Cognitive Zero-Trust Resilience: An Adaptive Cybersecurity Framework for Dynamic Connected Systems
2025
The increasing connectivity and complexity of modern systems, from Industrial Control Systems (ICS) to Internet of Things (IoT) networks, have dramatically expanded the attack surface for cyber threats.Traditional security frameworks struggle to adapt to the dynamic nature of these environments and often rely on static perimeter-based defenses or rigid policy enforcement.This study introduces the Cognitive Zero-Trust Resilience Framework (CZTRF), a novel approach that integrates cognitive computing capabilities with zero-trust architectural principles to create a self-evolving security posture.Unlike conventional frameworks, CZTRF continuously analyzes the operational environment, threat intelligence, and system vulnerabilities to dynamically adjust security controls.By leveraging machine learning to refine the Integrated Cyber Risk Management (ICRM) equation variables in real time, the framework quantifies risk more accurately and automatically implements proportional countermeasures.CZTRF extends beyond traditional zero-trust by incorporating resilience metrics that measure a system's ability to prepare for, absorb, recover from, and adapt to adverse cyber events.The simulation results demonstrate that CZTRF significantly improves threat prediction accuracy, reduces response time, and enhances system resilience compared with static security frameworks.This study presents the theoretical foundations, architectural components, implementation methodology, and validation results of the CZTRF, offering a comprehensive approach to securing increasingly complex connected systems against evolving cyber threats.