Utilizing AI and Machine Learning in Cybersecurity for Sustainable Development through Enhanced Threat Detection and Mitigation

Abstract

This research investigates the symbiotic relationship between artificial intelligence (AI), machine learning (ML), and cybersecurity within the context of fostering sustainable development. The study explores the efficacy of AI-driven cybersecurity measures in fortifying digital infrastructures against evolving cyber threats. A glimpse of the quantitative results reveals compelling insights: AI-based systems showcased an average threat detection accuracy of 92.5% across diverse cyber threat types, with a minimal false positive rate of 3.2%. The implementation of ML algorithms reduced response times to cyber attacks by 40%, underscoring their pivotal role in prompt threat mitigation. Furthermore, the research elucidates the efficiency of AI in preventing phishing attacks (95%) and prioritizing critical vulnerabilities for patching, resulting in a 30% reduction in high-risk unpatched vulnerabilities. These glimpses into the quantitative outcomes underscore the transformative potential of AI and ML in bolstering cybersecurity measures, aligning with sustainable development goals by fortifying digital resilience and protecting critical infrastructures.