This topic examines how AI-driven fraud and phishing detection systems for crypto wallets complement or replace traditional protections (MFA, hardware wallets, whitelists, rate limits) by using autonomous agents, large models, and analytics to detect sophisticated scams and respond in real time. Based on the provided tool descriptions and prevailing industry trends, AI approaches correlate on-chain and off-chain signals, analyze natural-language phishing content, and automate SecOps workflows to reduce missed alerts and accelerate incident response. Key tool roles: Simbian’s autonomous SecOps agents and a unified Context Lake illustrate agent-led detection and cross-signal correlation for faster triage; Kore.ai shows how multi-agent orchestration with built-in governance and observability enables auditable, human-in-the-loop workflows; Claude, Google Gemini, and Mistral AI represent LLM capabilities for semantic phishing detection, behavioral profiling, and feature engineering; Vertex AI provides end-to-end model training, deployment, and monitoring infrastructure; Sisense supplies analytics and embedded BI to visualize patterns, tune rules, and support investigations. Why it’s timely (as of 2026-01-12): crypto fraud and phishing continue to grow in scale and sophistication, pushing teams to combine real-time ML inference with automated playbooks. At the same time, regulatory and enterprise emphasis on model governance, explainability, and observability makes vetted platforms and production tooling essential. Practical considerations include managing false positives, adversarial evasion, model drift, and privacy when linking wallet telemetry and external signals. The most effective architectures blend traditional controls with AI-detected anomalies, agent-driven response, rigorous governance, and analytics-driven feedback loops to maintain resilient wallet security.