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.
A new foundation model for threat detection
Converts operational data to intent-aware detection and self-learns
Precise, adaptive, and efficient detection at scale
Foundation model that detects malicious intent early in the kill chain
DeepTempo’s AI uses deep learning to recognize attacker intent directly from operational activity, initially flow logs, revealing malicious intent even when hidden inside encrypted flows. This significantly expands your threat coverage — catching advanced, AI-driven cyberattacks legacy SIEM and NDR tools miss.
Key features:
- Detects attacker intent from flow data with extreme accuracy
- Identifies malicious activities early in attack progression
- Uncovers C2 channels and covert infrastructure, even when encrypted
- Integration with threat intelligence as an optional signal
Reveals threat context that matters
DeepTempo delivers reliable detections complete with context that is immediately actionable by SOC analysts. Each detection includes clear reasoning and is mapped to MITRE ATT&CK mapping so analysts can trust the results and act faster.
Key features:
- Contextual explanations for each detection
- Sequence of events, and entities involved
- ATT&CK mapping for faster triage and response
- ChatOps for natural language investigation
Auto-tunes to your environment
DeepTempo applies self learning to generalize across environments. It starts strong with high zero-shot accuracy on day one and automatically adjusts to new tools, workloads, and infrastructure. This reduces tuning burdens and delivers high accuracy at scale. No model maintenance required.
Key features:
- 85% zero-shot accuracy on the first deployment
- Self-calibrating model improves performance over time
- Minimal manual tuning overheads or training costs
- Collective defense improves results globally
Consistent protection across every environment
DeepTempo delivers agentless threat detection across cloud, data center, remote, and OT environments. It detects east-west movement, maps communication paths automatically, and scales to petabytes of log data — ensuring attackers can’t hide in internal traffic.
Key features:
- Flow-based visibility across hybrid environments
- East-west detection without any agents to deploy
- Automatic communication mapping for attack path discovery
- Scales to petabyte-scale deployments
Stronger protection, lower operational costs
By operating directly on your data lake, and filtering high-value signal before it reaches your SIEM, DeepTempo cuts ingestion and storage costs without losing visibility. Detection engineers spend less time tuning and more time responding, with optional Slack-based ChatOps for rapid collaboration.
Key features:
- Smart telemetry reduction lowers SIEM cost
- No manual rule writing or retraining
- Faster investigation and resolution cycles
- Slack integration for quick analyst collaboration
Proven accuracy and scale in large enterprise environments
Model Performance
DeepTempo’s LogLM architecture has shown consistent, verifiable results across controlled customer environments, proving that deep learning-based threat detection can outperform rule-based systems in both accuracy and operational efficiency.
- 99% detection rates for most common TTPs (e.g. Command & Control)
- 85%+ accuracy on day one, improving to 94%+ after adaptation
- Less than 5% false positives, significantly reducing alert noise
- Sub-second detection latency across petabytes of data
- Up to 45% lower SIEM cost through telemetry reduction
Works with your existing stack
DeepTempo integrates with your existing cloud, security stack, SIEM, and data lake infrastructure, running upstream of your detection and response systems.