Evaluating AI and ML in Cybersecurity: A USA and global perspective

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity represents a paradigm shift in the approach to defending against evolving cyber threats. This paper provides a succinct overview of the evaluation of AI and ML applications in cybersecurity, examining perspectives both within the United States and globally. The United States, as a forefront leader in technological innovation, has witnessed a rapid adoption of AI and ML solutions to enhance cybersecurity measures. From advanced threat detection to real-time incident response, these technologies have demonstrated their efficacy in augmenting the capabilities of cybersecurity professionals. This paper explores the specific use cases, challenges, and successes of AI and ML applications within the diverse landscape of cybersecurity in the United States. On a global scale, the evaluation extends beyond national borders, encompassing the diverse cybersecurity strategies and challenges faced by countries worldwide. The paper delves into the varying degrees of AI and ML integration, highlighting the shared benefits and unique considerations for different regions. The global perspective emphasizes collaborative efforts, information sharing, and the role of international partnerships in addressing cyber threats collectively. Throughout this evaluation, key themes such as the ethical implications of AI and ML, the need for explainability in automated decision-making, and the continuous evolution of cyber threats are explored. The paper concludes by emphasizing the significance of ongoing research, collaboration between nations, and the establishment of ethical frameworks to ensure responsible and effective integration of AI and ML in global cybersecurity efforts.