Why Wait for Threat Detection? The Case for Real-Time Telemetry Intelligence

The average organization manages 76+ security tools while drowning in data volumes that IDC estimates will exceed over 175 zettabytes globally this year. Yet according to Splunk research, more than half (55%) of organizational data is "dark data" meaning teams either don't know it exists or don't know how to find, prepare, analyze or use it. 

Most of this data is redundant, unstructured, and arriving too late to act on—creating a perfect storm of escalating costs and degraded security posture.

In our recent webinar, Real-Time Data Intelligence for Security & Platform Teams, we explored why the traditional "collect everything, analyze later" approach is breaking under the weight of modern infrastructure demands—and demonstrated a fundamentally different way forward that treats complexity as the enemy, not an inevitability.

Legacy telemetry pipeline solutions weren't designed for the scale and velocity of modern infrastructure. As David Cifuentes, our Field CTO, explained during the session, the typical enterprise architecture has become a maze of interdependent complexity: dozens of data sources generating logs, metrics, and events that flood SIEMs and observability platforms with raw, unprocessed data.

This complexity actively works against security and platform teams. Analytics systems become overloaded and slow to process the constant influx of raw data. Storage costs spiral as teams ingest "everything just in case," while alert fatigue increases as SIEMs struggle with noisy, low-value telemetry. 

Yet most teams continue feeding their analytics platforms with the same fire hose of unfiltered telemetry, hoping that more data will somehow lead to better outcomes.

The era of real-time data intelligence demands a fundamentally new approach where complexity is reduced, not compounded. Companies that continue to wait for after-the-fact analysis are missing critical events—from zero-day exploits to performance degradation to customer experience issues that directly impact revenue.

Real-time intelligence means acting on data while it's in motion rather than after it lands in storage. It involves enriching and contextualizing telemetry at the point of ingestion, filtering the signal from the noise before expensive tools ever see it, and routing the right data to the right destinations in the right format. This is shift-left for data pipelines, where action happens earlier, cost goes down, and performance improves.

Let's examine how leading teams are implementing this upstream control to break free from the complexity trap that's plaguing modern infrastructure.

During our live demonstration, Doug Felteau, VP of Technical Field Engineering, walked through a practical example that should resonate with any security or platform team: handling Azure AD logs with Sigma rule detection. This scenario illustrates how traditional approaches create unnecessary complexity and delay while real-time alternatives deliver immediate value.

Most organizations follow a predictable but problematic pattern. 

• Raw ingest: All Azure AD logs (including benign sign-ins and audit noise) flow directly to the SIEM

• Post-processing: The SIEM parses, enriches, and correlates—burning CPU and time

• Delayed detection: Sigma rules run only after everything else completes

• Late alerts: 10-30 minutes pass before teams know about privilege escalations or suspicious activity

This approach forces expensive analytics platforms to do the heavy lifting of parsing, enriching, and correlating data that should have been processed upstream. This results in delayed alerts, with 10-30 minutes passing before teams learn about privilege escalations or suspicious activity—time that attackers use to establish persistence and move laterally through the environment.

With upstream control, security teams can fundamentally change this sequence and eliminate the complexity that plagues traditional architectures:

• Parse in motion: Structure data as it arrives using ML-powered parsers (no regex required)

• Enrich immediately: Add threat intelligence, geo-IP data, and risk scores while data moves

• Detect instantly: Apply Sigma rules in transit, triggering alerts in milliseconds

• Route intelligently: Send enriched, actionable data to SIEMs while archiving full logs to low-cost storage

This method unlocks millisecond-level data processing while achieving a 40-60% reduction in SIEM storage costs through intelligent data reduction. More importantly, detection quality actually improves because signals are enriched and contextualized before they reach analytics platforms.

What makes real-time telemetry intelligence practical today is purpose-built infrastructure designed for wire-speed processing that replaces complexity with clarity:

• Process at wire speed: Data flows through enrichment, transformation, and routing in milliseconds

• Scale without limits: Containerized, horizontally scalable workers handle millions of events per second

• Integrate anywhere: Agent-less deployment with support for any protocol (Syslog, HTTP, Kafka, cloud APIs)

• Maintain flexibility: Visual pipeline builders eliminate complex scripting while preserving customization

As Doug demonstrated in the webinar, teams can build sophisticated detection and routing pipelines using drag-and-drop interfaces—no regex expertise required. This stands in stark contrast to first-generation data routing tools that created the category but reflect legacy thinking. Modern real-time intelligence platforms are built for today's telemetry operations with visual pipelines, AI-powered tuning, and lower operational footprint.

The transformation from complexity to clarity isn't theoretical. One of Onum's customers, a large international bank, exemplifies this shift from data burden to strategic asset. They were struggling with the familiar challenges that plague organizations trying to manage telemetry at scale:

• Cost-prohibitive network data analysis

• Maxed-out analytics platforms

• Unquantified cybersecurity risk

• Compliance gaps

By implementing upstream data control through real-time intelligence, they achieved remarkable results that demonstrate the power of eliminating complexity rather than managing it. Analysis costs dropped by 90% through intelligent filtering that eliminated unnecessary data processing. They reached 100% compliance with clear data traceability and governance. Cybersecurity risk became quantified and manageable through real-time threat detection capabilities. Perhaps most importantly, they simplified their entire architecture with unified telemetry pipelines that replaced multiple point solutions.

The shift to real-time telemetry intelligence delivers strategic advantages that position organizations for the challenges ahead. As AI and machine learning workloads demand cleaner, more structured data, and as regulatory requirements become more stringent, the ability to process and control data upstream becomes critical:

• Security teams gain earlier signal: Threats are detected and triaged while attackers are still moving, not after they've established persistence

• Platform teams reduce tool sprawl: One intelligent pipeline replaces multiple collectors, agents, and custom scripts

• Compliance becomes automatic: Data residency, retention, and auditing policies are enforced at ingestion

• Vendor lock-in disappears: Format-agnostic routing means tools can evolve without pipeline rewrites

These advantages become even more critical as industry trends accelerate. The rise of AI-driven security operations requires model-ready data that's structured and enriched at the source. Multi-cloud environments demand agnostic routing that isn't tied to specific vendor ecosystems. Zero-trust architectures need real-time visibility into data flows and access patterns.

Organizations that embrace real-time intelligence are positioned to leverage these emerging technologies, while those clinging to legacy approaches will find themselves increasingly constrained by complex, brittle architectures that can't adapt to changing requirements.

The data landscape is evolving rapidly, with global data creation expected to grow by 50% over the next two years. If your team is still waiting for data to land before acting on it, the gap between your capabilities and your requirements will only widen. Consider these critical questions:

• How much are you paying to store and process logs that never generate actionable alerts?

• Could you detect threats faster if enrichment happened upstream instead of downstream?

• What would 90% fewer false positives do for your SOC efficiency and analyst retention?

• How quickly could you test new security tools if data routing was format-agnostic?

These aren't just operational questions—they're strategic ones that will determine whether your organization can compete effectively in an increasingly data-driven world.

Legacy approaches to telemetry management are failing under the weight of modern data volumes and velocity demands. The complexity that was once manageable has become the enemy of effective security and platform operations. Teams that continue feeding raw, unprocessed data into expensive analytics platforms will find themselves outpaced by those who embrace real-time intelligence and treat complexity as a problem to solve, not accept.

The technology exists today to process, enrich, and route telemetry in milliseconds—not minutes. The question isn't whether real-time processing is valuable. It's whether your current architecture can deliver it without adding more layers of complexity to an already overburdened stack.

The era of real-time data intelligence is here. Organizations that recognize this shift and act on it will transform their data from a cost burden into a strategic asset. Those that don't will continue drowning in complexity while their competitors race ahead.

Ready to see real-time telemetry intelligence in action? Watch the full webinar recording or book a custom demo to see how Onum can help your team shift from reactive analysis to proactive intelligence.