This topic covers tools and patterns for preventing, detecting and governing unsafe or non‑compliant generative content across enterprise systems and community platforms. As organizations deploy conversational agents, multi‑agent orchestrations and embedded assistants, operational controls—visibility, provenance, automated moderation, audit trails and human‑in‑the‑loop workflows—become essential to manage legal, reputational and safety risks. No related articles were supplied; the overview synthesizes the provided tool descriptions and current industry trends as of 2026. Key categories include AI security governance (monitoring agent behavior and service connections), regulatory compliance tooling (audit logs, policy evidence), AI governance platforms (policy enforcement and model lifecycle controls), content detectors (classification and provenance checks) and community moderation tools (escalation/workflow systems). Representative tools: Xilos emphasizes enterprise visibility into agentic activity and connected services; IBM watsonx Assistant and the Claude family enable deployable virtual agents and multi‑agent orchestrations that require governance hooks; Microsoft 365 Copilot and Notion show how assistants and knowledge platforms embed generative AI into productivity stacks; Mistral AI highlights open/efficient models and an enterprise production platform with privacy and governance considerations; n8n and Zapier illustrate how automation platforms connect detectors, moderation rules and human review into operational workflows. Trends to watch: growing regulatory scrutiny and data‑residency expectations, wider use of agentic AIs that increase the need for runtime visibility, and the practical necessity of composable moderation pipelines (detectors + automated actions + human review). Effective implementations combine model‑level controls, system telemetry, auditability and integration into existing workflows to reduce risk without blocking legitimate productivity gains.