This topic covers how AI technologies are being applied to anti‑money laundering (AML) and regulatory compliance across crypto and traditional financial services, and how governance and security controls are evolving to manage those systems. It is timely as of 2026‑05‑25 because regulators and examiners expect auditable models, continuous monitoring, and explainable decisioning for both transaction surveillance and customer‑facing automation. At the same time, the rise of agentic AI and enterprise LLMs has introduced new operational and auditability challenges. Core capabilities and tool categories include regulatory compliance platforms for transaction monitoring and sanctions/KYC screening; AI governance and observability stacks to log, explain, and control model behavior; and AI security governance for access, data lineage, and incident response. Representative tools from the landscape: Xilos — infrastructure focused on visibility into agentic AI activity and connected services; Kore.ai — a platform for building, orchestrating and governing multi‑agent workflows with observability; Cohere — enterprise LLMs, private models, embeddings and retrieval used to power compliant NLP and search; Bookeeping.ai — automated accounting that can feed transaction and ledger context into compliance workflows; Pria AI — sales automation that illustrates the need for compliance guardrails in outreach; and Notion — a consolidated workspace for policy, playbooks, and audit artifacts. Practical implementations combine private models and embeddings for supervised screening and investigation search, orchestration platforms to enforce approval and audit trails, and continuous observability to detect drift and agentic deviations. The emphasis is on measurable controls: auditable logs, role‑based governance, data minimization, and integration with sanctions lists and financial ledgers to satisfy evolving AML and AI governance expectations.