SENTINEL-GUIDED INTELLIGENT CYBERSECURITY GOVERNANCE FOR RESILIENT DIGITAL PUBLIC SERVICES: AN ADAPTIVE RISK-BASED FRAMEWORK

Povzetek

Reliability and security for digital public services depend more now than ever upon robust network infrastructure as well as strong cyber-security management systems. Interconnected digital platforms are used by local government organizations for different purposes; however, there are issues like network failures and cyber attacks that can affect their operations as well as people’s confidence in them. The paper presents a new model called SG-ACGF which is an adaptive cybersecurity governance framework for improving resiliency and responsibility within internet-based government services' infrastructure systems. Graph Attention Networks are combined with Deep Reinforcement Learning for evaluating network connectivity and adapting to changing threats. This is an intrusion detection system based on sensors that can identify anomalies as well as predictive models of future events so we can take preventive measures beforehand. The Explainability Risk Prioritization Module helps to assess severity levels of incidents as well as their importance to services so that they can be addressed by local governments more effectively. Governance is based on roles of responsibility and transparency in making decisions. This model would be evaluated by means of simulation studies as well as comparison to current ones based upon parameters such as packet delivery rate and response time for governance issues. In conclusion, our study aims to integrate an adaptive network security system with local government administration so that there is better resilience in providing services of citizens through technology.