How to Optimize Splunk ES and ITSI Dashboards for Executive and Operational Reporting

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Key Takeaways

Most organizations leave 70–80% of their Splunk data underutilized, and dashboards built on this data are missing critical context and costing real money [1].
According to the SACR AI SOC Market Landscape 2025, 40% of security alerts are never investigated, and 61% of teams admit ignoring alerts that later proved critical [2], the problem isn't just data noise, it's that the noise is hiding real risk.
Splunk ITSI predicts service health degradation up to 30 minutes in advance using machine learning, but only when KPI thresholds are properly tuned [4].
Executive dashboards and operational (SOC/NOC) dashboards serve different audiences and require separate design, with distinct metrics, language, and scope.
datasensAI by bitsIO identifies which data sources, knowledge objects, and dashboards are actively being used, so you can eliminate waste and focus your Splunk investment where it creates the most value.

The Dashboard Problem No One Talks About

Most Splunk environments have a dashboard problem, not a shortage of them, but too many of the wrong ones. Security analysts are buried under alert noise. IT operations teams have services modeled in Splunk ITSI that nobody looks at. And when a CISO or CIO opens their Splunk executive dashboard, what they often see is a wall of charts that answers the question "what happened?" but not "should I be concerned?"

This is a fixable problem. Splunk ES and ITSI are genuinely powerful platforms. The challenge is that most environments were built for the teams who deployed them, not for the people who make decisions based on them. Getting to a place where operational dashboards surface the right signals, and executive dashboards tell a coherent story, requires two things: understanding how your environment is actually being used, and a deliberate design philosophy for each audience. 

What Executives Need vs. What Operations Teams Need

The gap between a SOC executive dashboard and an operational one is not just cosmetic. It's a difference in purpose.

Operations teams - SOC analysts, NOC engineers, IT responders need granular, real-time visibility. In Splunk ES, that means access to notable events, risk scores by system or user, alert timelines, and the ability to drill into an incident. In Splunk ITSI, it means service health dashboards with KPI analytics, ITSI alert and episode analytics, and root cause views that let an on-call engineer see exactly which component degraded and when.

An executive such as a CISO, CIO, or VP of IT does not need that level of detail in a morning briefing. They need to understand: Is our security posture improving or declining? Are critical business services running within acceptable health parameters? Are we trending toward or away from our SLA commitments? A Splunk CISO dashboard that tries to surface everything tends to communicate nothing.

Splunk's documentation on ITSI service insights makes this distinction practically: Infrastructure Overview Dashboards, Service Analyzer views, and Predictive Analytics dashboards serve different investigation depths . The same logic should apply when you're designing for human audiences that match the dashboard depth to the decision being made.

Why Your Dashboards Are Noisier Than They Need to Be

According to the SACR AI SOC Market Landscape 2025, security teams across 282 surveyed organizations handle an average of 960 alerts daily, a number that climbs above 3,000 for enterprises with more than 20,000 employees, generated across an average of 28 different tools . On average, 40% of those alerts are never investigated at all, and 61% of security teams admit they have ignored alerts that later proved critical. The SANS 2025 SOC Survey adds another dimension: 42% of SOCs are dumping all incoming data into a SIEM with no plan for how to retrieve or act on it, while 69% still rely on manual processes to report security metrics to leadership [3]. The result is a reporting environment where the volume of data and the quality of insight are moving in opposite directions.

In Splunk ITSI, the same dynamic plays out through poorly tuned KPI thresholds. Splunk's own documentation flags services and KPIs with excessive non-normal severity values as candidates for threshold tuning because a KPI that's always red stops being a signal and starts being wallpaper [5]. ITSI's Alert and Episode Field Values Analysis dashboard exists specifically to identify which services, KPIs, or hosts are generating disproportionate noise, and to help operations leaders understand longer-term pain points before they escalate.

The path to clean executive and operational dashboards runs through the same work: figure out what's a real signal and what's environmental noise, tune accordingly, and retire what nobody is using.

Start With What's Actually Being Used

Before you redesign anything, you need to understand the current state of your Splunk environment. That's where datasensAI comes in.

datasensAI is a Splunk-certified application developed by bitsIO that assigns a score to each data source based on how it's being utilized and whether teams are building useful knowledge objects from it — dashboards, reports, alerts, and saved searches. A high Score means a data source is being actively worked; a low score means you're paying to ingest and store data that nobody is turning into decisions .

The analysis covers search history, usage patterns, dashboard engagement, data volume trends, and error monitoring. It identifies which data sources are underutilized, surfaces optimization opportunities, and integrates with Splunk DMX to help right-size your environment. Critically, it does this without bitsIO requiring direct access to your environment, the process is handled securely by your team and takes roughly 2 to 4 hours depending on environment size.

For Splunk ES and ITSI specifically, this matters because many organizations have built dozens of dashboards over the years that simply aren't being used. Before investing time in redesigning executive security reporting or ITSI service health dashboards, datasensAI helps you identify what exists, what's being accessed, and what can safely be retired or consolidated. It also highlights underutilized data sources that could be powering better Splunk observability ROI if properly connected to KPIs and dashboards.

Designing Dashboards That Actually Get Used

Once you understand your environment, the design principles are straightforward, though the execution takes discipline.

For Splunk security executive reporting, the focus should be on risk trends, not raw events. A CISO dashboard should show mean time to detect and respond, the status of critical asset risk scores, notable event trends over time, and compliance posture, not a live feed of every alert. Splunk ES supports this through Risk-Based Alerting and MITRE ATT&CK-aligned detection summaries, which can be surfaced in a way that speaks to business impact rather than technical detail.

For ITSI service health dashboards aimed at IT leadership, the goal is business-aligned visibility. Service health scores tied to critical systems such as payment processing, customer portals, internal infrastructure tell an operations executive far more than raw CPU or memory metrics. ITSI's predictive analytics layer can forecast potential degradation 30 minutes ahead [4], which changes the conversation from "here's what broke" to "here's what we prevented."

Operational dashboards for SOC and NOC teams should preserve depth and real-time context, but with cleaner signal filtering. The combination of properly tuned KPI thresholds, AI-driven alert correlation through ITSI episode grouping, and focused notable event summarization in ES reduces the cognitive load on analysts and makes triage faster.

How bitsIO Can Help

bitsIO is a four-time Splunk Partner of the Year with deep expertise in Splunk ES and ITSI implementations. We help organizations move from cluttered, underperforming environments to reporting setups that actually work — for both operations teams and the executives who depend on them.

Using datasensAI, we start by showing you exactly where your Splunk investment is landing and where it isn't. From there, we help you tune KPI thresholds, consolidate or retire underused dashboards, and build executive-ready views in Splunk ES and ITSI that reflect how your business actually operates.

Ready to see what your Splunk environment is really doing? Book a free consultation with our team.

Frequently Asked Questions

This is an area where practitioners are actively experimenting, though there is no single documented standard yet. The general approach involves exporting structured, aggregated Splunk outputs — such as risk score summaries from ES or service health scores from ITSI — and passing those to an AI tool with a clear, context-rich prompt. The output quality depends almost entirely on the cleanliness and structure of the underlying Splunk data. Organizations with properly tuned KPIs and well-defined notable event categories are better positioned to get useful summaries than those whose environments are still noisy and untuned.

The most effective approach is role-based consolidation: identify the three to five metrics that matter most to an executive (MTTR trend, critical service availability, compliance posture) and build a single-pane view around those. Retire anything that isn't actively informing a business decision.

datasensAI scores each data source based on usage frequency and knowledge object creation. If a data source has a low Score and no active dashboards or alerts are built from it, it's a candidate for retirement or reallocation. bitsIO reviews the results with you and recommends specific consolidation steps

In principle, yes — but with important caveats. If your Splunk ES environment is well-tuned, with clean notable events, consistent risk scores, and reliable MITRE ATT&CK tagging, then a structured export from ES can be a reasonable input for an AI summarization tool. In practice, most organizations find their ES data needs significant cleanup before AI-generated summaries are reliable enough for executive audiences. The compliance mapping aspect in particular requires human verification — automated summaries should be treated as a starting draft, not a final report.

datasensAI's scoring mechanism evaluates each data source by how actively it's being used and whether it's generating actionable knowledge objects. High-scoring sources aligned to critical services and security use cases should be retained and expanded. Low-scoring sources should be reviewed against your MITRE ATT&CK coverage before any decommissioning decision

Executive views should be service-outcome focused — health scores, SLA adherence, predicted degradations. Operational views should retain KPI-level drill-down and episode analytics. Keeping these as separate dashboards, rather than trying to combine them, prevents the design compromises that make both audiences unhappy.

ITSI's Alert and Episode Field Values Analysis dashboard identifies which KPIs or hosts are generating disproportionate alert volume. Combining that with adaptive thresholding and anomaly detection reduces noise at the source. AI can then help identify threshold adjustment patterns across similar services based on historical data

There is no out-of-the-box native integration between commercial LLMs and Splunk ES or ITSI at this time — what exists is practitioner-built. The most common approach involves using Splunk's REST API to export structured results from saved searches or summary indexes, then passing that output to an LLM via its API with a well-designed prompt. For ITSI, service health scores and KPI trend summaries tend to produce more coherent AI outputs than raw event data. For ES, aggregated risk event counts and notable event categories work better than full log streams. This is an emerging area and results vary significantly depending on dat,a quality and prompt design.

Executive dashboards should focus on MTTR trends, critical service availability, open high-risk incidents, compliance status, and security posture trajectory. Operational dashboards should retain real-time alert feeds, KPI drill-downs, episode management queues, entity risk scores, and root cause analysis tools.

datasensAI provides a clear breakdown of data storage and processing costs alongside utilization scoring [1]. This makes it possible to show leadership a before-and-after view: which data sources were being paid for but not used, what was reallocated or removed, and what the resulting cost and performance improvement looks like. bitsIO's ROI Calculator at bitsioinc.com/datasensai-roi-calculator can help frame the conversation.

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