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.
When taking steps to guard against fraudulent transactions through contactless payments, organizations must carefully balance the level of security versus customer convenience, says Sriram Natarajan, COO at Quatrro.
Artificial intelligence and machine learning will have a significant impact on lowering the cost of securing an organization because it will reduce the need for advanced skillsets, predicts Rapid7's Richard Moseley.
Data science is playing a fundamental role in a more dynamic approach to cybersecurity, says Jim Routh, CISO of Aetna, who stresses the importance of applying machine learning to front-line data security controls. Routh will be a featured speaker at the ISMG Security Summit in New York Aug. 14-15.
To have any hope of keeping up "with the exponential rise in variants in malware," organizations must reduce their attack surface, in part by using technology designed to learn what attacks look like and respond as quickly as possible, says Cylance's Anton Grashion.
Security experts warn that hackers could one day make use of machine learning and AI to make their attacks more effective. Thankfully, says Cybereason's Ross Rustici, that doesn't appear to have happened yet, although network-penetration attacks are getting more automated than ever.