为自适应电信网络中的AI决策设计实时安全验证框架
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
- 通过多维度评估决定关键性等级，动态启用不同验证机制
- 可拦截并验证高风险操作，降低错误自主决策风险
- 适合需要合规与高可靠性的智能网络运维场景
向完全自治的电信网络（自治等级4-5）演进要求AI/ML代理在无人员干预下实时做出网络决策。然而，目前缺乏标准化的运行时机制来拦截和验证单个推理输出，防止其触发真实网络状态变更，存在错误自主决策的风险。本文提出守护轨道验证（GRV）框架，一种可标准化的运行时架构，用于在执行前拦截并验证AI驱动的决策。该框架基于多个加权维度——包括动作范围、动作类型、服务关键性、代理自治等级、可逆性及时间行为模式——评估决策的关键性等级，并据此应用分级验证机制：执行并记录日志、边界检查、独立代理验证或多方代理共识。框架还提供跨代理冲突检测与关键性加权优先级解决，并支持运行时合规日志记录以满足监管要求（如欧盟人工智能法案第14条）。我们展示了该架构、算法流程、O-RAN部署模型，并评估了对已知电信领域AI/ML攻击的威胁覆盖能力。
原文摘要 · Abstract (English)
The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisions without human intervention. However, no standardized runtime mechanism exists to intercept and validate individual inference outputs before they trigger live network state changes, creating risks of erroneous autonomous decisions. This paper proposes the Guard Rail Validation (GRV) framework, a standardizable runtime architecture for intercepting and validating AI-driven decisions before execution. The framework evaluates decisions across multiple weighted dimensions -- including action scope, action type, service criticality, agent autonomy level, reversibility, and temporal behavioural patterns -- to determine a criticality level. Based on this level, graduated validation mechanisms are applied: execute-with-logging, bounds checking, independent agent validation, or multi-agent consensus. The framework additionally provides cross-agent conflict detection with criticality-weighted priority resolution and runtime conformance logging for regulatory compliance (e.g., EU AI Act Article 14). We present the architecture, algorithmic procedures, O-RAN deployment model, and evaluate threat coverage against known AI/ML attacks in telecommunications.
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