7. Risk Control

With the rapid expansion of decentralized computing networks and increasing AI training task complexity, cAIToken regards risk control as the foundation for ecosystem sustainability. From inception, the project follows the principles of "safety first, computing authenticity, system stability, task trustworthiness," building a multi-layer defense system covering system, node, computing fraud, and compliance risks, ensuring network stability, transparent node behavior, and protection of user contributions.

7. Risk Control

With the rapid expansion of decentralized computing networks and increasing AI training task complexity, cAIToken regards risk control as the foundation for ecosystem sustainability. From inception, the project follows the principles of "safety first, computing authenticity, system stability, task trustworthiness," building a multi-layer defense system covering system, node, computing fraud, and compliance risks, ensuring network stability, transparent node behavior, and protection of user contributions.

1) System and Network Security Assurance

cAI employs multiple security mechanisms:

Hot/Cold Node Segmentation: Core validator nodes and standard computing nodes run in layers; sensitive tasks handled only by high-reputation nodes to prevent malicious participation.

Communication and Data Security: Task dispatch and computation return utilize full-path encryption and data anonymization with random sharding to protect training data and intermediate results.

Contract Security Audits: Mining pool, task distribution, and reward contracts undergo professional audits and multi-signature authorization before upgrades.

Real-Time Monitoring Engine: Monitors node downtime, task delay, abnormal outputs, and triggers task reassignment as needed.

2) Computing Authenticity and Node Behavior Control

To ensure genuine computing contributions, cAI establishes verification mechanisms:

Proof of Compute: Nodes submit training results with verifiable intermediate computation evidence to prevent fake contributions.

Node Reputation System: Scores based on task completion quality, historical performance, and failure rates, affecting task priority and rewards.

Staking and Punishment: Malicious nodes or repeated fake submissions result in stake deductions, reputation reduction, or permanent removal.

Task Rollback and Automatic Recheck: Non-compliant results trigger automatic recomputation to ensure reliability.

3) Operational Risk Response Mechanism

To address node exit, computing concentration, or extreme fluctuations, cAI implements operational contingency strategies:

Disaster Recovery and Redundancy: Network deployed across multi-region node pools, maintaining service despite partial offline nodes.

Self-Healing Scheduling: Scheduler switches to backup nodes upon anomalies, ensuring continuous task execution.

Risk Communication: Rapid public announcements to avoid panic during critical events.

Technical Contingency Plans: Emergency task halt or restriction of malicious nodes in case of severe faults.

4) Intelligent Risk Control Model Innovation

cAI directly applies AI for risk management:

Behavior Profiling: Analyzes node login, execution patterns, and computing performance to identify potential risks.

Anomaly Detection Algorithms: Detect abnormal training cycles, repeated outputs, or abnormal computation speed.

Dynamic Policy Updates: Risk parameters iteratively updated by AI models for flexibility and foresight.

Through this comprehensive security and risk control framework, cAIToken establishes a stable, transparent, and sustainable computing network, providing a reliable participation environment for users and nodes.