The Detection Layer that reveals what NDRs and SIEMs can't see
DeepTempo adds an intelligent 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 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 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
No. DeepTempo works with the telemetry your environment already produces — flow logs, Layer 7 logs like SQL and WAF, and threat intelligence feeds from major vendors. No agents, sensors, or packet inspection required.
No. DeepTempo operates as a detection layer that sits alongside your SIEM and NDR, adding intent-level signal and end to end validation of detection accuracy. You keep your existing alerts and workflows — they just get smarter and better managed.
DeepTempo provides value on day one. The model operates zero-shot and gets sharper as it sees more activity.
No. There are no rules, signatures, thresholds, or baselines to maintain. DeepTempo adapts automatically as environments change and will suggest and make ongoing improvements.
They only match what they’ve seen before. Small mutations, timing variations, or encryption evade them. DeepTempo identifies attacker intent across full activity patterns, not pattern syntax.
Threshold drift widens what looks “normal,” hiding gradual credential misuse and quiet pivots.
DeepTempo uses stable embeddings that expose subtle misuse without relying on thresholds.
Correlation works with fragmented events and must anticipate every variant. Sequence context gets lost. DeepTempo preserves semantics across activity and surfaces recon → pivot → movement early.
Heuristics depend on anomalies. Encryption and subtle east-west movement often show none.
DeepTempo reads flow semantics, revealing technique intent even when traffic looks benign.
UEBA asks “Is this unusual?”
DeepTempo asks “What is this activity trying to accomplish?”
Intent-level analysis exposes early-stage behavior that blends into normal baselines.
Yes. AI-generated attacks mutate timing and structure to evade rules and heuristics.
DeepTempo detects attacker intent, so mutations don’t hide the underlying objective.
