AI Code Security & DevSecOps covers the intersection of code-focused large language models, machine‑learning–enhanced static analysis, and governance controls that keep modern development pipelines secure and auditable. By 2026, teams routinely pair code assistants and code LLMs with SAST and CI/CD controls to speed development while reducing supply‑chain, secrets, and logic vulnerabilities. Key capabilities include context‑aware code review and automated test generation (Qodo/Codium), AST- and code‑graph–driven reviews that combine linters, SAST and generative feedback (CodeRabbit), and AI-native IDE/agent platforms that run multi-model stacks and live previews (Windsurf). Code-specialized open models such as Salesforce CodeT5 and Meta’s Code Llama are commonly used to power embeddings, vulnerability pattern recognition, and code-completion that respect license and data controls. Enterprise governance platforms (e.g., Monitaur) add policy centralization, monitoring, vendor risk controls and validation workflows important for regulated industries. Practically, teams are adopting ML‑augmented SAST to prioritize findings, reduce false positives, and surface contextual exploitability, while integrating model governance and SDLC rules to enforce testing, peer review, and deployment gates across multi-repo environments. The result is a shift from manual triage to a combined human+AI workflow where assistants propose fixes and CI enforces security policies. This topic is timely because widespread adoption of code LLMs has increased both productivity and new attack vectors, prompting stronger emphasis on reproducible toolchains, private/on‑prem model deployment, observability, and regulatory compliance. Understanding the tool categories and how they integrate—code assistants, ML‑driven SAST, governance platforms, and AI-native IDEs—helps teams select and compose controls that align security with developer flow.