protocols within the DevOps pipeline. This project is motivated by the need to enhance security measures in DevOps through advanced AI techniques, ensuring safer and more robust software development processes.

The primary goal of this research project is to develop and integrate Large Language Models into the DevOps pipeline to identify and mitigate security risks effectively. This involves creating models that can understand and analyze software code, detect potential security breaches, and suggest or implement fixes automatically. The project aims to bridge the gap between AI and software development, creating a more secure and efficient pipeline for DevOps practices. By harnessing the power of LLMs, the project seeks to automate security checks, reduce human error, and accelerate the software development lifecycle while maintaining high standards of security and compliance.