AI model security and vulnerability scanning covers the tools and practices used to find, fix, and mitigate weaknesses in large language models (LLMs) and agentic AI systems. This topic spans automated red‑teaming, fuzzing and adversarial testing, prompt‑injection and supply‑chain detection, runtime monitoring, policy‑as‑code, and model‑hardening workflows that integrate into CI/CD and compliance pipelines. Relevance in 2026 stems from widespread enterprise adoption of autonomous agents, expanded use of multimodal models in production, and growing regulatory expectations for documented testing, vendor governance, and incident visibility. Organizations face risks from prompt injection, data‑poisoning, model extraction, unintended agentic behavior, and third‑party service exposures—risk vectors that require both pre‑deployment scanning and continuous runtime controls. Key tooling patterns include: observability and service‑level visibility (e.g., Xilos’ agentic AI infrastructure for tracking connected services and agent activity); no‑code/low‑code agent platforms that bake governance into deployments (StackAI, Lindy); industry‑focused governance and vendor validation for regulated sectors (Monitaur’s insurance‑centric policy, monitoring and validation); enterprise assistant platforms with developer and no‑code controls for safe automation (IBM watsonx Assistant); and foundational model providers whose APIs and model choices (e.g., Google Gemini) influence attack surface and hardening strategies. Practitioners should evaluate combinations of scanning tools, runtime monitors, governance platforms, and model‑provider controls to create automated test suites, enforce policies, and produce auditable evidence for compliance. The effective approach is pragmatic: integrate detection and hardening into development lifecycles, and align technical controls with regulatory and vendor‑risk requirements.