AI model governance, compliance & security dashboards are centralized interfaces that translate policy, risk and observability into actionable metrics and audit artifacts. As enterprises deploy multi‑agent workflows, customer-facing assistants and private LLMs, these dashboards help teams operationalize model validation, drift detection, access controls, vendor governance and regulatory reporting. Regulatory pressure (data protection rules, sector guidance and AI-specific laws) and rising operational risk mean organizations now demand continuous monitoring, reproducible validation, and clear audit trails rather than ad‑hoc reviews. Product approaches vary by focus: Monitaur targets insurance and other highly regulated industries with centralized policy, monitoring, validation and vendor governance; StackAI offers a no‑code/low‑code end‑to‑end platform to build, deploy and govern enterprise AI agents; Kore.ai emphasizes observability and orchestration for multi‑agent workflows; Observe.AI concentrates on conversation intelligence and real‑time QA/assist for contact centers; IBM watsonx Assistant provides enterprise virtual agents and multi‑agent orchestration built on watsonx LLMs; Cohere supplies private, customizable LLMs, embeddings and retrieval to support secure model serving and search. Common dashboard functions include policy rule management, model performance and drift metrics, data and feature lineage, access and usage logs, vendor/SLA tracking, validation reports, and connectors to SIEM/MLOps pipelines. By 2026, the dominant trend is operational governance—integrating no‑code controls with developer APIs, embedding observability into production agents, and tying model telemetry to compliance workflows—so security, legal, and model ops teams can demonstrate continuous compliance and reduce model risk in regulated environments.