🤖 AI Summary
Current AI agents in autonomous telecommunications networks lack standardized runtime validation mechanisms, rendering them susceptible to safety risks stemming from erroneous decisions. This work proposes the Guard Rail Validation (GRV) framework, introducing a novel criticality-tiered runtime verification architecture. GRV dynamically assesses decision risk through a weighted evaluation of multidimensional indicators—including action scope, service criticality, autonomy level, reversibility, and temporal behavior—and enforces tiered validation accordingly. The framework supports cross-agent conflict detection, priority arbitration, and compliant audit logging. Evaluated in an O-RAN environment, GRV demonstrates high coverage against known AI/ML attacks and aligns with regulatory requirements such as Article 14 of the EU AI Act.
📝 Abstract
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.