How DeepTempo is different

Deep learning that reveals AI-attacks NDRs and SIEMs never see

DeepTempo analyzes flow logs to identify malicious activity that appears normal to traditional threat detection systems.

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The challenge

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.

OUR approach

Deep learning FTW

DeepTempo turns flow records into intent signals mapped to MITRE TTPs, exposing attacker behavior long before traditional systems react.

Foundation model for threat detection

LogLM is DeepTempo’s deep learning model trained to understand network flow. It projects groups of flow records into embeddings that capture structure and meaning. A classifier then uses 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 interprets what a group of flow records 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 flow records 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 foundational model adapts as services, workloads, and identities change, without rules, thresholds, or retraining. Accuracy improves as the model encounters more environments.

  • Adapts automatically as attack behaviors evolve
  • Avoids drift as services, users, and workloads shift
  • Maintains high accuracy over time
Comparing detection approaches

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

What they look at:
Known patterns and payloads
What they miss:
Minor mutations, timing changes, encryption
What DeepTempo sees:
Intent across the full activity pattern, even when every step looks normal

Baselines and UEBA

What they look at:
Behavior drift, thresholds, deviations
What they miss:
Slow credential misuse and low-volume lateral movement
What DeepTempo sees:
Slow credential misuse and low-volume lateral movement

Correlation & SIEM logic

What they look at:
Fragmented events and rule chains
What they miss:
Sequence context and attacker progression
What DeepTempo sees:
Semantic patterns that reveal recon → pivot → movement early

NDR heuristics

What they look at:
Metadata, anomalies, simple timing heuristics
What they miss:
Encrypted traffic and subtle east-west movement
What DeepTempo sees:
Flow semantics that reveal technique intent even in benign-looking traffic
Deep impact

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.

Advantage
Description
Reduces risk
Detects and defeats AI-powered, evasive, machine-speed attacks
Reduces impact
Catches attackers while they are probing, scanning, or staging before damage occurs
Reduces effort
Continuously adapts with no signatures, tuning, or content updates
Reduces gaps
Works across cloud, data center, OT, remote, and east-west traffic
Reduces cost
Lowers SIEM storage costs and increases SOC productivity without adding headcount
Faq

Common questions teams ask

Do we need to change our logging or telemetry?

No. DeepTempo uses the flow data your environment already produces. No agents, sensors, or packet inspection are required.

Does DeepTempo replace our SIEM or NDR?

No. It sits alongside them and adds intent-level signal. You keep your existing alerts and workflows.

How long until we see useful detections?

DeepTempo provides value on day one. The model operates zero-shot and gets sharper as it sees more activity.

Will this add tuning or maintenance work?

No. There are no rules, signatures, thresholds, or baselines to maintain. DeepTempo adapts automatically as environments change.

Why do rules and signatures miss modern attacks?

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.

Why do baselines and UEBA miss slow or low-volume attacks?

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.

Why does SIEM correlation miss multi-step attacker progression?

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.

Why do NDR heuristics miss attacks in encrypted or low-signal traffic?

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.

How is DeepTempo different from UEBA or anomaly detection?

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.

Can DeepTempo detect AI-powered or rapidly mutating attacks?

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.

See what your detections are missing

Run a 30-day assessment to let DeepTempo analyze your flow data. We’ll surface attacker intent and early-stage behaviors that may already exist in your environment.

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