Paper 2026/1341 Original: eprint.iacr.org/2026/1341 ↗

A Modular Risk Assessment Module for Adaptive Cryptographic Selection in Q-OPSEC

Abstract

This paper presents RiskService, a modular risk assessment module integrated into the Q-OPSEC adaptive AI middleware for quantum cryptography. A synthetic dataset covering 58 features across nine groups, including behavioral, device, network, authentication, and LLM-derived signals, feeds a training pipeline evaluating six model families under class-imbalanced conditions. LightGBM achieves the best performance, with AUC-ROC of 0.9895, average precision of 0.9344, and Brier score of 0.0421 at threshold 0.60, with inference latency of 1.8ms. Deployment benchmarks across three hardware tiers confirm feasibility under constrained resources: quantized XGBoost runs in 54.2ms on the ESP32 with AUC-ROC of 0.9112, enabling a two-tier architecture where edge nodes perform preliminary screening and forward ambiguous events for full-precision regime determination. Calibrated risk scores govern the selection among classical TLS1.3, post-quantum, and hybrid key derivation paths in the Q-OPSEC cryptographic layer.