Collective Deep Learning For Cybersecurity.
Collective Deep Learning For
Collective Deep Learning For
Cybersecurity
Cybersecurity.
Cybersecurity.
DeepTempo offers flexible deployment options both as a kubernetes solution and as a first of its kind Native App on Snowflake. Offering defense in depth, it adapts to fit various organizational structures and security requirements with increased cost savings and productivity.
Innovative Threat Protection
Innovative Threat Protection
Better protection from attacks with more defense in depth, without relying solely on known behaviors
Better protection from attacks with more defense in depth, without relying solely on known behaviors
Context For Operations
Context For Operations
Gain deep context, including discovered entities and MITRE ATT&CK mappings, for triaging incidents effectively
Gain deep context, including discovered entities and MITRE ATT&CK mappings, for triaging incidents effectively
Lower False Positives
Lower False Positives
Significantly reduce false positives and enhancing productivity with LogLMs that are extraordinarily accurate
Significantly reduce false positives and enhancing productivity with LogLMs that are extraordinarily accurate
Click to Try
Get started within minutes, with rapid production deployment. Available on the Snowflake Marketplace now
Freedom from Lock-in
Integrate with existing data lake, reducing vendor dependency while creating modular security solutions that adapt to your needs
Cost Savings
Only send incidents to your SIEM, reducing data volumes and resulting in significant cost savings
Data Security
Run where the data is, on premises or cloud, to reduce data risk by bringing intelligence to the data
Click to Try
Get started within minutes, with rapid production deployment. Available on the Snowflake Marketplace now
Freedom from Lock-in
Integrate with existing data lake, reducing vendor dependency while creating modular security solutions that adapt to your needs
Cost Savings
Only send incidents to your SIEM, reducing data volumes and resulting in significant cost savings
Data Security
Run where the data is, on premises or cloud, to reduce data risk by bringing intelligence to the data
Click to Try
Get started within minutes, with rapid production deployment. Available on the Snowflake Marketplace now
Freedom from Lock-in
Integrate with existing data lake, reducing vendor dependency while creating modular security solutions that adapt to your needs
Cost Savings
Only send incidents to your SIEM, reducing data volumes and resulting in significant cost savings
Data Security
Run where the data is, on premises or cloud, to reduce data risk by bringing intelligence to the data
Product Features
Product Features
Threat Detection
See deeper. Act faster. Spend less.
State of the art accuracy with advanced Deep Learning models
State of the art accuracy with advanced Deep Learning models
Runs on security datalake with enterprise-grade infrastructure
MITRE ATT&CKs indetified with high accuracy
Feeds into your SIEM w/ alters and additional context
Reduces SIEM spend with lower false positives
Sees attacks other solutions cannot
Runs on security datalake with enterprise-grade infrastructure
MITRE ATT&CKs identified with high accuracy
Feeds into your SIEM w/ alerts and additional context
Reduces SIEM spend with lower false positives
Sees attacks other solutions cannot
Threat Response & Forensics
Respond with confidence.
Threat Response & Forensics
Respond with confidence.
Leverages advanced embeddings for threat hunting
Streamlined threat hunting workflow
Similarity search identifies all related sequences
Precisely isolates sources of attack
Compatible with many other security platforms
Your threat hunters and IR teams will enjoy the UX
Leverages advanced embeddings for threat hunting
Streamlined threat hunting workflow
Similarity search identifies all related sequences
Precisely isolates sources of attack
Compatible with many other security platforms
Your threat hunters and IR teams will enjoy the UX
Where Do We Fit In The Enterprise?
Where Do We Fit In The Enterprise?
Tempo runs in your Datalake where your data lives. It can even run as a Snowflake Native App. Using Tempo will save you money on your SIEM, by sending incidents, not raw logs, into your SIEM.
Tempo runs in your Datalake where your data lives. It can even run as a Snowflake Native App. Using Tempo will save you money on your SIEM, by sending incidents, not raw logs, into your SIEM.
Powered By
Powered By
Powered By
From enterprise log ingestion to large-scale pre-training and real-time inference, our Tempo LogLM harnesses the NVIDIA stack to deliver unparalleled performance on security data—whether on-premise or in the cloud.
Inference
NVIDIA Triton Inference Server or NVIDIA Inference Microservices (NIM) deliver real-time threat detection—optimized with TensorRT for lightning-fast, GPU-accelerated inference on-prem or in the cloud.
Fine Tuning
We adapt Tempo LogLM to specific organizations or new security patterns using multi-GPU fine tuning (PyTorch, TensorFlow, etc.), accelerating updates with CUDA and NCCL.
Data Ingestion and Parsing
Morpheus ingests high-volume logs (e.g., NetFlow), while RAPIDS (cuDF, cuML) provides adaptive, GPU-accelerated parsing—keeping data on the GPU for maximum throughput and real-time speed.
Pretraining
Utilizing NVIDIA clusters (DGX servers or GPU-enabled data centers) and containers from NVIDIA NGC, we harness CUDA
and cuDNN to pretrain Tempo LogLM on vast corpora of security logs—ensuring a robust foundation for threat detection.
Inference
NVIDIA Triton Inference Server or NVIDIA Inference Microservices (NIM) deliver real-time threat detection—optimized with TensorRT for lightning-fast, GPU-accelerated inference on-prem or in the cloud.
Fine Tuning
We adapt Tempo LogLM to specific organizations or new security patterns using multi-GPU fine tuning (PyTorch, TensorFlow, etc.), accelerating updates with CUDA and NCCL.
Data Ingestion and Parsing
Morpheus ingests high-volume logs (e.g., NetFlow), while RAPIDS (cuDF, cuML) provides adaptive, GPU-accelerated parsing—keeping data on the GPU for maximum throughput and real-time speed.
Pretraining
Utilizing NVIDIA clusters (DGX servers or GPU-enabled data centers) and containers from NVIDIA NGC, we harness CUDA
and cuDNN to pretrain Tempo LogLM on vast corpora of security logs—ensuring a robust foundation for threat detection.
Model Criteria
Model Criteria
Model Criteria
While Accuracy is crucial for security operations, so too are
Adaptability and Explainability.
While Accuracy is crucial for security operations, so too are
Adaptability and Explainability.
While Accuracy is crucial for security operations, so too are Adaptability and Explainability.
Accuracy: Both low false positives and low false negatives are crucial. Low false positives reduce the burden on your security team, while low false negatives indicate how effective the model is in protecting your organization.
Adaptability: Foundation models like a LogLM quickly transfer knowledge from previous environments to new ones, reducing the time to value and minimizing the operational burden of retraining.
Explainability: For security teams to act on alerts, LogLMs must provide clear context—such as impacted entities and correlations with MITRE ATT&CK patterns.
Accuracy: Both low false positives and low false negatives are crucial. Low false positives reduce the burden on your security team, while low false negatives indicate how effective the model is in protecting your organization.
Adaptability: Foundation models like a LogLM quickly transfer knowledge from previous environments to new ones, reducing the time to value and minimizing the operational burden of retraining.
Explainability: For security teams to act on alerts, LogLMs must provide clear context—such as impacted entities and correlations with MITRE ATT&CK patterns.
Explainability
Accuracy
Adaptability
Effectiveness
Explainability
Accuracy
Adaptability
Effectiveness
Explainability
Accuracy
Adaptability
Effectiveness
Explainability
Accuracy
Adaptability
Effectiveness
Compare DeepTempo Against Competitors
Accuracy
Architecture
Forensics
Complexity
Learning
False Positives
False Negatives
Tempo
Adapted in minutes
Runs on a datalake, reduces lock-in
Traditional search AND search by pattern
One model
Pretrained
Can Achieve 1%
Sees “all” anomalies
Runs within a proprietary SIEM; locks customers in
Traditional search only
Thousands of Rules
TTPs -> Rules
False positives can be 40-50% or more
Cannot see novel attacks
Manual and hard to mantain
Rules
Typically requires agents on devices and proprietary datalayer, locking customers in
Traditional search only
Hundreds of Models
Dozens of models per user
Can take weeks of retraining
Can achieve 1%; brittle to changes in the environment
If signature based, cannot see novel attacks
Traditional ML
DeepTempo’s mission is to empower defenders through collective defense and deep learning.
Built by engineers and operators who’ve lived the challenges of security operations, we deliver open, AI-native software that runs on any data lake—freeing teams from legacy constraints. Our LogLMs return control to defenders, enabling faster, smarter, and more collaborative responses to cyber threats.
DeepTempo’s mission is to empower defenders through collective defense and deep learning.
Built by engineers and operators who’ve lived the challenges of security operations, we deliver open, AI-native software that runs on any data lake—freeing teams from legacy constraints. Our LogLMs return control to defenders, enabling faster, smarter, and more collaborative responses to cyber threats.
DeepTempo’s mission is to empower defenders through collective defense and deep learning.
Built by engineers and operators who’ve lived the challenges of security operations, we deliver open, AI-native software that runs on any data lake—freeing teams from legacy constraints. Our LogLMs return control to defenders, enabling faster, smarter, and more collaborative responses to cyber threats.