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Users: AI improves the interpretability of transaction outcomes, helping users understand transaction results and analyze the causes of failure and state changes. Platforms/Protocols: Platforms benefit from stronger transaction interpretation, risk identification, and continuous monitoring capabilities, thereby enhancing the platform's own security tool layer and operational efficiency. Security/Operations Teams: AI, with the help of preset monitors, replaces some high-frequency and repetitive monitoring and alerting work, reducing the labor cost of security operations.
Final Signers / Platform Teams: Most tools only undertake part of the interpretation and risk identification work, while the final decision-making power and ultimate responsibility remain with the user and the platform team. Therefore, the final signers and teams must bear the consequences of misjudgment, misplaced trust, and erroneous decisions. AI Agent Developers/Users: After integrating AI agents into development, testing, and validation workflows, developers must bear the risks of incorrect calls, misconfigured permissions, and automated operational errors. Privacy-Sensitive Users: Privacy-sensitive users may be cautious about exposing transaction behavior, address activities, or tool permissions to external security/interpretation platforms; therefore, these users bear additional concerns and trust costs. Platforms/Teams Relying on External Security Intermediaries: After integrating intelligence databases, external scanning systems, and security tools, platforms and teams bear additional dependency costs, misjudgment costs, and maintenance costs; if external intelligence or rules are inaccurate, it will inversely affect their own business judgments. External Security Intermediaries Themselves: External intermediaries must continuously bear higher responsibilities for data screening, rule maintenance, and model updates; otherwise, the quality of their output will backfire on downstream platforms and users.