Supervised machine learning can free up security analysts to respond to actual threats instead of sifting through endless streams of false positives.
IBM Security brought home multiple prestigious awards, hosted well-attended events and continued to grow in the U.K. and Europe in 2016.
Two researchers performed an experiment based on billions of log lines that demonstrated the importance of domain expertise in machine learning analysis.
AI2 uses an "analyst-in-the-loop" system to improve itself and a "human-in-the-loop" system to create examples to be used in iterative training algorithms.
IBM is working to help companies address the challenges they face when adopting a more proactive approach to incident response.
Augmented intelligence solutions enable security analysts to examine previously unthinkable amounts of data with the helps of cognitive technology.
Many organizations are adopting cognitive security solutions to boost response speed, security intelligence and accuracy, while others are lagging behind.
Machine learning systems like A12 are designed to augment human analysis with cognitive intelligence, enabling IT professionals to reduce false positives.
The emergence of cognitive computing is driving the future of security, enabling human analysts to make better decisions when responding to data breaches.
Cybersecurity leaders hope to use cognitive security solutions to improve detection and incident response capabilities and reduce false positives.