Objective
Security Information and Event Management systems (SIEM) are crucial for detecting and responding to cyber threats, but we often lack full visibility into the data they collect. Without a clear understanding of their monitoring capabilities, which leads organisations to struggle optimising security operations, identify coverage gaps, and enhance threat detection.
SIEVA is a SIEM visibility analysis tool that helps organisations gain a clear and comprehensive view of their security monitoring landscape. By leveraging Natural Language Processing (NLP), SIEVA classifies raw log data, mapping it to the MITRE ATT&CK framework. This allows security teams to assess their visibility in a structured and actionable way, ensuring that their SIEM configurations align with real-world threats.
SIEVA empowers organisations to:
- Understand monitoring coverage through a detailed, colour-coded MITRE ATT&CK matrix.
- Improve monitoring strategies by identifying and addressing visibility gaps.
- Optimise resource allocation by streamlining data integration and security rule development.
- Improve long term data integration planning needs
SIEVA is designed to help cybersecurity professionals:
- Gain insights into SIEM logs to fine-tune detection capabilities.
- Evaluate security data sources and improve monitoring strategies.
- Enhance SIEM visibility assessments for multiple clients.
You can find available below in GitHub the first relsease