Emerging Cybersecurity Threats in the Era of AI and IoT: A Risk Assessment Framework Using Machine Learning for Proactive Threat Mitigation

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, enabling automation, real-time decision-making, and enhanced connectivity. However, these advancements have also introduced new cybersecurity threats, increasing the vulnerability of interconnected systems. The proliferation of IoT devices and AI-driven applications has expanded the attack surface, making them prime targets for cyber adversaries. Traditional security mechanisms, which often rely on signature-based threat detection, struggle to address sophisticated attacks such as adversarial AI manipulations, IoT botnet infiltrations, and real-time data breaches. This study examines emerging cybersecurity risks in AI and IoT environments, emphasizing the limitations of existing security frameworks in detecting and mitigating evolving threats. One of the key challenges is the inability of conventional methods to adapt to novel attack patterns in dynamic and complex networks. To address this issue, we introduce a machine learning-based risk assessment framework designed for proactive threat mitigation. This framework leverages anomaly detection, behavioral analytics, and predictive threat modeling to identify potential cybersecurity risks in real time. By integrating adaptive learning algorithms and continuous monitoring, the proposed system enhances resilience against AI-driven cyberattacks and IoT-based vulnerabilities. The findings highlight the critical need for AI-driven cybersecurity solutions capable of evolving alongside emerging threats, ensuring the safety and reliability of interconnected digital ecosystems.