Turn EDR noise into actionable intelligence

Traditional endpoint investigations require manually correlating EDR logs with identity provider logs, cloud platform audit trails, SaaS activity logs, and network telemetry across multiple consoles and tools. This process typically consumes 2-6 hours per incident as analysts piece together fragments of the attack timeline. Exaforce eliminates this manual correlation by automatically unifying endpoint, cloud, identity, and SaaS telemetry into single attack timelines, constructing visual attack maps showing compromise progression across systems, surfacing related findings and evidence automatically through the Semantic Model, providing pre-investigated findings with clear recommendations rather than raw logs, and enabling natural language investigation where analysts ask questions instead of writing complex queries. Investigations that required hours of manual work complete in minutes with complete context and supporting evidence.

EDR solutions generate high false positive rates by flagging routine IT operations like legitimate software installations, administrative scripts, and normal Windows utility usage as suspicious activity. Exaforce applies AI-powered triage that validates EDR alerts against actual system behavior using the Behavioral Model to distinguish routine activities from genuine anomalies, incorporates business context like maintenance windows, approved software deployments, and expected administrative actions, and correlates endpoint events with identity authentication, cloud activity, and SaaS access patterns to confirm threat validity. Rather than forwarding every EDR alert for manual review, Exabots automatically dismiss false positives with supporting evidence while escalating only high-fidelity threats requiring analyst attention, reducing alert volume by 60-80%.

Raw EDR alerts typically contain only endpoint-level details like process names, file hashes, and registry changes without broader context about threat significance or business impact. Exaforce enriches each EDR alert by correlating endpoint activity with identity authentication events to determine which user was logged in, cloud resource access to identify what sensitive data or infrastructure was targeted, SaaS application usage to reveal lateral movement across systems, threat intelligence to match indicators with known attack campaigns, historical baseline data to show why this activity is anomalous, and automated investigation results with clear disposition recommendations and response steps. This transforms raw technical alerts into actionable incidents that analysts can act on immediately without hours of manual context gathering.

Raw EDR alerts typically contain only endpoint-level details like process names, file hashes, and registry changes without broader context about threat significance or business impact. Exaforce enriches each EDR alert with endpoint data by querying the EDR tools directly (e.g., CQL, S1QL) and adds cross-system correlations by correlating endpoint activity with identity authentication events to determine which user was logged in, cloud resource access to identify what sensitive data or infrastructure was targeted, SaaS application usage to reveal lateral movement across systems, threat intelligence to match indicators with known attack campaigns, historical baseline data to show why this activity is anomalous, and automated investigation results with clear disposition recommendations and response steps. This transforms raw technical alerts into actionable incidents that analysts can act on immediately without hours of manual context gathering.