{"id":25327,"date":"2025-10-13T12:29:09","date_gmt":"2025-10-13T16:29:09","guid":{"rendered":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/?p=25327"},"modified":"2025-10-13T12:29:11","modified_gmt":"2025-10-13T16:29:11","slug":"deeptempo-and-cribl-join-forces-to-strengthen-behavior-based-threat-detection","status":"publish","type":"post","link":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/security\/deeptempo-and-cribl-join-forces-to-strengthen-behavior-based-threat-detection.html","title":{"rendered":"DeepTempo and Cribl Join Forces to Strengthen Behavior-Based Threat Detection"},"content":{"rendered":"\n

Key Takeaways:<\/strong>
\u2022 The partnership combines Cribl\u2019s telemetry management with DeepTempo\u2019s deep learning platform for behavior-first threat detection.
\u2022 DeepTempo\u2019s LogLM foundation model interprets the \u201clanguage of logs,\u201d identifying subtle deviations that could indicate malicious activity.
\u2022 The integrated solution aims to reduce false positives, lower SIEM costs, and improve detection accuracy across hybrid and cloud environments.
\u2022 Human behavior remains crucial in monitoring attacks, as subtle deviations might be linked to evolving or AI-driven threats.<\/p>\n\n\n\n

DeepTempo has announced a strategic partnership with Cribl to advance how organizations detect and respond to cybersecurity threats in the age of polymorphic and AI-driven attacks. The collaboration pairs Cribl\u2019s expertise in telemetry collection and management with DeepTempo\u2019s deep learning-powered detection platform, Tempo, to deliver what both companies describe as a unified approach to modern Security Operations (SecOps).<\/p>\n\n\n

\n
\"Evan
Evan Powell, CEO of DeepTempo<\/figcaption><\/figure><\/div>\n\n\n

At the center of this new joint offering is Tempo, DeepTempo\u2019s flagship behavioral detection platform. Tempo is powered by LogLM, a deep learning foundation model designed to understand the \u201clanguage of logs.\u201d By modeling and learning from normal patterns of system activity, LogLM helps detect deviations that might signal malicious reconnaissance, lateral movement, or exfiltration attempts\u2014while maintaining a false positive rate below one percent after domain adaptation.<\/p>\n\n\n\n

\u201cThe future of threat detection lies in context and behavior, not static signatures,\u201d said Evan Powell, CEO of DeepTempo. \u201cWith Cribl\u2019s data management and Copilot capabilities, pairing our Tempo platform at the network layer gives defenders both coverage and governance at scale. Tempo\u2019s LogLM turns raw telemetry into high-signal context\u2014the insight security teams need to outpace agentic AI threats, zero-click exploits, and other attacks that slip past traditional defenses.\u201d<\/p>\n\n\n\n

A Unified, Data-Driven Approach<\/strong><\/p>\n\n\n\n

Cribl\u2019s platform acts as the central nervous system for data ingestion and routing, while DeepTempo\u2019s analytics engine interprets that data in real time. Cribl Stream, Lake, and Search form the foundation for unified telemetry management, giving security teams the ability to collect, route, tier, and query data from virtually any source. This consolidation helps eliminate the need for multiple collectors or manual preprocessing pipelines.<\/p>\n\n\n\n

\u201cSecurity teams need full visibility and the ability to act fast,\u201d said Vlad Melnik, VP of Business Development and Global Alliances at Cribl. \u201cWith Cribl, organizations can shape and route telemetry to the right tools, like DeepTempo\u2019s purpose-built deep learning engine, for real-time threat detection. It\u2019s a natural fit: we deliver the right data, and Tempo extracts maximum security value.\u201d<\/p>\n\n\n\n

Cribl\u2019s schema-aware enrichment tools, including its Copilot Editor, automatically align telemetry with industry frameworks such as OCSF, ECS, UDM, and ASIM. DeepTempo\u2019s Tempo platform then adds behavioral enrichment to this data, improving both the accuracy and interpretability of alerts. Together, this enables faster investigations and sharper detection signals while reducing analyst fatigue.<\/p>\n\n\n\n

Speed, Scale, and Cost Efficiency<\/strong><\/p>\n\n\n\n

DeepTempo\u2019s Tempo platform leverages GPU acceleration through NVIDIA\u2019s RAPIDS framework, allowing it to perform high-throughput, real-time analysis on massive data sets. This performance boost is critical in environments where billions of log entries must be processed continuously.<\/p>\n\n\n\n

Tempo also incorporates automatic tagging of log sequences with MITRE ATT&CK techniques, helping analysts quickly understand the stage and type of threat they\u2019re facing. Forensic timelines, vector-based correlation, and replay capabilities enable security teams to revisit historical data stored in lower-cost object storage\u2014useful for both investigations and model retraining.<\/p>\n\n\n\n

The companies say this efficiency translates into tangible cost benefits, with customers potentially lowering their SIEM licensing expenses by up to 45%. Intelligent data routing, reduced false positives, and centralized visibility contribute to operational savings without sacrificing detection fidelity.<\/p>\n\n\n

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\"Profile
Vlad Melnik, VP of Business Development and Global Alliances at Cribl<\/figcaption><\/figure><\/div>\n\n\n

Human Insight and Behavior Monitoring Still Central<\/strong><\/p>\n\n\n\n

While DeepTempo\u2019s LogLM automates detection across vast data streams, both companies stress that human expertise remains an essential part of the equation. Subtle deviations in user or system behavior can still be the earliest indicators of a sophisticated attack. Human analysts play a critical role in validating, interpreting, and responding to these signals\u2014ensuring that machine learning models are not only efficient but contextually aware.<\/p>\n\n\n\n

The rise of agentic AI and polymorphic malware has made it more important than ever to integrate human understanding into automated systems. Identifying, tracking, and remediating anomalies early can prevent small irregularities from escalating into major breaches. Behavior-first detection\u2014where every signal is analyzed in the context of normal activity patterns\u2014helps bridge the gap between raw telemetry and actionable intelligence.<\/p>\n\n\n\n

Toward a More Adaptive Defense Model<\/strong><\/p>\n\n\n\n

As security teams confront growing data volumes and increasingly dynamic threat landscapes, partnerships like this one reflect a broader shift in SecOps. Traditional rule-based detection systems often struggle to keep pace with evolving attack patterns, especially those powered by AI. DeepTempo and Cribl\u2019s integrated approach\u2014fusing telemetry management with behavioral modeling\u2014offers an adaptive, data-centric path forward.<\/p>\n\n\n\n

The combined solution is available immediately for deployment across cloud, hybrid, and on-premises environments. It integrates seamlessly into existing workflows without requiring endpoint agents or vendor lock-in, aligning with modern preferences for open, modular security architectures.<\/p>\n\n\n\n

Both companies position the collaboration as a response to the industry\u2019s most pressing challenges: data overload, complexity, and the need for faster, more accurate detection. By aligning Cribl\u2019s visibility across data pipelines with DeepTempo\u2019s domain-specific AI, the partnership aims to help organizations focus less on tool integration and more on strategic response.<\/p>\n\n\n\n

In an era where threats evolve as fast as the networks they target, this behavior-first approach might mark an important shift in how enterprises balance automation with human insight.<\/p>\n\n\n

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\"\"<\/a><\/figure><\/div>\n\n\n

If you liked this post, you\u2019ll love one of the the leading global business communications and technology events since 1999, the ITEXPO #TECHSUPERSHOW<\/a>, Feb 10-12, 2026 Fort Lauderdale, Florida.<\/p>\n\n\n\n

Don\u2019t forget the collocated MSP Expo<\/a> \u2013 just for managed service providers!<\/p>\n\n\n\n

Aside from his role as CEO of TMC<\/a> and chairman of ITEXPO<\/a> #TECHSUPERSHOW Feb 10-12, 2026, Rich Tehrani is CEO of RT Advisors<\/a> and a Registered Representative (investment banker) with and offering securities through Four Points Capital Partners LLC <\/a>(Four Points) (Member FINRA\/SIPC). He handles capital\/debt raises as well as M&A. RT Advisors is not owned by Four Points.<\/em><\/p>\n\n\n\n

The above is not an endorsement or recommendation to buy\/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.<\/p>\n\n\n\n

The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.<\/p>\n\n\n\n

Portions of this article may have been developed with the assistance of artificial intelligence, which may have contributed to ideation, content generation, factual review, or editing<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Key Takeaways:\u2022 The partnership combines Cribl\u2019s telemetry management with DeepTempo\u2019s deep learning platform for behavior-first threat detection.\u2022 DeepTempo\u2019s LogLM foundation model interprets the \u201clanguage of logs,\u201d identifying subtle deviations that could indicate malicious activity.\u2022 The integrated solution aims to reduce false positives, lower SIEM costs, and improve detection accuracy across hybrid and cloud environments.\u2022 Human<\/p>\n","protected":false},"author":44,"featured_media":25328,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156],"tags":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/25327"}],"collection":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/comments?post=25327"}],"version-history":[{"count":1,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/25327\/revisions"}],"predecessor-version":[{"id":25331,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/25327\/revisions\/25331"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media\/25328"}],"wp:attachment":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media?parent=25327"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/categories?post=25327"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/tags?post=25327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}