The proliferation of Internet of Things (IoT) devices and distributed computing environments has intensified the demand for robust, adaptive security frameworks capable of continuous trust evaluation. Traditional perimeter-based security models fail to address the dynamic nature of modern network ecosystems, where device behavior evolves continuously and adversarial threats adapt in real-time. This paper introduces Adaptive-AI-ZeroTrust-Chain (AAZTC), a novel framework that integrates artificial intelligence-driven dynamic trust boundary modeling with blockchain-based verifiable access logging to enable granular, auditable zero-trust enforcement. The proposed architecture employs deep reinforcement learning algorithms for continuous behavioral analysis and trust score computation, while leveraging smart contracts on a permissioned blockchain to ensure immutable, transparent access decision records. The framework incorporates a lightweight post-quantum cryptographic module to future-proof security against emerging quantum computing threats. Extensive experiments conducted on the NSL-KDD and CICIDS2017 datasets demonstrate that AAZTC achieves 98.73% detection accuracy, 97.89% precision, and 98.21% F1-score, outperforming state-of-the-art baseline methods by margins of 3.2–5.8%. The system maintains low latency characteristics with average trust decision times of 12.4 ms, making it suitable for real-time IoT deployments. Ablation studies confirm the synergistic contributions of each architectural component, validating the comprehensive design philosophy underlying AAZTC.
Faris Alsulami (Tue,) studied this question.