AI-Powered Insider Threat Detection

Any employee using privileged credentials inside a network to collect and exfiltrate valuable data is an insider threat. It is much harder to detect malicious data theft by someone with legitimate credentials in your systems, because an insider does not have to install malware or probe your network's perimeter for weaknesses, the way hackers do.

An insider's behavior looks nearly identical to an outside threat. One difference is that the insider is much more likely to conduct activities from a single machine, rather than hopping around laterally to mask suspicious actions. The insider still needs to find high-value assets and determine how to exfiltrate them. The most effective way to spot an authorized user doing bad things is by tracking user behavior across the adversary campaign lifecycle, just as you would with an Advanced Persistent Threat (APT).

Threat Campaign Lifecycle: Planning, Access, Recon, Collection and Exfiltration
Threat Campaign Lifecycle: Planning, Access, Recon, Collection and Exfiltration.

An Insider Threat's behavior in your environment is nearly identical to that of an outside hacker or APT. They have a mission to complete, which generally involves finding important data (recon), bringing it together (collection), and removing it from the network (exfiltration). Detecting and connecting these behaviors is the most effective way to curb insider threats.

Versive Security Engine Threat Viewer
Versive™ Security Engine ThreatCase™ Viewer Presenting An Insider Threat Case

How Versive Detects Insider Threats

The Versive Security Engine (VSE) uses adaptive machine learning on your data, automatically customizing itself to your environment, to assure total accuracy in detecting suspicious behavior. The key is pinpointing behaviors that are unusual, both in the context of your unique environment and relative to the mission of the adversary. This is an enormous challenge given the variability in behavior and sheer volume of corporate data. What looks like suspicious data collection in one environment might be normal internal sharing in another. This is why machine learning is critical to the future of cybersecurity.

Here is how the Versive Security Engine detects Insider Threats in your environment:

1. Learn The Environment The Engine uses adaptive machine learning on data sources from across your network and logs, as well as supplementary security tools like endpoint monitoring, to learn what “normal” behavior looks like in your unique environment.

2. Suspicious Behavior Detection The Engine models behaviors that are strong indicators of advanced adversaries in your environment.

3. Build ThreatCases: By connecting suspicious behaviors among multiple hosts across the Recon, Collection, and Exfiltration stages of the adversary campaign, the engine can track and visualize the progress of a threat over time, with a high degree of confidence.

4. Deliver Results The ThreatCase Viewer presents intuitive cases that represent coherent, well-documented threats that are straightforward to investigate. You can view results in the UI or access the data through the API to use in your existing tools.

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