This topic examines the emergence of specialized large language models (LLMs) tuned for code security—models and services that assist with vulnerability detection, secret scanning, secure‑coding recommendations, and developer workflow integration—using Anthropic’s Claude family (including code‑security oriented variants), Google’s Gemini and Vertex AI platform, Cohere, IBM watsonx Assistant, and Mistral AI as representative options. By 2026 these specialized LLMs are increasingly used as part of software supply‑chain security: augmenting SAST/DAST tools, producing explainable vulnerability traces, generating remediation suggestions, and powering developer-facing assistants that enforce policy and guardrails. Enterprise platforms (Vertex AI, watsonx, Cohere) emphasize private deployment, fine‑tuning, embeddings/RAG for project‑specific context, and operational controls (access, logging, model versioning) to meet governance needs. Providers like Mistral position open/efficient models for privacy‑focused deployments; cloud vendors (Google’s Gemini family, Anthropic’s Claude) offer multimodal and conversational capabilities that fit interactive code review and triage workflows. Key governance considerations include managing hallucination and overconfidence in vulnerability claims, preventing secret leakage and prompt‑injection, maintaining audit trails for model decisions, and validating model outputs with deterministic SCA pipelines and human review. Organizations evaluate tradeoffs between hosted convenience and on‑premises control, model explainability, and integration with CI/CD and ticketing systems. This area sits at the intersection of AI security governance and software engineering: adoption is driven by the need for scalable, continuous code inspection while demand for robust evaluation, reproducibility, and policy controls grows. Comparing vendors requires looking beyond raw capability to deployment options, governance primitives, model transparency, and integration with existing security toolchains.