What is telemetry in security and observability?
In today’s IT environments, telemetry refers to the automated collection of operational data—metrics, logs, traces, and events—from systems, applications, and infrastructure. The main goal: improve visibility to troubleshoot issues, monitor performance, and support risk management. However, the way telemetry is managed has changed as environments have grown more complex.
Why simply collecting more data isn’t enough
For many years, the idea was simple: gather as much telemetry as possible to gain full visibility. In practice, this approach often leads to overwhelming amounts of noise, making it harder (not easier) to detect security incidents or performance problems.
- Information overload: Too much low-value data makes it harder to identify real threats or root causes.
- Fragmentation: Data arrives from disparate tools and sources, leading to inconsistent signals that slow down incident response.
- Rising costs: Storing and processing every possible data point burns through budgets, while much of that data adds little security value.
The real differentiator isn’t quantity, but telemetry quality—meaningful, relevant, and context-rich signals that drive actionable insight.
Upstream processing: Improving quality before data reaches observability platforms
Security and IT teams are now rethinking how and where they process telemetry. Instead of pushing all data downstream to expensive storage and analytics platforms, leading organizations:
- Filter and enrich telemetry immediately as it’s generated, prioritizing key signals and suppressing unhelpful data.
- Leverage open standards like OpenTelemetry to extract and format data consistently across diverse environments, supporting multi-cloud and hybrid infrastructure.
- Route only high-quality telemetry to core observability and security tools, reducing operational overhead and making it easier to identify real threats or anomalies.
This shift not only lowers costs but directly improves security outcomes and operational agility—teams spend less time sorting through irrelevant logs and more time responding to real events.
How AI and automation depend on telemetry quality
AI-driven security tools require reliable, contextualized data to surface risks and automate responses. Flawed or noisy telemetry undermines these capabilities, leading to false positives, missed threats, or unreliable automated actions.
Executives and boardrooms increasingly seek confidence in AI recommendations, which hinges on trust in the underlying data. High-quality telemetry supports end-to-end transparency—critical for compliance, audit, and explaining security decisions both inside and outside the IT team.
Key takeaways for security leaders
For organizations investing in observability and security automation, early attention to telemetry quality pays off. By building intelligence and filtering into the telemetry pipeline from the start, teams reduce costs, accelerate troubleshooting, and lay a stronger foundation for dependable AI. Rather than capturing more, focus on capturing smarter—relevant, trustworthy, and actionable data that drives better business and security results.