Advanced Machine Learning Techniques for Cybersecurity: Enhancing Threat Detection in US Firms

Abstract

US corporations' computing technologies are evolving towards new technologies to detect, respond, and prevent new threats using sophisticated machine learning (ML) methods for their cybersecurity systems. To be sure, machine learning is not a silver-bullet solution, but it does have speed, scalability, and pattern detection capacity which have no match. Robust cybersecurity is built on a multi-faceted strategy incorporating cutting-edge machine learning models with traditional countermeasures and human expertise. By collaborating, engineers, legislators lawyers can ensure safe and responsible execution in business, especially in the high-stakes world of US companies. This paper describes how machine learning (ML) can enhance threat detection systems, enabling enterprises to move from reactive to proactive defense strategies. But beyond the effectiveness of the technologies, we emphasize the need for accountability, transparency and ethical governance in deploying these technologies. Finding the right spot for the combination of machine learning's computational capabilities without abandoning decisions because of any relationship remains part of ethical assessment and passive strategy. But, as attacks become more complex, we need our defenses to do the same. However, this study uses the power of machine learning to study more and implement it correctly so US companies can create a resilient and agile cybersecurity solution that will safeguard their digital assets in an increasingly interconnected world.