This topic covers AI security and anti-fraud solutions tailored to crypto wallets and decentralized finance (DeFi), spanning anti‑phishing, behavioral analysis, and bot detection. It focuses on combining on‑chain transaction graphs, off‑chain telemetry (browser/in‑app signals, IP/device fingerprints), and identity/credential signals into real‑time models that detect credential theft, social‑engineering scams, automated front‑running and sybil bot networks. Relevance and timeliness (2026‑01‑10): the volume and sophistication of automated attacks against wallets and DeFi protocols have increased, while attackers also use AI to scale phishing and social engineering. Regulators and institutional users demand stronger fraud controls and explainable detection workflows. That drives adoption of integrated AI platforms and autonomous SecOps to reduce missed alerts and accelerate responses. Key tools and roles: Simbian’s autonomous AI security agents and unified Context Lake model the SecOps workflow—detecting, reasoning, and acting across alerts, threat hunts, pentests and GRC. Cloud ML platforms such as Google’s Vertex AI (plus Gemini multimodal models) provide hosted tooling for training, evaluating and deploying real‑time models and multimodal signal fusion. Enterprise LLM providers like Cohere supply private, fine‑tunable generation and embedding services for phishing detection, alert enrichment and analyst workflows. Mistral AI’s open, efficient models and production platform address privacy, governance and on‑premises deployment needs for sensitive wallet telemetry. Trends and operational considerations: successful defenses combine low‑latency scoring, continuous model retraining on drifting attack patterns, explainability for compliance, and orchestration between AI agents and human analysts. Balancing detection accuracy with false positives, privacy, and latency remains central to practical deployments in wallets and DeFi.