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.
In a recent Ponemon survey on cyber resilience, respondents reported that both the time to respond to attacks and the severity of the attacks have increased. They also reported that the number one barrier to cyber resilience is the "lack of investment in new cybersecurity technologies, including artificial...
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.
The hype around artificial intelligence (AI) and machine learning (ML) has exploded, sometimes overshadowing the real uses and innovations happening every day at organizations across the globe. The reality is that applying AI and ML to data-dependent challenges presents the opportunity for better security, faster...
With stretched security teams struggling to keep up with evolving cyber-threats, autonomous response technologies leveraging AI are transforming how quickly and effectively human security teams respond to emerging threats.
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Why legacy incident response strategies are...
Amidst an era of evolving cyber-threats, legacy security approaches are being replaced by applications of machine learning and AI. While machine learning has the power to transform cyber defense, the challenge of getting it to work at scale, in a variety of dynamic data environments, without human intervention, is not...
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
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