Claire’s post — est. reading time: 9 minutes

In today’s DevSecOps telemetry monitoring environments, data is everywhere. Every layer—from containers to code repositories—is instrumented. Logs flow continuously, dashboards refresh in real time, and security alerts stack up by the minute. It’s never been easier to collect data. But it’s never been harder to make sense of it all. Telemetry, once seen as a means to enhance visibility, is now overwhelming the very teams it was meant to empower.

At first glance, this abundance of information seems like a sign of maturity. Detailed logs, alerting mechanisms, and audit trails signal a highly observant organisation. But visibility without clarity is an illusion. When teams are flooded with events but lack the tools, context, or capacity to understand them, the result is paralysis. Critical anomalies go unnoticed. Minor issues are escalated. And response times stretch—sometimes with serious consequences.

This isn’t just theory. Many organisations have suffered real damage because telemetry failed them at a crucial moment. A suspicious login buried in routine access logs. A vulnerability flagged but left untriaged. An alert triggered but never escalated. The root cause often isn’t a missing tool, but a missing capability: the ability to convert telemetry into timely, actionable insight.

Consider a real-world scenario. An organisation detects a spike in failed login attempts across a set of microservices. The data is available—logged, timestamped, and technically accessible. But it is distributed across multiple monitoring platforms, uncorrelated, and unactionable. By the time a security analyst spots the pattern, the attacker has already escalated privileges and exfiltrated sensitive information. The telemetry was there. But the signal was lost in the noise.

This problem is particularly acute in DevSecOps environments. The very practices that enable speed and scalability—CI/CD pipelines, microservices, ephemeral infrastructure—also generate vast telemetry volumes. Build systems emit logs. Dependency scanners produce vulnerability reports. Runtime monitors track resource use. Network proxies capture traces. Every action is instrumented. And every tool wants to tell its story.

But more data is not more security. In fact, it can be the opposite. When every tool reports in isolation, teams are forced to manually stitch together context. An alert about a suspicious container might not reveal that it came from a known build, deployed under urgency, with a recently approved exception. Without correlation, urgency is misread. Without context, alerts are ignored. Without focus, risk is missed.

This leads to a phenomenon that is becoming all too common: dashboard fatigue. DevSecOps teams, confronted with a wall of visualisations and metrics, become desensitised. Dashboards show that systems are ‘green’, even when something is deeply wrong. Alerts fire constantly, triggering tickets and messages—but lacking the enrichment or context to drive informed response. Over time, trust in the data erodes. Teams stop watching. And the very tools meant to enhance situational awareness become a source of confusion.

So where does the responsibility lie? It’s tempting to blame the tools, or the vendors, or the volume of change. But the real challenge is organisational. Most teams have prioritised telemetry collection without investing equally in telemetry interpretation. Logging has been commoditised. Understanding has not. Without a deliberate approach to data hygiene, contextual enrichment, and response automation, observability systems simply amplify chaos.

The solution begins with a shift in mindset. Telemetry is not an output—it’s an input to decision-making. That means it must be curated, designed, and governed like any other strategic capability. Not all logs need to be retained. Not all metrics are meaningful. Not all alerts deserve equal weight. Teams must be empowered to tune their systems—not to collect everything, but to focus on what enables them to act with confidence.

This requires consolidation. Too many organisations suffer from tool sprawl—multiple platforms monitoring overlapping areas, each with its own language, dashboards, and priorities. Integrating these into a coherent observability layer is essential. Centralising logs, metrics, and traces allows for unified analysis. Correlating security signals with operational context enables faster triage. And creating shared dashboards across teams fosters collaboration and shared accountability.

Enrichment is another critical component. An alert without context is just a question. Enriched telemetry—tagged with user identities, change requests, pipeline metadata, or business impact—turns that question into an answer. It tells teams not just what happened, but who, where, and why it matters. This is what elevates monitoring from passive observation to proactive security posture management.

Automation can also help. Manual triage is not scalable. AI-driven correlation engines, rules-based filters, and adaptive alerting thresholds allow organisations to reduce noise while preserving fidelity. Automation should not replace analysts—but it should reduce cognitive load. It should surface the truly unusual, the truly dangerous, and the truly urgent. And it should do so consistently.

Yet technology is only part of the answer. Culture matters. Teams must align on what constitutes risk, how to respond, and who owns which part of the response chain. This is where many observability strategies falter. Without clear operational agreements—who gets paged, when, and why—alerts become background noise. Without training, teams struggle to distinguish anomaly from incident. And without leadership support, telemetry remains a technical concern, rather than a business one.

Executives must also engage. Observability is not just about uptime—it’s about resilience, integrity, and trust. Security incidents have reputational consequences. Compliance failures have regulatory ones. Business continuity depends on knowing what is happening, why, and how fast you can respond. Leadership should treat telemetry not as an operational metric, but as a strategic signal. When a system fails, or is breached, the real question is: how quickly did we know, and how clearly did we understand?

Building this capability takes time. It requires investment in platform engineering, in data architecture, in incident management practices. It means creating feedback loops between detection and response, so that alerts improve, dashboards evolve, and teams learn. It means measuring not just volume of data, but quality of insight. And it means continually asking: are we observing the right things? Are we understanding them in time? Are we acting effectively?

When done well, observability becomes a force multiplier. It enables faster iteration, because problems are detected earlier. It enhances security posture, because anomalies are surfaced with context. It improves reliability, because teams learn from incidents. And it builds confidence, because everyone knows what’s happening, and what to do when something goes wrong.

But when neglected, telemetry becomes a tax. It consumes storage. It overwhelms teams. It creates false confidence. And it opens the door to undetected failure. In an age where digital systems underpin every part of the business, this is not a risk organisations can afford to take.

So the real question is this: Is your telemetry helping your teams respond—or just distracting them from what matters most?