detects evasive attacks

Deep learning threat detection that identifies attacker intent

DeepTempo uses deep learning to understand how your systems routinely operate and identify attacker intent as it forms. It detects evasive attacks including AI-driven cyberattacks, across cloud, data center and OT environments, before they escalate.

See it in action
How it works

A new foundation model for threat detection

Converts operational data to intent-aware detection and self-learns

LogLM foundation model

LogLM is DeepTempo’s foundation model, built using transformer-based deep learning for threat detection. Trained on large-scale operational data, initially flow logs, it learns how systems communicate and evolve over time. The result is a semantic understanding of what “usual” looks like across cloud, data center, and OT environments.

Real-time classifiers

Lightweight classifiers operate on top of LogLM embeddings to identify malicious intent and map detections to MITRE ATT&CK techniques such as Command & Control, Credential Access, and Exfiltration. These classifiers turn deep learning insights into clear, actionable detections in real time.

Adaptive learning

DeepTempo continuously refines its understanding through adaptive learning. It recognizes new behaviors, adjusts to environment drift, and improves detection accuracy — without rule tuning or retraining. The system grows sharper with every deployment.

What capabilities does it offer

Precise, adaptive, and efficient detection at scale