Robust cybersecurity solutions in the public sector actively enhance data protection, safeguard critical infrastructure, and foster public trust in government institutions. However, cybersecurity faces a multitude of challenges, including the constant evolution of sophisticated threats, vulnerabilities in software and systems, and the growing complexity of digital landscapes. Artificial Intelligence (AI) emerges as a potent solution by enabling proactive threat detection through real-time analysis of vast datasets, swift identification of anomalies, and the development of adaptive response mechanisms. Therefore, this research employs a mixed-methods approach to investigate the role of AI in enhancing cybersecurity within the public sector including detection and response mechanisms. In this implementation process, we integrate qualitative methods through an in-depth examination of case studies and success stories. These real-world scenarios showcase instances where AI played a pivotal role in preventing and mitigating cyber threats. And quantitative analysis involves AI algorithms scrutinizing extensive datasets to identify subtle indicators of cyber threats. We develop AI algorithms to sift through vast datasets, monitoring and identifying subtle indicators of potential threats, including malware, phishing attacks, and advanced persistent threats. This mixed-methods approach serves as a robust strategy to ensure the reliability and validity of the research findings. The findings underscore the efficacy of AI as a sentinel in the public sector's cybersecurity landscape and significantly enhances threat detection, minimizes potential damage and defense cyber-threats. This research method not only instills confidence in the cybersecurity measures undertaken by government entities but also serves as a model for other organizations seeking effective strategies. Overall, the method contributes to the overall resilience and trustworthiness of government institutions in the digital age.