Splunk ITSI vs Opstral
The Splunk ITSI Alternative for Autonomous Resolution
Written by
Alok Singh Pawar
Published
June 2026
Reading time
7 min read
Splunk ITSI turns Splunk data into service-health scores, predictive alerts and grouped episodes. If your goal is to resolve incidents rather than monitor and predict them, here is an honest comparison with Opstral.
Why teams evaluate an alternative to Splunk ITSI
Splunk IT Service Intelligence is a mature AIOps layer for teams that already live in Splunk. It applies machine learning and predictive analytics to the data you ingest, scores service health against KPIs, and groups noisy events into episodes so an operator sees situations rather than a wall of alerts. For a committed Splunk shop, that depth is real. Teams look for an alternative for three reasons: ITSI monitors and predicts, but the fix still runs through Splunk SOAR playbooks or a human; its model assumes your operational data is centralised in Splunk first; and since Cisco completed its acquisition of Splunk in 2024, ITSI is being folded into a much larger Cisco strategy.
| Dimension | Splunk ITSI | Opstral |
|---|---|---|
| Primary focus | Service-health monitoring, prediction and event grouping on the Splunk data platform | Autonomous, governed resolution across enterprise operations |
| Detection vs resolution | Scores health, predicts and groups events into episodes; remediation via Splunk SOAR or a human | Closes the loop: ProcBot executes the fix, Sherlock validates it before the incident is closed |
| Governance of actions | Automation through SOAR playbooks; governance depends on how those playbooks are built | Every action runs as a reversible, audited Action Ticket, with approval gates where you want them |
| Data model | Assumes operational data is ingested and centralised in Splunk | Reads from the tools you already run through Integration Connectors, no single index required |
| Operational breadth | Service monitoring and event analytics, with the wider Splunk suite for SIEM and SOAR | Ten modular pillars spanning telemetry, service, infrastructure, security, data, cost, process, DevSec Ops, agent ops and the managed estate |
| Ownership | Part of Cisco since 2024 | Independent platform from VisionWaves |
| Best for | Teams standardised on Splunk who want service-health monitoring and prediction on that data | Enterprises that want incidents resolved with governance across ten domains, including air-gapped |
Where Opstral is different
- It resolves, not just predictsITSI tells you a service is at risk. Sentinel AI acts: ProcBot executes the fix and Sherlock verifies recovery before the incident is closed.
- No single-index requirementOpstral reads from the monitoring, ITSM and cloud tools you already run through Integration Connectors, rather than assuming everything lands in one platform first.
- Governed and air-gappedEvery action is a reversible, audited Action Ticket, and the whole platform runs on-premises or fully air-gapped across ten operational domains.
Detection capacity has outrun investigation capacity
The bottleneck in most Splunk estates is not detection quality. Correlation searches and ITSI episodes work, and they represent years of tuning with real value. The bottleneck is arithmetic: a search can produce notables continuously, and an analyst can triage a few dozen properly in a shift.
An untriaged notable queue is not a backlog. It is a record, written in advance, of what you will be asked about in the post-incident review.
Which makes the governing question not how much more you can detect, but what may safely be done about a detection without a human. Opstral scores the blast radius of the response rather than the confidence of the detection, and in a security context that distinction has teeth. Enriching a notable, resolving entities across sources and gathering evidence are reads: reversible by definition, so they run unattended. Disabling an account, blocking a source or isolating a host are not reversible in the way that matters, because a wrong one creates a second incident during a trading window. Those are held for a named approver with the procedure and rollback already written.
The right-hand column is not a limitation we are apologising for. It is the reason the left-hand columns are permitted to run at all. The argument is set out in confidence scores are the wrong gate.
What this looks like on one notable
We walked a single notable end to end in when Splunk detects a pattern nobody has time to investigate, including the part most teams find uncomfortable: how much of triage is retrieval rather than judgement.
Splunk stays where the data lives and where the detection logic lives. Sentinel queries Splunk in SPL for adjacent evidence rather than duplicating the index, and queries outside Splunk for what your deployment is unlikely to carry, then writes the disposition and its reasoning back against the notable. Where you run Splunk SOAR, we trigger it rather than duplicate it. The correlation across notables usually changes the queue more than any single triage improvement: a large fraction of any queue is several detections of one underlying event.
What we can evidence, and what we cannot
Our measured figures come from one deployment, a Tier-1 telecom operator in India: more than 27,000 devices under one pane, a 43 percent MTTR reduction measured in production within six months of go-live, and 85 percent of routine manual operations running as governed procedures. They link to the case study that records them. Figures from our other deployments live on their own pages and do not join that set, for the reasons set out in how we decide what to publish as a number.
What we cannot evidence is a triage throughput number for your estate. Queue reduction is easy to produce by suppressing rather than investigating, and it looks excellent on a dashboard for several months. The check that matters is whether every suppressed detection still produced a record you can pull a quarter later.
Frequently asked questions
Does Opstral replace Splunk ITSI?
It can, for the resolution job. Opstral correlates and then resolves incidents across your estate. Teams that keep Splunk for logging and search can feed ITSI episodes or Splunk data into Opstral through Integration Connectors.
What does Opstral add over Splunk ITSI?
Autonomous, governed execution of the fix through reversible Action Tickets, validation via Sherlock before an incident is closed, breadth across ten operational domains, and a model that does not depend on centralising all data in Splunk first.
Is Splunk ITSI still independent?
No. Cisco completed its acquisition of Splunk in 2024, and ITSI is being integrated into Cisco's observability and AIOps strategy. Opstral is an independent platform from VisionWaves.
How is this different from Splunk SOAR?
Splunk SOAR is Splunk own product for the response step and it is good at it: it executes a playbook you wrote, on a trigger you defined, deterministically. That determinism is both the virtue and the limit, because a playbook does not decide what to look at next based on what it just found. The triage that decides whether a playbook should fire at all still lands on an analyst. Where SOAR is already deployed we trigger it rather than duplicate it.
Can it act on a security notable without a human?
Only where the action is reversible, bounded and machine verifiable. Enrichment, entity resolution, evidence gathering and case creation run unattended. Disabling an account, blocking a source or isolating a host are held for a named approver, because a wrong one creates a second incident and the wrong ones are not always visible immediately.