There is a pernicious myth floating around. It's that rule-based Security Information and Event Management (SIEM) is old technology, and is no longer worth using today. It's that modern attacks can bypass rule-based SIEMs. This is partly true, but mostly false. There are large, older companies out there that don't use SIEMs to defend their data, and they do so at their own risk.

The Gartner Security & Risk Management Summit is just a few days away, and I'm delighted to have the opportunity to chat with attendees about how anomaly detection and machine learning can help give your organization a more proactive security posture. You don't need to have been in the cybersecurity space for long to be bewildered by and unsure about vendor claims around artificial intelligence, machine learning, and analytics. At Interset (acquired by Micro Focus in February of this year), we have regular conversations with security professionals who struggle to understand which techniques and tools are effective in boosting breach defense in the real world. Ultimately, these conversations lead to an important question for us: How can you implement user and entity behavioral analytics (UEBA) in a way that will enable an efficient security operations center (SOC)? There are multiple factors that go into an effective UEBA implementation, but it's helpful to start with ensuring that the math and machine learning powering the solution are suitable for your security objectives.

Ben Dickson is a software engineer and the founder of TechTalks. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs. Data science -- the application of mathematics, big data analytics and machine learning to extract knowledge and detect patterns -- is an emergent, advanced technology area that is proving its effectiveness in the realm of cybersecurity, including fighting insider threats.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs.