OpenText Network Detection & Response

OpenText Network Detection and Response analyzes network behavior and traffic patterns to detect lateral movement, command and control activity, and other anomalies missed by signature-based tools like firewalls or antivirus.

Yes. OpenText Network Detection and Response uses machine learning and behavioral analysis to correlate signals, suppress noise, and escalate only meaningful, high-fidelity alerts.

OpenText Network Detection and Response provides enriched metadata, visual timelines, packet capture, and integration with MITRE ATT&CK to speed up triage and root cause analysis.

Yes. OpenText Network Detection and Response offers encrypted traffic analysis using flow data and metadata to identify anomalies without decrypting payloads—maintaining privacy and compliance.

OpenText Network Detection and Response offers APIs, syslog, or native integrations to feed alerts and context into tools like Splunk®, QRadar®, Sentinel®, CrowdStrike®, or Palo Alto Networks Cortex®.

Baseline modeling starts immediately after deployment, with meaningful detections often within days. Some tuning may be needed for environment-specific behaviors.

Unlike conventional tools that may rely solely on signature-based detection, OpenText Network Detection and Response employs multiple detection engines, including anomaly detection and machine learning, to identify both known and unknown threats, reducing false positives and enhancing threat visibility.

Yes, it can analyze encrypted traffic by examining metadata and behavioral patterns without decrypting the content, maintaining privacy while detecting anomalies.