The threat landscape is expanding, and organizations must undergo a cognitive convergence to manage evolving security, fraud and operational risks.
Machine learning algorithms can help security teams improve decision-making while conducting user access review and cleanup projects.
When it comes to protecting the railroad industry from cyberthreats, the security immune system approach can reduce incident triage from months to minutes.
To close common security gaps in the oil and gas industry, IT leaders should educate employees, better manage remote devices and eschew standard products.
Cognitive security solutions help security teams distinguish valuable threat data from noise on the network and respond to incidents more efficiently.
Machine learning can be a boon for businesses, but effective machine learning must help analysts cut through the noise with few false positives.
IBM and MIT have announced the foundation of a new AI lab. Its research will focus on developing four key areas, including advancing core AI algorithms.
To protect their networks from malicious insiders, user negligence and other threats, CISOs need advanced machine learning capabilities such as UBA.
While it promises to improve quality of life across the globe, many are resistant to widespread cognitive adoption due to fear of change and other factors.
Recent advancements in machine learning, deep learning and cognitive security have made artificial intelligence an essential tool for cybersecurity teams.