AI‑Driven Web3 Security & Real‑Time Defense covers emerging platforms and practices that apply large language models, autonomous AI agents, and unified observability to protect smart contracts, DeFi protocols, NFT marketplaces and cross‑chain infrastructure. This area focuses on continuous, low‑latency detection and automated or semi‑automated response across on‑chain telemetry and off‑chain signals to close the gap between reconnaissance and remediation. Relevance (2025): escalating numbers of on‑chain exploits, faster attack automation, and wider regulatory scrutiny have made real‑time, explainable defenses a priority. At the same time, enterprise‑grade LLMs and agent frameworks have matured enough to support contextual reasoning, retrieval‑augmented analysis, and policy‑aware action workflows that were previously impractical at scale. Key components and examples: autonomous SecOps agents and a Context Lake (Simbian) ingest and correlate alerts and transaction traces to reduce missed signals and enable automated triage; enterprise virtual assistants and multi‑agent orchestrations (IBM watsonx Assistant) provide policy‑driven automation and human‑in‑the‑loop controls; LLM platforms (Cohere, Mistral, Claude, Google Gemini) supply private, customizable models, embeddings, and retrieval capabilities for threat understanding and pattern matching; infrastructure platforms (Vertex AI) manage training, deployment, and monitoring of models in production. Operational and governance emphasis: solutions in this space balance speed with explainability, model governance, and data privacy—using private models, provenance for alerts, and fine‑grained policy controls. Products such as Kerberus Sentinel3 and its peers aim to integrate these capabilities into pipelines that detect anomalous transactions, recommend or enact mitigations, and generate audit‑ready justification for actions.