Artificial intelligence and machine learning get all the marketing buzz, but First Data's Jaclyn Blumenfeld cites examples of use cases where these emerging technologies are actually transforming security and fraud management.
While unsupervised machine learning techniques get away from the data labeling and classification that most supervised systems require, they are dependent on the quality and variety of the data provided, says Gartner's Jonathan Care.
Identity and access management is not about compliance anymore - It's really about security, says Gartner's Felix Gaehtgens. With cloud, virtualization, DevOps and other IT trends, IAM has evolved from being a one-off project to an ongoing initiative.
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