One Defense Platform for the workflows your SOC already runs
Threat intelligence, proactive threat hunting, incident identification, and incident response and forensics. One Defense Platform powered by LogLM, with Vigil, an open-source AI SOC project started by DeepTempo, included and pluggable so any AI SOC works.
Threat intelligence
DeepTempo analyzes telemetry from your existing security environment to identify suspicious behavioral patterns in real time. Instead of relying on manually maintained rules and static baselines, the platform continuously adapts as attacker techniques and operational environments evolve.
Predicts which infrastructure will be weaponized
External feeds describe other people's incidents and arrive after the campaign. Rather than replace the feeds you already trust, LogLM treats them as inputs, then predicts which infrastructure will be weaponized from behavioral signatures in your own telemetry, often hours before commercial feeds tag it.
Generates intelligence from your own telemetry
Internal intelligence usually costs analyst time no SOC has. The platform generates it automatically, with no hand correlation across SIEM, EDR, and NDR. Each finding feeds the learning loop, so the intelligence specific to you sharpens with every campaign it sees.
Keeps your telemetry in your environment
Detection and intelligence run where your data already lives, on-premise or in your own cloud, so raw telemetry never has to leave your environment to produce intelligence specific to you.
Proactive threat hunting
Hunt by behavior, continuously, at petabyte scale, directed by intelligence rather than analyst hypotheses alone.
Searches by behavior, not by signature
Because LogLM has already embedded your activity into a TTP-aligned vector space, a hunt becomes a vector query that returns in sub-second time where pattern-matching would take hours, and finds behavioral variants no signature describes.
Turns intelligence into proactive hunts
When a feed surfaces an emerging technique, the platform searches behaviorally for it across your environment, finding instances even when the specific indicators differ from those in the feed. Hunting becomes continuous, not confined to scheduled campaigns.
Scales your threat hunters
Senior hunters now oversee agents that use LogLM to intelligently hunt, with Vigil or your existing AI SOC running the campaign. The learning loop captures what each hunt finds, improving efficacy and efficiency as it goes.
Incident identification
Identify even rapidly evolving attacks through learned behavior across your existing telemetry. Ongoing efficacy and efficiency measurement can be applied to existing detections as well, for a comprehensive understanding.
Detects the sequence, not the single event
Instead of relying only on manually maintained rules and static baselines, the platform identifies incidents as sequences across telemetry. LogLM embeds activity into a TTP-aligned space and adds purpose-built classifiers, zero-shot on day one and sharper with exposure, so it adapts as attacker techniques and your environment evolve.
Catches what signatures miss
Most intrusions now leave no malware behind, and a growing share exploit zero-days. Learned behavior surfaces malware-free and novel activity that polymorphic and living-off-the-land techniques use to evade authored rules.
Measures the detections you already run
The platform can evaluate LogLM detections alongside your rules, your ML models, and your bespoke detections, identifying coverage gaps and monitoring decay. It improves your detection estate, not only the LogLM portion, and can adapt locally so raw telemetry never has to leave your environment.
Incident response and forensics
Begin response with context already assembled, and investigate any past window at the same fidelity as live detection.
Starts response with context, not a query
Most MTTR is reconstruction, not judgement. Because LogLM keeps activity embedded, every finding arrives as a story: source, destination, sequence, related behavior, and external reputation. Vigil, or your existing AI SOC, assembles that context from LogLM findings, cutting the six-to-twelve-hour window analysts spend rebuilding it.
Investigates any window in history
The historical record is a behavioral representation, not an aged-out archive. Today's model can run against last year's data to find attacks that were invisible at the time, with chain of evidence preserved for audit and counsel.
Replaces costly external engagements
Work that once required a forensic firm over weeks runs in-house and fast. The behavioral substrate stays where your data lives, so retrospective investigation never depends on shipping telemetry out of your environment.
Four gains across every workflow
Save time
Findings arrive with context already assembled, so analysts act on the incident instead of reconstructing it.
Improve accuracy
Learned behavior catches malware-free and novel activity that manually maintained rules and static baselines miss.
Lower cost
One adaptive detection layer reduces rule maintenance, tuning, and the analyst hours spent stitching tools together.
Keep control
The platform can run and adapt where your data already lives, so raw telemetry never has to leave your environment. Vigil, or your existing AI SOC, drives the workflows on top, all improved by LogLM.
Save time
Findings arrive with context already assembled, so analysts act on the incident instead of reconstructing it.
Improve accuracy
Learned behavior catches malware-free and novel activity that manually maintained rules and static baselines miss.
Lower cost
One adaptive detection layer reduces rule maintenance, tuning, and the analyst hours spent stitching tools together.
Keep control
The platform can run and adapt where your data already lives, so raw telemetry never has to leave your environment. Vigil, or your existing AI SOC, drives the workflows on top, all improved by LogLM.
Save time
Findings arrive with context already assembled, so analysts act on the incident instead of reconstructing it.
Improve accuracy
Learned behavior catches malware-free and novel activity that manually maintained rules and static baselines miss.
Lower cost
One adaptive detection layer reduces rule maintenance, tuning, and the analyst hours spent stitching tools together.
Keep control
The platform can run and adapt where your data already lives, so raw telemetry never has to leave your environment. Vigil, or your existing AI SOC, drives the workflows on top, all improved by LogLM.
Save time
Findings arrive with context already assembled, so analysts act on the incident instead of reconstructing it.
Improve accuracy
Learned behavior catches malware-free and novel activity that manually maintained rules and static baselines miss.
Lower cost
One adaptive detection layer reduces rule maintenance, tuning, and the analyst hours spent stitching tools together.
Keep control
The platform can run and adapt where your data already lives, so raw telemetry never has to leave your environment. Vigil, or your existing AI SOC, drives the workflows on top, all improved by LogLM.
Proven accuracy and scale in large enterprise environments
Examples of attack behaviors DeepTempo can identify
DeepTempo is designed to scale across large telemetry environments while maintaining fast detection response times and reducing operational overhead for security teams.
- 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
Integrates with existing security infrastructure
DeepTempo works alongside existing SIEMs, NDRs, cloud environments, telemetry platforms, and security data lakes without requiring organizations to replace their existing tools.