AI can help your DevSecOps pipeline (www.spiceworks.com)

A recent analysis argues that AI and ML are poised to materially improve DevSecOps by automating and accelerating security tasks across the CI/CD lifecycle—shifting security left into planning, coding, testing, deployment, and operations. Practically, teams are embedding AI-driven static code analysis, container and dependency scanning, API tests, and infrastructure-as-code checks (Terraform, OpenTofu, Spacelift, Ansible) directly into pipelines to catch vulnerabilities before production. Vendors and tools already in play include Darktrace PREVENT, Qualys VMDR, Microsoft Defender Threat Intelligence, CrowdStrike Falcon, Tanium, NinjaOne, Microsoft Security Copilot, Vectra, SentinelOne, Radiant Security and HiddenLayer, enabling continuous risk assessment, automated patch prioritization, regression testing and compliance automation for standards like GDPR and PCI DSS. The significance for the AI/ML community is twofold: these use cases create high-impact, data-rich problems for ML (anomaly detection, prioritization, remediation suggestion) while exposing risks from overreliance on imperfect models. Studies show many orgs struggle to implement DevSecOps and demonstrate ROI, and practitioners warn that AI can generate noisy false positives or even harmful artifact (e.g., bogus bug reports). Best practice: use AI as a force-multiplier—not a replacement—treating its outputs as prioritized suggestions verified by experienced engineers to gain speed and safety without trading human judgment for model certainty.