When Security Data Overload Hides the Context You Need

How observability solves the context problem

Observability tools are designed to understand complex, distributed systems in motion. They ingest logs, but also traces, metrics, and dependency maps. They detect latency shifts, error spikes, retries, saturation, service health issues, and unknown unknowns. And that’s exactly the context security teams lack.

When observability and security come together, teams can finally correlate what matters. For example, they can identify failed authentications tied to a spike in CPU usage on a specific pod, trace malicious request paths across microservices and clouds, and investigate service degradation to determine whether it is the result of misconfiguration or intentional disruption.

Observability grants situational awareness. And in today’s threat landscape, that’s the difference between catching an attack before it happens or cleaning up after it.

The problem isn’t your SOC. It’s the tools it’s forced to use

Security leaders are feeling the pressure. They’ve invested in the best tech they could justify to the board, like firewalls, EDR, CASBs, threat intel feeds, and SIEMs. And still, incidents can go undetected for weeks. Analysts spend hundreds of costly hours investigating alerts that turn out to be false alarms — incident response personnel rates are in the $75–$150 per hour range, and response for a single incident can take up as much as 150 hours of staff time. Plus, tool sprawl means organizations are often paying to ingest the same telemetry multiple times into different platforms for slightly different use cases, like security, DevOps/ITOps, and network operations.

In most enterprises, the observability stack and the security stack don’t talk to each other. Or worse, they duplicate effort. Logs are forwarded in triplicate. Agents are installed three times over. Detection rules are built in siloes, with incomplete context.

Where traditional SIEMs fall short

Traditional SIEMs are event-driven systems built to correlate discrete log events, alert on known patterns, and perform historical queries, and they do that well. But they fall short when it comes to understanding time-based behavior, reconstructing the flow of a request across services, or surfacing signals buried in infrastructure or application performance.

Why does that matter? Because modern threats aren’t always logged. They’re embedded into how your systems behave. Consider:

• A containerized workload mining crypto may not trip a security alert, but will cause a subtle, sustained rise in CPU usage.
• An insider quietly exfiltrating data may cause a dip in available storage and a surge in outbound traffic, but not a “security event.”
• A zero-day exploit may first appear as degraded latency between microservices, not as a logged error.

Traditional SIEMs are blind to anything they aren’t programmed to see. The result is two big problems. You miss the early warning signs of an attack and you lose the context you need when it’s time to investigate. The result is a reactive, delayed security posture, when the ideal state is continuous, contextual awareness.

Three forces driving convergence of security and observability

Bringing security and observability together is not just a philosophical shift. it’s a practical necessity driven by three indisputable forces:

1. Digital complexity

Most organizations that use clouds now run hybrid or multi-cloud workloads — illustrating why visibility across systems is no longer optional. As organizations adopt more cloud services and APIs, the complexity of their environments increases. A single request might touch a dozen services across three cloud providers. Security can’t afford to treat that as a black box.

2. Cost pressure

Security teams are being told to do more with less, and their biggest budget drain is redundant telemetry. Ingesting the same data into multiple tools means paying three times to see the same incident.

Consolidating ingestion pipelines alone can reduce telemetry costs by 20 to 30%. This results in reallocated spend that can go directly into improving threat detection rather than paying for duplication.

3. Organizational silo collapse

Many enterprises now measure detection and recovery together (MTTD + MTTR), a sign that incident response has evolved into a shared, end-to-end responsibility between security, SRE, and observability teams. It’s not scalable for these teams to be operating with separate tools for shared responsibilities and workflows.

The imperative for CIOs

Convergence is overdue. If your team is working in silos, you’re probably experiencing missed incidents and duplicated efforts due to gaps in telemetry and overlapping tools. But now that these issues are realized, they can be fixed.

When you unify observability and security into a shared data platform, you achieve several key outcomes: You reduce noise by correlating signals across systems and domains. You increase the accuracy and speed of detection. You empower teams to move from simple alerting to comprehensive storytelling — something I always emphasize within my own teams.

Be sure to implement a platform that has embedded OpenTelemetry. This will give you a vendor-neutral way to capture and analyze rich telemetry once and use it everywhere. From there, opt for unified dashboards, detections, and investigative workflows that combine infrastructure metrics, service traces, and security logs into a single investigative view.

According to an IDC study, organizations leveraging advanced IT management solutions see a 387% three-year return on investment, with a rapid payback period of just 13 months. Beyond the financial gains, the study also highlights marked improvements in security, with organizations identifying 86% more threats and experiencing 47% fewer incidents.

Security can’t afford to be in the dark, and in a cloud-native world, traditional SIEMs simply don’t see enough. The answer isn’t to rip and replace. It’s to evolve by extending your SIEM with observability-grade telemetry, and anchoring your defense strategy to a unified platform that understands modern systems.

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