LogLM vs. ML

Comparing Deep Learning-Powered Tempo LogLM with Traditional ML/UEBA Solutions

Traditional machine-learning approaches in cybersecurity rely upon many different models which are selected and trained by engineers to work in specific environments. In contrast, DeepTempo’s Tempo LogLM is a Foundation Model pretrained on vast amounts of log data, resulting in superior accuracy, rapid adaptability, and improved explainability.

UEBA Comparison

The table below compares Tempo LogLM against leading UEBA solutions across key attributes such as Accuracy, Adaptability, Extensibility, Scalability, Explainability, Ease of Deployment, Operations Cost, and Visibility.

Attribute Tempo Securonix Splunk UBA Exabeam IBM QRadar
Accuracy A B B B C
Adaptability A B B B C
Extensibility A B C B B
Scalability A A A A A
Explainability A B B B C
Ease of Deployment A B C B C
Operations Cost A B C B C
Visibility IC A A A A

Model Criteria

While Accuracy is crucial for security operations, so too are Adaptability and Explainability.

  • Accuracy: Both low false positives and low false negatives are crucial. Low false positives reduce the burden on your security team, while low false negatives indicate how effective the model is in protecting your organization.
  • Adaptability: Foundation models like a LogLM quickly transfer knowledge from previous environments to new ones, reducing the time to value and minimizing the operational burden of retraining.
  • Explainability: For security teams to act on alerts, LogLMs must provide clear context—such as impacted entities and correlations with MITRE ATT&CK patterns.
Accuracy Adaptability Explainability Effectiveness