Recent advancements in machine learning, deep learning and cognitive security have made artificial intelligence an essential tool for cybersecurity teams.
A platform approach to security monitoring empowers analysts to take their SIEM to the next level with advanced threat detection and response capabilities.
An automated security analytics platform can help understaffed security teams sift through threat data more efficiently and focus on more critical tasks.
Security monitoring and analytics platforms deliver business value by reducing the time it takes to identify, investigate and remediate threats.
An evolved security monitoring and analytics platform — as opposed to a tools-based approach — can help analysts make better use of available threat data.
In the Industry 4.0 era, mainframe security is supported by four key areas: big data, analytics, human-machine interaction and cognitive computing.
To deliver the level of personalization today's prosumers demand, utilities must support their energy security strategies with predictive analytics.
Given the growing number of connected devices, organizations are beginning to leverage IoT data analytics to drive better decision-making.
User entity behavioral analysis (UEBA) can provide analysts with actionable insights and early warnings of threats, much like a canary in a coal mine.
Intelligent Code Analytics: Increasing Application Security Testing Coverage With Cognitive Computing
With intelligent code analytics, developers can use machine learning to mark up APIs and bring application security testing to the next level.