Deep Learning Meets Data Control: DeepTempo and Cribl Unite Against AI-Driven Cyber Threats

In a cybersecurity landscape increasingly defined by polymorphic and autonomous AI attacks, DeepTempo and Cribl are joining forces to give defenders a new kind of edge: behavioral detection at machine scale, paired with frictionless data control.

The companies today announced an integration between DeepTempo’s Tempo platform and Cribl’s Data Engine for IT and Security, a collaboration designed to close the widening gap between data visibility and threat detection.

At the heart of the partnership is LogLM, DeepTempo’s foundation model built to “understand the language of logs.” Instead of relying on static rules or signatures, LogLM continuously learns how systems behave — spotting anomalies that traditional tools miss. Combined with Cribl’s unified data collection, routing, and storage capabilities, the integration enables security teams to funnel precise, schema-enriched telemetry directly into Tempo’s deep learning pipeline for immediate behavioral analysis.

“Security teams need full visibility and the ability to act fast,” said Vlad Melnik, VP of Business Development and Global Alliances at Cribl. “With Cribl, organizations can shape and route telemetry to the right tools, like DeepTempo’s purpose-built deep learning engine, for real-time threat detection. It’s a natural fit: we deliver the right data, and Tempo extracts maximum security value.”

The AI-Native Threat Problem

Organizations are drowning in telemetry — and adversaries are now using generative and agentic AI to mutate faster than defenders can write detection rules. Every log, metric, and event holds a potential signal of compromise, but extracting insight without crushing infrastructure or budgets has become nearly impossible.

DeepTempo’s behavior-first model directly addresses this challenge. Rather than indexing everything, Tempo processes logs using NVIDIA GPU acceleration and RAPIDS integration, achieving real-time, high-throughput detection with less noise. False positives remain below 1% after domain tuning, and replay capabilities let analysts pull historical data from cold storage for model retraining or forensic deep dives.

Meanwhile, Cribl’s Stream, Lake, and Search products unify the telemetry lifecycle — collecting and normalizing data across hybrid and multi-cloud environments, automatically mapping it to schemas like OCSF and ECS. Its Copilot Editor automates telemetry alignment, ensuring that what’s sent into DeepTempo arrives ready for behavioral enrichment.

Smarter SOCs, Lower Costs

The result is a detection and response architecture that’s not only faster, but also leaner. According to DeepTempo, the integration can reduce SIEM licensing and data pipeline costs by up to 45%. Security teams can run detections on curated, high-signal telemetry without maintaining multiple collectors or redundant preprocessing layers.

Tempo also adds context to investigations by tagging every sequence with MITRE ATT&CK techniques and generating vector-based correlations — a data science-driven method that links related events even when they differ syntactically.

“With Cribl’s data management and Copilot capabilities, pairing our Tempo platform at the network layer gives defenders both coverage and governance at scale,” said Evan Powell, CEO of DeepTempo. “Tempo’s LogLM turns raw telemetry into high-signal context, the insight security teams need to outpace agentic AI threats, zero-click exploits, and other attacks that slip past traditional defenses.”

The Bigger Picture

This partnership underscores a broader shift in cybersecurity: as attacks become increasingly AI-powered, so must detection. Static rules, regex filters, and manual pipelines can’t keep up with adversaries that rewrite their code in seconds.

By blending Cribl’s data orchestration with DeepTempo’s neural behavioral models, the companies are betting that the future of threat detection lies not in collecting more data — but in understanding it better.

The integrated DeepTempo–Cribl solution is available now for cloud, hybrid, and on-premises environments.