Enhancing Cyber Defense Mechanisms: AI and Machine Learning-Based Threat Mitigation Strategies

Abstract

As cyber threats become more advanced and persistent, traditional security measures are proving inadequate to prevent or mitigate attacks effectively. The rise of Artificial Intelligence (AI) and Machine Learning (ML) has introduced new, sophisticated strategies for improving cybersecurity defenses. AI and ML-based threat mitigation strategies, focusing on how these technologies can enhance the ability of organizations to detect, analyze, and respond to evolving cyber threats. By automating the detection of anomalies, identifying patterns in large datasets, and adapting to new attack vectors, AI-driven systems provide dynamic and proactive defense mechanisms. This research investigates various AI/ML models, including supervised learning, unsupervised learning, and deep learning, applied to real-time threat mitigation and response. It also presents case studies of AI-powered solutions used to combat malware, ransomware, phishing attacks, and insider threats. an analysis of the challenges associated with implementing AI in cybersecurity, including issues of data privacy, bias, and adversarial attacks on AI models. The findings suggest that while AI and ML-based systems significantly enhance cyber defense, they must be continuously refined to keep pace with emerging threats and ensure robust, ethical security practices in the digital era.