As companies go through a digital transformation, they need to move toward real-time risk management - and artificial intelligence can play a critical role, says David Walter, vice president of RSA Archer.
Organizations can effectively rely on managed security services providers to take care of many tasks, but certain strategic security functions must be handled in-house, says Sid Deshpande, research director at Gartner.
Machine learning could be a breakthrough for data classification, addressing fundamental challenges and paving the way to create and enforce automated policies that can be scaled across the enterprise, says Titus CEO Jim Barkdoll.
CISOs should ask tough questions of vendors that claim to offer machine learning and artificial intelligence capabilities so they can cut through the marketing hype to find out what's real, says Sam Curry of Cybereason.
The EU's General Data Protection Regulation, which has tough breach notification requirements, is spurring global interest in technologies to help prevent insider breaches, says Tony Pepper of Egress Software Technologies.
Machine data and machine learning have the potential to connect disparate data sources, enabling better fraud detection and prevention, says Matthew Joseff of Splunk, who highlights real-world examples of fighting fraud with better data.
Unsupervised machine learning is essential to mitigate the sophisticated cross-channel fraud techniques attackers are using to take advantage of the multiple silos and security gaps at financial institutions, says ThetaRay's James Heinzman
Cybercrime is a business and, like any business, it's driven by profit. But how can organizations make credential theft less profitable at every stage of the criminal value chain, and, in doing so, lower their risk?
The best way to take a holistic approach to the current threat landscape is to define security issues as business problems and then put the problem before the solution - not the other way around, contends RSA CTO Zulfikar Ramzan.