In today's rapidly evolving digital landscape, risk control systems have become the core defense line for enterprises against malicious activities, fraud, and ensuring transaction security. However, traditional risk control faces challenges such as high reliance on manual labor and lagging strategies. Data analysts must manually extract risk characteristics and design protective rules from massive amounts of data daily, which is time-consuming and inefficient; new strategies often take days or even weeks to deploy, while malicious actors constantly update their attack methods. How can we make risk control systems "think autonomously and evolve in real-time," just like humans? The answer lies in the deep integration of AI Agents and risk control systems.
The Solution: AI Agent + Expert Model + Risk Control System = Intelligent Risk Control Agent
Tongfu Shield introduces RiskAgent, codenamed "Doge," as a loyal security companion tirelessly and proactively identifying security risks. "Doge" leverages expert domain models and a Multi-Agent Collaboration Protocol (MCP) to build an integrated risk control solution encompassing "perception, decision-making, and execution."
Core Capabilities: Four Major Leaps from "Human-Driven" to "AI-Driven"
1. Intelligent Risk Feature Mining
l Large Model Understanding of Business Semantics: Through natural language interaction, the AI Agent can accurately parse user business requirements (e.g., "evaluate the risk control system's performance last month"), automatically correlate data fields, and generate feature processing logic.
l Automated Feature Engineering: Based on the built-in risk control knowledge base, the Agent can call statistical tools and graph computing engines to automatically generate high-value features such as "number of associated accounts within 7 days on the same device" and "abnormality of user behavior sequences," significantly improving efficiency.
2. Dynamic Strategy Generation and Verification
l Strategy Inference and Simulation Testing: The AI Agent combines historical risk control data and real-time data details to generate candidate strategies using a large model, verifies their effectiveness in a simulation environment, and automatically recommends the optimal rule combination.
l Explainable Risk Decision-Making: Each strategy comes with a natural language interpretation report clearly showing the triggering conditions, impact scope, etc., eliminating "black box" concerns.
3. MCP Protocol-Driven Automated Execution
l Seamless Integration with Risk Control Systems: Through the MCP protocol, the AI Agent can cross-platform schedule toolchains—automatically generating SQL to extract data, deploying strategies using a rule engine, and issuing commands to the interception system, all without manual coding.
l Minute-Level Strategy Iteration: The entire process, from feature analysis to strategy deployment, is compressed to the minute level, effectively handling scenarios such as "sudden attacks from malicious users in the early morning."
4. Continuously Evolving Risk Control Knowledge Base
l Attack Pattern Self-Learning: The AI Agent monitors strategy effectiveness in real-time, automatically captures abnormal samples that bypass rules, dynamically analyzes and generates new strategy suggestions, forming a closed loop of "attack-defense-model iteration."
l Human Expert Collaboration: Data analysts can correct AI strategy logic through dialogue, and the system synchronously updates the knowledge base, achieving human-computer collaborative evolution.
