During the recent IBM Resilient year-end webinar, expert panelists discussed and debated the trends that defined 2018 and offered cybersecurity predictions on what the industry can expect in 2019.
When used as part of the software development process, machine learning can help identify vulnerabilities before threat actors have a chance to exploit them.
Mobile threats are growing both in number and severity. To protect crucial data, organizations need mobile threat defense solutions that can replicate the accuracy of manual analysis on a large scale.
Before you can choose the right machine learning algorithms to serve your business' needs, you must understand the type of problem you're trying to solve and the type of training data you'll need.
IT automation is the future of security programs, but it's not simply plug-and-play. SOC leaders must implement this technology thoughtfully to unlock the full range of benefits.
If you've ever gotten a financial fraud alert from your bank, you can thank Daniel Gor for developing the automated processes by which fraud analysts monitor customers' behavioral patterns.
If you're thinking about adopting artificial intelligence as an ally in your security operations center, the following questions and considerations can be helpful to guide your decision-making.
Dimitry Snezhkov didn't touch a computer until he was 18. Now he spends his days penetration testing to uncover security gaps and his nights meditating on the balance of life.
With a corporate culture that supports transparency and human agency, it's possible to maximize the existing benefits of artificial intelligence (AI) while laying the groundwork for the future of AI.
User behavior analytics (UBA) can help security teams uncover ignorant, negligent and malicious activity with advanced machine learning algorithms — but Rome wasn't built in a day.