Agentic Intelligence for Unified Cyber Defense: A Self-Adaptive Framework for Threat Detection Across Cloud, Edge, and IoT Systems

The rapid expansion of cloud, edge, and Internet of Things environments has increased both the scale and complexity of modern cyber-attacks. Conventional detection systems that operate effectively under stationary conditions often degrade when attacker behaviour or underlying data distributions change. This work introduces the Agentic Intelligence Self-Adaptive Framework (AISAF), a unified and self-adjusting threat detection model designed for heterogeneous computing environments. AISAF employs a hybrid CNN, LSTM, and Transformer backbone to capture spatial, temporal, and contextual dependencies in network traffic. Concept drift is detected through lightweight behavioural monitoring that tracks latent-space deviations and prediction-confidence variations in streaming data. When meaningful drift is detected, an agentic meta-optimization controller selectively updates only the affected submodules, reducing retraining cost while preventing catastrophic forgetting. An attention-based explanation layer provides feature-level interpretability that remains consistent during adaptation, supporting transparent and auditable threat reasoning. AISAF is evaluated on six benchmark datasets spanning financial, network, and IoT domains: PaySim, IEEE-CIS Fraud Detection, CICIDS2018, UNSW-NB15, CICIDS2017, and BoT-IoT. Experimental results show consistent improvements in adaptability, robustness, and drift recovery compared with classical machine learning, deep learning, and adaptive baselines. AISAF achieves reliable cross-domain generalization and maintains transparent decision behaviour throughout the adaptation process, highlighting its potential as a practical step toward autonomous and interpretable cybersecurity systems.