AI-Powered Data-Exfiltration Early Warning

If you can stop an attacker before they move data out of your environment, or catch them in the act and cut them off, you've got a much better chance of protecting your reputation, valuable intellectual property, and bottom line.

Versive AI can understand normal and abnormal data flow patterns on your network, enabling you to discover clear indicators of exfiltration preparation.

By tracking an adversary's behaviors along the entire campaign lifecycle, you increase your chances of catching them before they reach the exfiltration step of the mission.

How Versive Predicts Data Exfiltration in Time to Stop it

The Versive Security Engine uses adaptive machine learning on your internal data to track behaviors that indicate a malicious actor is searching for, collecting, and staging data for exfiltration. By connecting these behaviors across time, the Engine can build a visual Threat Case to show you which hosts or collections of hosts are showing signs of data reconnaissance and collection, and alert you before exfiltration occurs.

Here’s what the Versive Security Engine does:

1. Learns what constitutes normal data movement between hosts, clients, servers, and anything else connected to your network, and then uses adaptive machine learning to model normal behavior in your environment.

2. Discovers behaviors that stray from normal ranges in ways that are indicative of data reconnaissance, collection, or exfiltration from the network.

3. Connects these unusual behaviors indicating threat activities over time, and corroborates them across data sources, for a high level of statistical confidence that someone is acting with malicious intent.

4. Builds an easy to read, data-rich, and high-confidence Threat Case, indicating that an adversary is preparing to exfiltrate valuable data.

With your Threat Case in hand, you can rapidly investigate the situation and prevent data exfiltration, or immediately shut down exfiltration in progress, saving your company the embarrassment and financial loss of a major data-breach.

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