The combination of AI-based on-chain data analysis and the white-hat security ecosystem @SurfAI, @immunefi, @MetaMask Despite the characteristic of transactions being publicly recorded, security incidents in the blockchain environment have long been addressed primarily through post-analysis. In this process, on-chain data has mainly been used as material to explain already occurred events, and the structure for interpreting and responding to attacks in real-time has been limited. Recently, with the combination of AI technology analyzing large-scale on-chain data and the white-hat security ecosystem, a more structured approach to interpreting and responding to security signals is being established. On-chain data includes various security-related signals beyond simple transaction histories. Sudden abnormal changes in fund flows, unexpected changes in administrative privileges, abnormal timing of smart contract upgrades, discrepancies in price feeds across multiple decentralized exchanges, and rapid changes in asset composition within liquidity pools are all characteristics that have been repeatedly confirmed through past security incident analyses. However, such data represents only a tiny fraction of all transactions, and since most transactions are normal activities, it is difficult to distinguish meaningful signals from raw data alone. At this point, an intelligent layer dedicated to interpreting on-chain data emerges. Surf serves as this on-chain intelligence layer, focusing on identifying patterns by aggregating data from various protocols and chains. The information handled by Surf is not a single transaction but rather structural changes that appear over time across multiple protocols. This allows for the identification of significant anomalies from a security perspective beyond simple transaction summaries. The signals generated in this process undergo verification and adjustment in the next stage rather than being used as is. Immunefi Magnus is the verification and orchestration layer that connects these signals to actual security responses. Magnus utilizes data collected from real-time monitoring partners like Fuzzland and Failsafe, along with the CODEX vulnerability database accumulated by Immunefi. CODEX is a structured database of actual vulnerabilities and incident cases reported by thousands of security researchers, used as a benchmark for comparing the types and impacts of security incidents. Based on this, Magnus prioritizes multiple warning signals and selects issues for security researchers to review. This structure operates across hundreds of protocols and assets worth hundreds of billions of dollars. The white-hat security ecosystem operates as a human verification layer on top of these technical layers. Immunefi has established procedures for researchers to publicly report vulnerabilities and receive rewards based on the severity of the vulnerabilities through bug bounty programs. In this process, researchers review the anomalies captured by AI and determine whether they are actual vulnerabilities. Additionally, reported vulnerabilities are communicated to the relevant protocols through mediation procedures and response processes. This structure is characterized by the integration of automated analysis and human judgment into a single flow. For security intelligence to lead to actual user protection, an interface layer is necessary. MetaMask plays the role of delivering this information to users at the wallet level. Transaction Shield indicates the risk of transactions before execution based on pre-simulation and threat databases, and analyzes contract interactions using Blockaid's threat analysis data. mUSD is a stablecoin pegged 1:1 to U.S. Treasury bonds, provided as a stable means of payment and storage within the MetaMask environment. This setup helps users proceed with transactions while reflecting risk signals, even if they do not directly interpret complex security information. This hierarchical structure also operates during incident response processes. When anomalies are detected through Magnus and confirmed by white-hat researchers, the relevant protocols can take actions such as functionality restrictions or temporary suspensions. At the same time, caution messages are delivered to wallet users. After an incident, confirmed attack methods and vulnerability information are reflected in the CODEX database, serving as a benchmark for future similar case analyses. The relatively swift recovery process observed in the Value DeFi incident is cited as an example demonstrating that this collaborative structure actually works. In the field of on-chain security analysis, specialized analysis institutions like PeckShield and CertiK continuously publish incident data. The reports they provide are used to summarize the causes and structures of individual incidents and serve as materials to enhance the overall ecosystem's security understanding. Such external analyses are also important information resources referenced by AI-based security systems and the white-hat network. The combination of AI-based on-chain data analysis and the white-hat security ecosystem is an example where the three elements of data interpretation, human verification, and user protection are interconnected in a continuous structure. Surf, Immunefi Magnus, CODEX, MetaMask mUSD, Transaction Shield, and the surrounding white-hat researchers and analysis institutions operate towards the same goal from different positions. This structure illustrates the current technological reality that blockchain security is maintained not by a single tool or entity but through a multilayered collaborative system. $MASK $SURF $CYBER $XRP