AI DevSecOps: how machine learning is reshaping the secure SDLC | Harness Blog

AI DevSecOps applies machine learning throughout the secure software development lifecycle to automate tasks such as security scanning, finding deduplication, risk-based triage, code review, remediation, and release decisions, while also addressing risks created by AI-enabled development. The approach expands traditional DevSecOps asset coverage beyond code and dependencies to include AI models, prompts, and training or retrieval data, which can introduce threats such as model supply-chain tampering, data poisoning, and prompt injection. The article argues that AI can reduce the backlog of duplicate or low-priority findings, but human review, governance, provenance, policy enforcement, and auditability remain necessary because AI-generated code and automated decisions can create new vulnerabilities or errors. It describes maturity as progressing from ad hoc AI use to governed and verified autonomous workflows, with models and related assets inventoried, versioned, monitored, and subject to policy as code. Harness positions its platform as supporting this model through scanner orchestration, AI-assisted triage and remediation, secure AI coding integrations, SBOM and AI-BOM generation, provenance verification, and pipeline-based security controls.

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