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ML can help security teams perform better, smarter, and faster by providing advanced analytics to solve real-world problems, such as using ML UEBA to detect user-based threats.

Fremont, CA: Machine learning (ML) and artificial intelligence (AI) are popular buzzwords in the cybersecurity industry. Security teams urgently require more automated methods to detect threats and malicious user activity, and machine learning promises a brighter future. Melissa Ruzzi offers some pointers on how to bring it into your organization.

Cybersecurity is undergoing massive technological and operational shifts, and data science is a key component driving these future innovations. Machine learning (ML) can play a critical role in extracting insights from data in the cyber security space.

To capitalize on ML's automated innovation, security teams must first identify the best opportunities for implementing these technologies. Correctly deploying ML is critical to achieving a meaningful impact in improving an organization's capability of detecting and responding to emerging and ever-evolving cyber threats.

Driving an AI-powered Future

ML can help security teams perform better, smarter, and faster by providing advanced analytics to solve real-world problems, such as using ML UEBA to detect user-based threats.

The use of machine learning to transform security operations is a new approach, and data-driven capabilities will continue to evolve in the coming years. Now is the time for organizations to understand how these technologies can be deployed to achieve greater threat detection and protection outcomes in order to secure their future against a growing threat surface.

Machine Learning and the Attack Surface

Because of the proliferation of cloud storage, mobile devices, teleworking, distance learning, and the Internet of Things, the threat surface has grown exponentially, increasing the number of suspicious activities that are not necessarily related to threats. The difficulty is exacerbated by the large number of suspicious events flagged by most security monitoring tools. Teams are finding it increasingly difficult to keep up with suspicious activity analysis and identify emerging threats in a crowded threat landscape.

This is where ML comes into play. From the perspective of a security professional, there is a strong need for ML and AI. They're looking for ways to automate the detection of threats and the detection of malicious behavior. Moving away from manual methods frees up time and resources, allowing security teams to concentrate on other tasks. They can use ML to use technologies beyond deterministic rule-based approaches requiring prior knowledge of fixed patterns.

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All You Need to Know about the Growing Role of Machine Learning in Cybersecurity - CIO Applications