This topic covers enterprise AI platforms and governance stacks tailored for regulated industries—finance, payments, and healthcare—where data privacy, model risk management, auditability, and regulatory reporting are mandatory. As of late 2025, organizations must balance productivity gains from generative AI with controls that satisfy regulators, internal compliance teams, and auditors. Key capabilities include private/customizable LLMs, retrieval-augmented workflows, integrated audit trails, role-based access, data residency and masking, and explainability tools. Vendor and product categories to evaluate include regulatory compliance tools (for policy enforcement, monitoring, and logging), AI governance platforms (model inventory, testing, and drift detection), and AI security governance (data protection, threat mitigation, and secure deployment). Representative platforms: IBM watsonx Assistant for enterprise virtual agents and multi-agent orchestrations with no-code and developer paths; Microsoft 365 Copilot for embedding assistant functionality across productivity apps; Cohere for private, customizable LLMs, embeddings, and retrieval; Anthropic’s Claude family for conversational and analysis-focused assistants; Harvey for domain-specific legal/professional workflows; Observe.AI for contact-center voice and QA with post-interaction analytics; Kay AI for insurance operations automation; and Cimba.AI for no-code agent creation with emphasis on auditable governance. Selecting solutions requires mapping regulatory requirements (model governance, data handling, record retention) to product capabilities—private model hosting, fine-tuning with labeled safe data, searchable and versioned knowledge bases, and demonstrable audit logs. In practice, regulated enterprises increasingly adopt hybrid approaches: domain-specific platforms for regulated workflows, enterprise LLM providers for core models, and dedicated governance/security layers to enforce compliance and reduce model risk.