For years, enterprises have been told a comforting story: telemetry is telemetry. Logs are logs. If you can collect, normalize, and route data efficiently, you can support both observability and security from the same pipeline.
At first glance, this sounds efficient. One ingestion layer. One set of collectors. One routing engine. Lower cost. Cleaner architecture. But this story hides a fundamental mistake.
Observability, telemetry, and security telemetry are not simply two consumers of the same data stream. They are different classes of data with distinctintents, time horizons, economic models, and failure consequences.
The issue is intent. This is what we at Databahn call the Telemetry Intent Gap: the structural difference between operational telemetry and adversarial telemetry. Ignoring this gap is quietly eroding security outcomes across modern enterprises.
The Convenient Comfort of ‘One Pipeline’
The push to unify observability and security pipelines didn’t stem from ignorance. It stemmed from pressure. Exploding data volumes and rising SIEM costs which outstrip CISO budgets and their data volumes are exploding. Costs are rising. Security teams are overwhelmed. Platform teams are tired of maintaining duplicate ingestion layers. Enterprises want simplification.
At the same time, a new class of vendors has emerged,positioning themselves between observability and security. They promise a shared telemetry plane, reduced ingestion costs, and AI-powered relevance scoring to “eliminate noise.” They suggest that intelligent pattern detection can determine which data matters for security and keep the rest out ofSIEM/SOAR threat detection and security analytics flows.
On paper, this sounds like progress. In practice, it risks distorting security telemetry into something it was never meant to be.
Observability reflects operational truths, not security relevance
From an observability perspective, telemetry exists to answer a narrow but critical question: Is the system healthy right now? Metrics, traces, and debug logs are designed to detect trends, analyze latency, measure error rates, and identify performance degradation. Their value is statistical. They are optimized for aggregation, sampling, and compression. If a metric spike is investigated and resolved, the granular trace data may never be needed again. If a debug logline is redundant, suppressing it tomorrow rarely creates risk. Observability data is meant to be ephemeral by design: its utility decays quickly, and its value lies in comparing the ‘right now’ status to baselines or aggregations to evaluate current operational efficiency.
This makes it perfectly rational to optimize observability pipelines for:
· Volume reduction
· Sampling
· Pattern compression
· Short- to medium-term retention
The economic goal is efficiency. The architectural goal isspeed. The operational goal is performance stability. Now contrast that with security telemetry.
Security telemetry is meant for adversarial truth
Security telemetry exists to answer a very different question: Did something malicious happen – even if we don't yet know what or who it is?
Security telemetry is essential. Its value is not statistical but contextual. An authentication event that appears benign today may become critical evidence two years later during an insider threat investigation. A low-frequency privilege escalation may seem irrelevant until it becomes part of a multi-stage attack chain. A lateral movement sequence may span weeks across multiple systems before becoming visible. Unlike observability telemetry, security telemetry is often valuable precisely because it resists pattern compression.
Attack behavior does not always conform to short-term statistical anomalies. Adversaries deliberately operate below detection thresholds. They mimic normal behavior. They stretch activity over long time horizons. They exploit the fact that most systems optimize for recent relevance. Security relevance is frequently retrospective, and this is where the telemetry intent gap becomes dangerous.
The Telemetry Intent Gap
This gap is not about format or data movement. It is about the underlying purpose of two different types of data. Observability pipelines are meant to uncover and track performance truth, while security pipelines are meant to uncover adversarial truth.
Observability asks: Is this behavior normal? Is the data statistically consistent? Security asks: Does the data indicate malicious intent? In observability, techniques such as sampling and compression to aggregate and govern data make sense. In security, all potential evidence and information should be maintained and accessible, and kept in a structured, verifiable manner. Essentially, how you treat – and, at a design level, what you optimize for – in your pipeline strongly impacts outcomes. When telemetry types are processed through the same optimization strategy, one of them loses. And in most enterprises, the cost of retaining and managing all data puts the organization's security posture at risk.
The Rise of AI-powered ‘relevance’
In response to cost pressure, a growing number of vendors catering to observability and security telemetry use cases claim to solve this problem with AI-driven relevance scoring. Their premise is simple: use pattern detection to determine which logs matter, and drop/reroute the rest. If certain events have not historically triggered investigations or alerts, they are deemed low-value and suppressed upstream.
This approach mirrors observability logic. It assumes that medium-term patterns define value. It assumes that the absence of recent investigations or alerts implies no or low risk. For observability telemetry, this may be acceptable.
For security telemetry, this is structurally flawed. Security detection itself is pattern recognition – but of a much deeper kind. It involves understanding adversarial tradecraft, long-term behavioral baselines and rare signal combination that may never have appeared before. Many sophisticated attacks accrue slowly, and involve malicious action with low-and-slow privilege escalation, compromised dormant credentials, supply chain manipulation, and cloud misconfiguration abuse. These behaviors do not always trigger immediate alerts. They often remain dormant until correlated with events months or years later.
An observability-first AI model trained on short-term usage patterns may conclude that such telemetry is "noise". It may reduce ingestion based on absence of recent alerts. It may compress away low-frequency signals. But absence of investigations is not the absence of threats. Security relevance is often invisible until context accumulates. The timeline over which security data would find relevance is not predictable, and making short and medium-term judgements on the relevance of security data is a detriment to long-horizon detection and forensic reconstruction.
When Unified Pipelines Quietly Break Security
The damage does not announce itself loudly. It appears as:
· Missing context during investigations
· Incomplete event chains
· Reduced ability to reconstruct attacker movement
· Inconsistent enrichment across domains
· Silent blind spots
Detection engineers often experience this in terms of fragility: rules are breaking, investigations are stalling, and data must be replayed from cold storage – if it exists. SOC teams lose confidence in their telemetry, and the effort to ensure telemetry 'completeness' or relevance becomes a balancing act between budget and security posture.
Meanwhile, platform teams believe the pipeline is functioning perfectly – it is running smoothly, operating efficiently, and cost-optimized. Both teams are correct, but they are optimizing for different outcomes. This is the Telemetry Intent Gap in action.
This is not a Data Collection issue
It is tempting to frame this as a tooling or ingestion issue. But this isn't about that. There is no inherent challenge in using the same collectors, transport protocols, or infrastructure backbone. What must differ is the pipeline strategy. Security telemetry requires:
· Early context preservation
· Relevance decisions informed by adversarial models, not usage frequency
· Asymmetric retention policies
· Separation of security-relevant signals from operational exhaust
· Long-term evidentiary assumptions
Observability pipelines are not wrong. They are simply optimized for a different purpose. The mistake is in believing that the optimization logic is interchangeable.
The Business Consequence
When enterprises blur the line between observability and security telemetry, they are not just risking noisy dashboards. They are risking investigative integrity. Security telemetry underpins compliance reporting, breach investigations, regulatory audits, and incident reconstruction. It determines whether an enterprise can prove what happened – and when.
Treating it as compressible exhaust because it did not trigger recent alerts is a dangerous and risky decision. AI-powered insights without security context will often over index on short and medium term usage patterns, leading to a situation where the mechanics and costs of data collection obfuscate a fundamental difference in business value.
Operational telemetry supports system reliability. Security telemetry supports enterprise resilience. These are not equivalent mandates, and treating them similarly leads to compromises on security posture that are not tenable for enterprise stacks.
Towards intent-aware pipelines
The answer is not duplicating infrastructure. It is designing pipelines that understand intent. An intent-aware strategy acknowledges:
· Some data is optimized for performance efficiency
· Some data is optimized for adversarial accountability
· The same transport can support both, but the optimization logic – and the ability to segment and contextually treat and distinguish this data – is critical
This is where purpose-built security data platforms are emerging – not as generic routers, and not as observability engines extended into security, but as infrastructure optimized for adversarial telemetry from the start.
Platforms designed with security intent as their core – and not observability platforms extending into the security 'use case – do not define the value of data by their recent pattern frequency alone. They are opinionated, have a contextual understanding of security relevance, and are able to preserve and even enrich and connect data to enable long-term reconstruction. They treat telemetry as evidence, not exhaust.
That architectural stance is not a feature. It is a philosophy. And it is increasingly necessary.
Observability and Security can share pipes – not strategy
The enterprise temptation to unify telemetry is understandable. The cost pressures are real. The operational fatigue is real. But conflating optimization logic across observability and security is not simplification. It is misalignment. The future of enterprise telemetry is not a single, flattened data stream scored by generic AI relevance. It is a layered architecture that respects the Telemetry Intent Gap.
The difference between operational optimization and adversarial investigation can coexist and share infrastructure, but they cannot share strategy. Recognizing this difference may be one of the most important architectural decisions security and platform leaders make in the coming decade.
.png)
.png)
.png)
.avif)
.avif)
