Threat intelligence and signature-based defenses are proving ineffective at stopping today's advanced cyber criminals. If your organization is a likely target, you should be operating on the assumption that you have already been hacked.
But even the most advanced criminals leave fingerprints in the form of unusual software connecting to networks, anomalous access, and abnormal data traffic patterns. The problem is that it is not possible to invest the time needed to find these activities using traditional rule or signature-based approaches.
Prelert behavioral analytics offers a new way forward. Machine learning algorithms automatically determine normal behavior patterns for hundreds of thousands of data points. Automated anomaly detection provides early detection of the suspicious behavior patterns that security analysts need to know about. Forensic analysis times are slashed. And the result is you see threats as they develop and stop them dead in their tracks.