This topic covers AI-enabled security and anti‑scam capabilities used by crypto exchanges and on‑chain monitoring platforms, and the governance and compliance tooling needed to operate them safely. Exchanges (for example, exchange-side Anti‑Scam programs such as Bitget’s anti‑scam initiatives) and third‑party on‑chain monitors combine transaction analytics, wallet clustering, threat intel, and automated alerting to detect phishing, rug pulls, wash trading and other fraud vectors. Rising regulatory scrutiny, persistent social‑engineering campaigns, and increasingly automated attacker tooling make these capabilities both timely and operationally critical as of mid‑2026. Practical deployment requires not only detection models but governance, observability, and vendor controls. Enterprise AI governance platforms like Monitaur centralize policy, monitoring, validation and vendor oversight for regulated environments. Agent‑aware infrastructures such as Xilos provide visibility into agentic AI activity that many monitoring stacks now orchestrate. Platforms for building and operating assistants and multi‑agent workflows — IBM watsonx Assistant, Kore.ai, StackAI and conversational models from Claude or Mistral — are used to automate incident triage, customer communications, and analyst workflows while requiring explicit observability and compliance controls. Microsoft 365 Copilot and other productivity assistants can accelerate reporting and investigation but must be governed to prevent data leakage. Key considerations for practitioners: integrate on‑chain signals with off‑chain intelligence, apply explainability and audit trails to automated decisions, maintain vendor and model governance, and align detection workflows with regulatory reporting obligations. The ecosystem blends specialized blockchain monitoring with enterprise AI governance and agent orchestration — a multidisciplinary stack focused on reliable, auditable anti‑scam operations rather than black‑box automation.