The DevSecOps environment is evolving rapidly—it's no longer a matter of integrating security scans into legacy CI/CD pipelines. Instead, we're headed toward an intelligent, adaptive workflows future powered by AI. In this session, we explore how integrating machine learning models into DevSecOps practices makes software delivery more intelligent and more secure.

We’ll dive into practical examples of AI-enhanced pipelines that go beyond basic automation. These advanced workflows are capable of proactively identifying vulnerabilities, detecting anomalies in real-time, and streamlining incident response. By embedding machine learning into popular CI/CD tools like GitLab and Tekton, teams can implement predictive testing and dynamic compliance validation that evolves with the system, improving both security and efficiency.

Attendees will discover real-world use cases and strategies drawn from large-scale enterprise environments, where AI has significantly boosted security posture without slowing down development. We’ll demonstrate how to set up intelligent pipelines that not only react to issues but anticipate them, enabling a more resilient and agile approach to DevSecOps.

If you're interested in scaling secure deployment, gaining more visibility, or eliminating manual overhead, this session presents a view of the future of how AI can be used as a force multiplier. This is ideal for DevOps, security teams, and architects that want to upscale their toolchain and stay in front of a more complex threat landscape.