Deploying Lightweight AI for Real-Time Threat Neutralization in Governmental Communication Networks

As cyberspace becomes more complicated and advanced, communication networks, being treasure points of sensitive and mission-critical data, become increasingly vulnerable to malicious attacks. Infrastructure security remains a key area of concern, particularly with regard to latency, scalability, and responsiveness, as well as real-time demands. This research looks into the use of lightweight AI models on advanced cybersecurity frameworks for real-time threat detection and neutralization within government communication networks. It seeks to comprehend the primary approaches, model frameworks, and lightweight AI deployment strategies within cybersecurity that are tailored for sensitive environments with strict security requirements and high demands for low latency through systematic literature review of the recent AI and cybersecurity literature. It was determined that the use of sophisticated yet lightweight AI models with embedded neural networks and federated and edge learning frameworks deliver real-time analysis while safeguarding privacy and maintaining high standards of performance. Recommendations are provided that relate to policymakers, information technology professionals, cyberdefense and cyber offense experts, and infrastructure security strategists to aid in the design of systems within government frameworks with autonomous threat detection and mitigation capabilities.