Securing AI Pipelines with DevSecOps Practices
Artificial Intelligence is changing how we build and ship software — but it's also rewriting the threat model. Large Language Models (LLMs) and GenAI systems don't just inherit the vulnerabilities of traditional applications; they introduce entirely new ones. Prompt injection, data poisoning, model extraction, and insecure plugin integrations are no longer theoretical — they're showing up in real incident reports.
The good news: we don't have to start from zero. DevSecOps already gives us a proven playbook for building security into fast-moving pipelines. The question isn't whether these principles apply to AI systems — it's how we adapt them.
Where DevSecOps and AI Security Overlap
Having worked hands-on securing CI/CD pipelines, scanning for vulnerabilities, and testing applications with tools like Burp Suite, Wireshark, and DefectDojo, I keep seeing the same patterns show up when I look at AI system security:
