Security teams are drowning in data but starved for signal. Every new endpoint, workload, and application generates more telemetry to process - while attackers use AI to launch polymorphic, zero-click, and adaptive campaigns that traditional rules and signatures can’t keep up with.
Cribl and DeepTempo are betting that the answer isn’t more tooling, but smarter, coordinated data and detection pipelines.The two companies have formed a partnership that brings Cribl’s telemetry management and routing platform together with DeepTempo’s Tempo system, which uses a purpose-built deep learning model - LogLM - to understand the “language of logs.” The goal is simple: make it easier for defenders to see everything, correlate faster, and act on real signals instead of chasing noise.
Turning Data Chaos into Context
Cribl’s Stream, Lake, and Search products sit at the heart of many enterprise data flows, collecting, shaping, and routing telemetry from any source to any destination. DeepTempo’s Tempo builds on that foundation by layering in behavior-first analytics that look for deviations in activity patterns, not just known attack indicators.
Instead of relying on static rules or brittle regex filters, Tempo’s LogLM interprets event sequences as language - learning the semantics of how systems behave under normal conditions and spotting when that rhythm breaks. The model is trained across diverse log data and refined for each environment, giving it the ability to flag subtle signs of compromise while maintaining a false-positive rate below 1% after domain adaptation.
A Unified View for Modern SOCs
By connecting the two platforms, security teams can manage telemetry across clouds, networks, and applications with far less friction. Cribl handles ingestion, normalization, and schema mapping - automatically aligning raw data with standards like OCSF or ECS. Tempo then enriches that data with behavioral context, tagging events with MITRE ATT&CK techniques, and assembling timelines that help analysts move from alert to root cause in minutes.
The system is agent-free and built for scale. GPU acceleration and RAPIDS integration allow DeepTempo’s detection engine to process massive data volumes in real time, even in distributed or hybrid setups. Analysts can also replay stored telemetry from low-cost object storage for deeper investigations or model retraining, creating a closed feedback loop between detection and improvement.
Speed, Accuracy, and Cost Control
For MSSPs, the Cribl–DeepTempo partnership speaks to two everyday realities: cutting cost and gaining control. By sending only enriched, high-value telemetry to downstream tools and filtering out the noise early, teams can lower SIEM and storage costs and avoid paying for unnecessary data. The setup also replaces messy, overlapping collectors with a cleaner, more flexible pipeline that’s easier to manage and scale.
Both companies are looking ahead to how AI is reshaping security operations. As agentic AI starts to influence both attackers and defenders, SOCs will need systems that can adapt and act fast without human delay. The Cribl–DeepTempo integration brings that balance - centralized data control paired with adaptive, deep learning–based detection - so MSSPs can stay efficient and ready for what’s next.
