AI-powered Web3 wallet security and detection platforms use ensembles of models and agentic workflows to fuse on-chain, off-chain, behavioral and conversational signals for faster, more accurate threat detection and response. By 2026 the proliferation of DeFi, NFTs and account abstraction has increased attack surface complexity—phishing, social-engineering, smart-contract exploits and automated agent attacks now require multi-modal detection rather than single-model heuristics. A practical stack pairs infrastructure for running and orchestrating models (Vertex AI for managed model training, deployment and monitoring) with platforms that enable agentic automation and visibility (Xilos for agentic AI infrastructure and service-level observability). No-code/low-code agent builders like StackAI accelerate deployment of detection agents and remediation playbooks. Governance and risk controls (Monitaur) are essential in regulated or insured environments to centralize policies, vendor oversight and validation for model-driven decisions. Conversation and social-channel signals—where much wallet compromise begins—can be analyzed with tools like Observe.AI to surface social-engineering indicators in voice and chat interactions. Key considerations for these multi-model stacks include cross-model orchestration, explainability, continuous evaluation, telemetry fusion (graph ML for on-chain links, behavioral models, LLM-based semantic detectors), and policy-aligned incident response. Operationalizing such stacks requires end-to-end visibility, vendor governance, and runtime monitoring to manage false positives, adversarial inputs and regulatory requirements. Together, these components form a defensible, auditable approach to reducing Web3 wallet risk while enabling rapid adaptation as attacker techniques evolve.