The Prediction and Detection Layer that reveals what NDRs and SIEMs can't see
DeepTempo adds an intelligent prediction and detection layer that analyzes operational telemetry — from flow logs and application behaviors to WAF data and threat intelligence — to surface malicious activity hidden inside what looks like normal operations.
Modern attacks evade traditional detection
Attackers hide inside normal looking activity. Slow privilege drift, low-volume movement, and quiet C2 traffic blend into what rules, signatures, baselines, and NDR heuristics treat as benign.
Rules and signatures
Single event triggers miss multi step behavior and timing differences.
Baselines and UEBA
Drift widens thresholds and hides gradual identity misuse.
Correlation and SIEM logic
Fragmented views lose the sequence and distort intent.
Packet and NDR heuristics
Encrypted traffic and low frequency lateral movement produce no anomalies.
Deep learning FTW
DeepTempo delivers a prediction and detection layer that transforms operational telemetry into intent signals mapped to MITRE TTPs — exposing attacker behavior long before traditional systems react.
Foundation model for threat detection
The LogLM is DeepTempo's vertical foundation model, purpose-built for security. It projects groups of log records into embeddings that capture structure and meaning. Classifiers then use these embeddings to assign MITRE TTPs.
- No rules, baselines, or tuning required
- Embeddings expose relationships hidden in raw flow logs
- Classifier provides precise TTP labels for faster triage
From rules and anomalies to intent
Most systems either match patterns (rules, signatures) or flag deviations (UEBA, ML). DeepTempo's LogLM interprets what an attacker is trying to accomplish, not whether it looks unusual.
- Detects intent behind the activity, not surface symptoms
- Works even when traffic looks normal or rule-compliant
- Reveals progression early across recon, pivots, and movement
Sees early attacker progression
By turning groups of logs into embeddings and labeling them with MITRE TTPs, DeepTempo exposes attacker movement that blends into normal operations. This makes early-stage activity visible long before escalation.
- Detects recon, credential use, pivots and lateral movement early
- No rule packs, signatures, or tuning
- Reveals malicious intent in normal looking traffic
Learns continuously and adapts
DeepTempo's prediction and detection layer adapts as services, workloads, and identities change, without rules, thresholds, or retraining. Existing detections are augmented with end to end validation and adaptation.
- Adapts automatically as attack behaviors evolve
- Measures accuracy of both existing detections and LogLM detections
- Maintains high accuracy over time
DeepTempo closes your detection gaps
Rules, baselines, correlation logic, and heuristics fail for different reasons. DeepTempo closes each gap by identifying attacker intent early.
Rules & signatures
Baselines and UEBA
Correlation & SIEM logic
NDR heuristics
What DeepTempo means for your detection engineering
DeepTempo augments your existing SIEM, NDR, and cloud tooling with intent-level detection that stays effective against modern, adaptive, AI-powered attacks and makes early-stage attacker behavior visible without rules, tuning, or changes to your architecture.
Common questions teams ask
DeepTempo works with the telemetry your environment already generates, including flow logs, Layer 7 logs, WAF logs, SQL logs, and major threat intelligence feeds. No agents, packet inspection, or infrastructure changes are required.
DeepTempo operates alongside your existing SIEM and NDR as an additional prediction and detection layer. Existing workflows stay in place while DeepTempo adds intent-level analysis and improved detection validation.
DeepTempo provides detection value immediately through zero-shot capabilities and continuously improves as it learns more about your environment over time.
DeepTempo does not rely on rules, signatures, thresholds, or manually maintained baselines. The platform continuously adapts automatically as environments evolve.
Rules and signatures only identify known patterns. Modern attackers use timing variation, encryption, automation, and small mutations to evade static detection logic. DeepTempo identifies attacker intent across behavioral activity patterns instead of relying on fixed signatures.
Traditional baselines often expand over time, causing gradual misuse and low-volume attacker activity to appear normal. DeepTempo uses behavioral embeddings that surface subtle malicious intent without relying on thresholds alone.
Correlation engines analyze fragmented events independently and often lose broader sequence context. DeepTempo preserves behavioral relationships across reconnaissance, pivoting, lateral movement, and escalation activity to identify attacks earlier.
Many heuristic approaches depend on obvious anomalies or payload visibility. Encrypted traffic and subtle east-west movement often appear benign. DeepTempo analyzes behavioral flow semantics to uncover malicious intent even when traffic patterns look normal.
UEBA and anomaly detection focus primarily on identifying unusual behavior. DeepTempo focuses on understanding the objective and intent behind activity, helping identify early-stage attacker behavior that blends into normal operations.
Yes. AI-generated attacks constantly modify timing, infrastructure, and execution patterns to evade traditional detection methods. DeepTempo focuses on attacker intent and behavioral structure, making these mutations far less effective at avoiding detection.
