Organizations pour budget into Splunk but rarely know which indexes and sourcetypes are actually contributing to security, operations, or business decisions. Low-value data quietly inflates license costs. The analysis needed to separate critical data from waste is manual, inconsistent, and hard to defend to leadership. Sending that operational data to external services for analysis creates real security concerns.
datasensAI connects to your Splunk environment via the REST API to evaluate each index across utilization, MITRE ATT&CK-aligned detection coverage, and data quality, classifying indexes into actionable tiers and generating prioritized recommendations. As a self-hosted platform, customer telemetry and analysis remain within the customer's infrastructure when configured with the local Ollama provider, with the option to use an external AI provider based on customer requirements.
Three core outputs that turn Splunk index spend into defensible, scored decisions.
Scores each index on Utilization (35%), MITRE ATT&CK Detection Coverage (40%), and Data Quality (25%) into a composite tier. Every score is deterministic, reproducible, and tied to a full audit trail.
The current dashboard source describes ROI Score and GainScope as being generated by the LLM pipeline. Therefore, we should not publish that every score is fully deterministic until the implementation and UI wording are aligned and the formulas are verified
Surfaces prioritized MITRE ATT&CK-aligned detection and operational use-case recommendations for underutilized data sources. Each recommendation connects dormant data to a specific detection or operational outcome.
Runs a fully local LLM under a code-enforced allowlist. This satisfies strict security, compliance, and data residency requirements without compromise.
Maintains full decision lineage and hash-chained snapshots for every scoring run. A continuous governance daemon ensures reproducibility and supports audit requirements.
Deploy datasensAI with Docker Compose in one command. Processing time varies by environment size, infrastructure, query performance, and AI provider.
When configured with the local Ollama provider, customer telemetry and analysis remain within your infrastructure. An optional external AI provider can also be configured based on your requirements.
RETAIN, OPTIMIZE, ARCHIVE, or ELIMINATE with the scoring rationale attached. Not guesswork, but a reproducible, multi-dimensional evaluation your team can present to leadership.
100% local inference means no customer data goes to an external cloud service. Sensitive operational data stays within your own infrastructure throughout the entire process.
The savings staircase shows specific cost reduction steps in order of risk level. Teams can start cutting waste immediately while planning larger optimizations with confidence.
After the initial setup, each Refresh run can be repeated whenever the environment changes. Processing time varies based on the number of indexes and sourcetypes, infrastructure capacity, Splunk query performance, and the selected AI provider.
datasensAI gives you a scored, auditable view of every Splunk index and a clear roadmap for cutting waste without touching data that matters.
Dive into our extensive library of resources tailored to enhance your experience with Splunk and other leading technologies. Keep up with the latest industry trends, best practices, and expert insights to fuel innovation and help you reach your goals.
datasensAI is a self-hosted agentic platform built by bitsIO that connects to a Splunk environment and autonomously evaluates every index for business value. It produces data utilization scores, ROI and cost analysis, and MITRE ATT&CK-aligned use-case recommendations all running locally with no data leaving the machine.
datasensAI produces three core outputs: a data scoring view that classifies each index as RETAIN, OPTIMIZE, ARCHIVE, or ELIMINATE; an ROI and cost analysis showing license spend breakdown and GainScope savings potential; and 10 to 15 specific use-case recommendations aligned to MITRE ATT&CK for underutilized data sources.
Operational and security data in Splunk indexes and sourcetypes is sensitive. Sending that data to an external cloud service creates compliance, data residency, and security risks that many organizations cannot accept. datasensAI runs entirely on your own infrastructure the analysis happens on the machine.
For each underutilized data source, surfaces prioritized MITRE ATT&CK-aligned detection and operational use-case recommendations for underutilized data sources datasensAI surfaces . This connects dormant data to concrete security outcomes and gives your team a prioritized list of what to build next.
Each index is scored on three dimensions: Utilization at 35% of the composite (how actively the data is searched and used), Detection Coverage at 40% (how well the data supports MITRE ATT&CK-aligned detections), and Data Quality at 25% (completeness, freshness, and reliability). The composite tier determines whether the index should be retained, optimized, archived, or eliminated.
After the one-time Docker Compose setup, each Refresh run can be initiated whenever needed. Processing time varies based on environment size, infrastructure capacity, Splunk query performance, and the selected AI provider. Results appear in the executive dashboard after completion.
datasensAI connects to Splunk environments via the REST API. Compatibility with Splunk Cloud depends on whether API access is enabled for the deployment. bitsIO can advise on the right configuration during the onboarding process.
We're a team of passionate experts helping businesses like yours extract maximum value from their data investments. Whether you need to streamline data pipelines, build AI-powered solutions, or unlock advanced analytics, we have the expertise to guide you through every step.
920 South Spring St, Ste 1200, Springfield, IL, 62704
Copyright © 2026 bitsIO | All Rights Reserved.
