Title: AI-Driven Adaptive Cybersecurity:
Authors: A Multi-Layer Detection And Governance Framework
Volume: 10
Issue: 9
Pages: 121-131
Publication Date: 2026/09/28
Abstract:
Cybersecurity environments now generate a mixture of endpoint events, authentication records, network flows, application logs and threat intelligence that can exceed the practical capacity of manually maintained detection rules. This study proposes a multi-layer adaptive cybersecurity framework in which deterministic rules, machine-learning analytics, contextual risk scoring and human review operate as complementary components. The manuscript reorganizes the supplied source around five operational functions-malware analysis, traffic anomaly detection, behavioral monitoring, access verification and alert prioritization-and adds a governance layer covering model drift, explainability, adversarial robustness, privacy and accountability. A structured comparative methodology is developed using accuracy, precision, recall, F1-score, latency, analyst workload and response time. New synthetic datasets are constructed to demonstrate how the framework can be evaluated without presenting simulated values as field observations. In the illustrative experiment, the hybrid strategy produces the strongest mean accuracy (94.12%) and F1-score (92.96%), while rule-based detection has the lowest mean latency (30.2 ms). An additional six-month synthetic scenario shows how increasing AI-assisted triage can coincide with faster response times, but the result is presented only as a methodological demonstration. Recent NIST guidance and the 2025 ENISA threat landscape reinforce the need to treat AI as both a defensive capability and an additional risk surface. The paper concludes that responsible cybersecurity automation should optimize detection quality and operational efficiency without removing human accountability.