ARTIFICIAL INTELLIGENCE, MACHINE LEARNING ALGORITHM IN SUSTAINABLE CYBERSECURITY PRACTICES FOR DIGITAL AGE

Abstract

The proliferation of digital technologies has necessitated the integration of sustainable cybersecurity practices to safeguard against escalating threats. This research explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in fortifying cybersecurity frameworks for the digital age. By leveraging AI-driven threat detection and ML-powered predictive analytics, this study aims to develop a robust and adaptive cybersecurity paradigm capable of mitigating emerging risks and ensuring the integrity of digital ecosystems. The investigation will delve into the optimization of AI/ML algorithms for enhanced cybersecurity performance, the examination of their applications in threat intelligence and incident response, and the analysis of their implications on sustainable digital transformation. 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 Ultimately, this research seeks to contribute to the development of resilient and sustainable cybersecurity practices, empowering organizations to navigate the complexities of the digital landscape with confidence.