Yes, Darktrace can differentiate between malicious and accidental insider activity using its Self-Learning AI. It builds a baseline understanding of normal user behavior, so when activity deviates from this pattern, it can identify potential threats.  

Malicious Insider Threats: These often involve deliberate actions like data theft, sabotage, or unauthorized access for personal gain.  

Accidental Insider Threats: These might include sending sensitive data to the wrong recipient or misconfiguring access controls due to negligence.  

While Darktrace may not always determine intent directly, it can infer risk levels based on the nature, context, and severity of the anomaly. For example, a user suddenly accessing sensitive files before resigning might be flagged as higher risk than someone who accidentally shares a document.

Darktrace’s Cyber AI Analyst runs continuous investigations on events, re-investigating existing alerts with emerging data to ensure thorough analysis. Each investigation produces detailed natural language summaries, providing security teams with clear decision logic and well-defined recommended actions to reduce false positives and speed up response efforts.

So, while Darktrace doesn't label actions as "malicious" or "accidental" per se, it provides the context and risk assessment needed for security teams to make that judgment.

Darktrace detects early signs of insider threats by continuously monitoring network and user behavior. The AI learns what is normal for each user, device, and service. Any deviation, such as a user accessing sensitive files they do not normally interact with or sending unusual amounts of data, is flagged as suspicious. By identifying subtle signs of malicious intent, Darktrace can detect threats before significant damage occurs. Autonomous Response functions also allow Darktrace to take action depending on the severity of the event to stop data loss all together.

Darktrace detects lateral movement or privilege escalation by monitoring patterns of behavior across the entire network. When an internal user attempts to move from one system or network segment to another, Darktrace can spot this lateral movement by analyzing unusual access patterns. Additionally, if a user’s privileges increase unexpectedly, such as gaining access to sensitive areas or systems they don’t typically use, Darktrace flags this behavior for investigation.

Darktrace is particularly effective at detecting insider threats in remote or shadow IT environments by monitoring network traffic and user behavior across both authorized and unauthorized systems. Even if an employee uses unsanctioned devices or connects to unauthorized cloud services, Darktrace can track these activities by analyzing unusual access patterns. The AI can detect non-compliant behaviors such as connecting to unauthorized apps or services, providing early alerts to the security team.

Yes, Darktrace can monitor the use of unsanctioned SaaS tools and personal devices connected to the network. Through its comprehensive behavioral analysis, it can detect when employees connect to or interact with unauthorized applications or devices. This includes monitoring network traffic, user access behavior, and the use of unsanctioned services. When an insider uses shadow IT or personal devices that are not authorized by the organization, Darktrace flags these activities as suspicious, enabling security teams to take action.