This topic covers the tools and practices organizations use to secure, govern and demonstrate compliance for AI in both creative workflows and tightly regulated sectors (insurance, finance, healthcare, public sector). As of 2026-01-27, enterprise use of generative and agentic AI has grown alongside stronger regulatory expectations (audit trails, model risk management, data privacy), creating demand for centralized policy, monitoring, vendor governance, and automated detection and response. Key product categories include: governance and policy platforms (Monitaur), which centralize policies, monitoring, validation and third‑party vendor controls—notably for insurance and other highly regulated firms; intelligent agentic infrastructure (Xilos) that provides visibility into agent activity and connected services; agentic-AI SOC platforms (Radiant Security) that automate ingestion, triage, investigation and response across security stacks; and multi-agent orchestration and assistant platforms (Kore.ai, IBM watsonx Assistant, Microsoft 365 Copilot) that enable no-code to pro-code workflows but require observability and governance hooks. Foundational and managed model platforms (Mistral AI’s enterprise models and production tooling, Google’s Vertex AI) are central for model provisioning, fine‑tuning, evaluation and runtime control. Practical priorities today are transparency, continuous monitoring, vendor and model validation, and automation of security and compliance workflows. Organizations evaluate combinations of model platforms, governance layers, and SOC/observability tooling to meet auditability and risk‑management needs while enabling creative use cases. The ecosystem trend is toward integrated stacks that instrument agentic behavior, provide end‑to‑end traceability, and automate compliance tasks without blocking productive AI adoption.