Built for Speed, Stuck in Neutral: Why Splunk ES Deployments Stall

Splunk Enterprise Security (ES) is a high-performance engine, built to process enormous volumes of data and surface high-fidelity intelligence at speed.

Yet as a Security Technical Account Manager (TAM), I regularly see environments where that performance never materialises. The platform is deployed, detections are enabled, dashboards light up, but outcomes still fall short.

The issue is rarely the technology itself. More often, it is how ES is configured, deployed, and measured.

Operational excellence in Splunk ES is not about how many detections are enabled. It is about aligning detection engineering to business risk, building on solid data foundations, and measuring outcomes that actually matter.

Based on real‑world customer engagements, here are five common pitfalls that cause Splunk ES deployments to stall, and what to do instead.

1. Misunderstanding Where CIM Compliance Matters

Teams often treat Common Information Model (CIM) compliance as a nice‑to‑have, deferring it in favour of getting content into production quickly. In the moment, this feels like progress. In practice, it almost always causes problems down the line.

Splunk ES is built around the CIM. Out‑of‑the‑box detections, dashboards, and the Threat Intelligence framework all consume CIM-compliant data. More importantly, CIM enables Data Model Accelerations, which dramatically improve performance and scalability.

Skipping this step for security-relevant data often leads to slow searches and a Threat Intelligence framework that does not provide value. These issues rarely surface immediately. They tend to appear only once data volumes grow or detection content expands. By that point, the rework is expensive and disruptive. With the right foundations in place from the start, it is entirely unnecessary.

That said, a common misconception is that all data in Splunk must be CIM compliant for ES to be effective. In reality, data searched infrequently or produced in highly irregular formats can still provide value without CIM compliance. Some use cases also depend on data that simply does not exist within the CIM.

Get the foundations right early, and everything built on top of them is faster, more reliable, and easier to scale.

2. No Detection Strategy

One of the most common challenges I see is teams enabling detections without a clear strategy for deciding which ones actually belong in their environment.

Without a detection strategy, organisations struggle to answer basic questions: Which out-of-the-box detections should be enabled? Which should be built? Which can be safely retired?

In the absence of a structured backlog, teams default to what’s easiest, starting with the data they already ingest, enabling large volumes of out‑of‑the‑box content, or browsing MITRE ATT&CK techniques aimlessly looking for coverage gaps. The result is a detection library that looks impressive, but is difficult to maintain and evolve, and fails to deliver meaningful security value.

Mature detection programs work the other way around: detection backlogs are driven by evidence. Confirmed incidents, penetration testing and purple team findings, near misses, environmental changes, and threat intelligence mapped to observable behaviour define what needs to be detected before teams decide how to detect it.

A strategy also needs to operate at the right level of specificity. High-level objectives like "detect ransomware" describe outcomes, not behaviours. Without decomposing goals into specific, observable behaviours, detections become ambiguous, difficult to evaluate, and hard to tune or retire. Over time, this lack of behavioural grounding leads to fragile content and accumulating detection debt.

The issue is not a lack of features or content. It is the absence of a clear detection strategy.

3. Misunderstanding Risk‑Based Alerting

Risk‑Based Alerting (RBA) is one of the most powerful capabilities in Splunk ES and also one of the most misunderstood.

A common assumption is that RBA replaces the need for tuning. It does not. If a detection is generating false positives, it still needs to be fixed, regardless of whether it creates a risk signal or generates an alert.

The real shift with RBA is that it decouples detection from alerting:

• Detections generate signals that indicate behaviour occurred
• Signals accumulate risk across entities and time
• Alerts are created only when aggregated risk justifies analyst attention

In traditional SOC models, detections are designed to directly generate alerts. This forces detections to be rare, high‑confidence, and immediately actionable. Over time, that pressure leads to over‑tuning and blind spots.

Most attacker behaviour is more subtle than what can be surfaced in a single direct alert. This is why RBA uses scores and metadata to make low‑fidelity observations meaningful over time. With RBA, attacks emerge through risk accumulation, not just individual detections.

Single‑event alerts still have a place for inherently malicious or extremely rare activity, such as credential dumping or direct security control tampering. Almost everything else benefits from an RBA‑first design.

4. Measuring Activity Instead of Outcomes

Another common pitfall is measuring success using the wrong metrics.

Alert volume, the number of enabled detections, or MITRE ATT&CK coverage percentages are easy to track, but they are poor indicators of detection effectiveness.

MITRE ATT&CK is a threat intelligence framework, not a SOC operating model. You don’t detect “T1135.” You detect specific behaviours in your environment, such as unusual LDAP query patterns, that may support that technique. A colourful coverage matrix is not a security posture. It is a comfort blanket.

Effective measurement focuses on outcomes at multiple levels:

• Signal level: Are detections reliable and precise? Are they running as intended?
• Alert and incident level: What was missed? What did purple team exercises or post‑incident reviews reveal?
• Operational consistency: Are analysts applying dispositions consistently, with clear distinctions between false positives and benign positives?

Without shared definitions and consistent processes, even well‑intentioned metrics become misleading, and confidence in the platform erodes.

5. Ignoring Endpoint Telemetry

Of all the gaps I see in Splunk ES deployments, limited endpoint telemetry is one of the most consequential and one of the most consistently underestimated.

EDRs are good at detecting badness on the endpoint. But Splunk's value is in correlating behaviour across the enterprise. Without endpoint telemetry in Splunk, your visibility into endpoint behaviour is limited to what your EDR vendor chooses to surface. With it, you are in control.

Bringing endpoint telemetry into Splunk enables cross-domain correlation across identity, email, cloud, and network. It allows detection logic to run independently of vendor alerts, supports longer retention of telemetry, and integrates directly with Risk-Based Alerting. For teams who prefer threat hunting with SPL, it also means endpoint data is available in the same place as everything else.

This is not about duplicating what an EDR already does or indiscriminately forwarding raw endpoint data into Splunk. It is about defence in depth.

Driving ES at Full Potential

Splunk Enterprise Security is a powerful platform, but power alone does not create outcomes.

Organisations that succeed move beyond feature enablement. They focus on risk‑driven use cases, strong data foundations, RBA‑first detection design, outcome‑based measurement, and comprehensive telemetry.

That is when Splunk ES transitions from a detection tool into a true security operations platform, and when teams start to see the results the technology was designed to deliver.

Call to Action

These are solvable problems. The Splunk Guide to Risk Based Alerting is a strong starting point if RBA is on your radar. And if any of these pitfalls sound familiar, connect with your account team to find out how a Security TAM can help.