Microsoft Sentinel

How fast is a Splunk to Microsoft Sentinel migration?

Microsoft Sentinel is the cloud-native SIEM many teams consolidate onto. Logmetry designs and implements Sentinel, normalizes data to ASIM, controls Log Analytics ingestion cost, and runs parallel-run migrations led by a Chief Observability and Security Architect with 26+ years at Fortune 500 scale.

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Why do Sentinel migrations and ingestion costs become hard?

Sentinel bills on data ingested into Log Analytics, so cost is driven by volume long before it is driven by detections. Teams that lift and shift a Splunk feed straight into Sentinel pay for verbose operational logs that never feed a single analytic rule. Yale New Haven Health cut SIEM spend 40% while moving 30,000+ endpoints to Microsoft Sentinel in 2 weeks (published).

Migrations stall when detections, parsers, and data sources are rebuilt by hand against an unfamiliar schema. Without a parallel run, security teams cannot prove the new detections fire before the old SIEM is turned off, so the cutover slips for months.

Sentinel also expects data in a consistent shape. Inconsistent field names across firewalls, identity providers, and endpoints mean every analytic rule carries its own parsing logic, which is fragile and expensive to maintain across a large estate.

40%

SIEM spend cut at Yale New Haven Health (published)

30,000+

Endpoints migrated to Sentinel in 2 weeks (published)

40-70%

Typical ingest reduction with upstream control. Your number depends on your environment.

How do you control Sentinel ingestion cost without losing detections?

You decide what enters Log Analytics before it is billed, then normalize what remains to ASIM so detections stay clean. A vendor-neutral control layer in front of Sentinel routes high-value security events into the Analytics tier and sends verbose operational data to cheaper storage, so each workspace pays for signal rather than noise.

Logmetry is vendor-agnostic. We review your environment, recommend the right tier and routing model with honest pros and cons, and you choose. Where a pipeline is the right fit we implement Cribl as one option among the platforms we work across, and we normalize to the Advanced Security Information Model so analytic rules read consistent fields regardless of source.

Tier data before it reaches Log Analytics. High-value security events to the Analytics tier, verbose operational data to lower-cost storage, so the billed volume drops while detections keep their inputs.
Normalize to the ASIM schema upstream so firewall, identity, and endpoint events arrive in consistent fields, which keeps analytic rules simpler and easier to maintain across a large estate.
Run Splunk and Sentinel in parallel against the same routed feed, so detections are validated in Sentinel before the old SIEM is retired.
Keep full-fidelity copies in object storage for investigation and replay, so cheaper Sentinel tiers do not mean losing access to raw data.
Decouple collection from the SIEM, so the next platform change becomes a routing change rather than another rebuild.

How Logmetry designs, migrates, and runs your Sentinel environment

Sentinel sits inside the Azure ecosystem, so the work spans workspace architecture, commitment tier economics, Data Collection Rule configuration, and the relationship between Sentinel, Defender, and Log Analytics. Zbigniew Gajuk, Co-Founder and Chief Observability and Security Architect, has led this work at Fortune 500 scale across Splunk, Microsoft Sentinel, Datadog, and Cribl, so the design reflects your environment rather than a template.

1Architecture Review: audit workspace layout, ingestion volume by source, commitment tier utilization, and the Splunk detection set, then model the migration path and the cost opportunity. This first review is the free hook.

2Design and Recommend: architect the routing and tiering model, document honest pros and cons for each option, and let you choose what enters the Analytics tier, what goes to cheaper storage, and how ASIM normalization is applied.

3Implement: build the control layer with DCR endpoint integration and ASIM normalization at config-level depth, validated against existing Splunk detection coverage so nothing is lost in translation.

4Migrate and Optimize: run Splunk and Sentinel in parallel, cut over by routing change once detections are proven, then govern ingestion and detections on an ongoing basis as data sources evolve.

Frequently asked questions

Can we run Splunk and Sentinel in parallel during migration?

Yes. A vendor-neutral control layer fans the same routed feed to both Splunk and Sentinel, so you validate that Sentinel detections fire correctly before retiring Splunk. The cutover becomes a routing change once coverage is proven, which is how Yale New Haven Health moved 30,000+ endpoints in 2 weeks (published).

How do you control Sentinel ingestion cost?

Sentinel bills on data ingested into Log Analytics, so we decide what enters before it is billed. High-value security events route to the Analytics tier and verbose operational data goes to cheaper storage. Typical ingest reductions run 40-70%, and Yale New Haven Health cut SIEM spend 40% (published). Your number depends on your environment.

What is ASIM normalization?

ASIM is the Advanced Security Information Model, Sentinel native schema for consistent field names across sources. Normalizing firewall, identity, and endpoint data to ASIM upstream means analytic rules read the same fields regardless of vendor, which keeps detections simpler and cheaper to maintain across a large estate.

Is Logmetry tied to one platform for this?

No. We are vendor-agnostic across Splunk, Microsoft Sentinel, Datadog, and Cribl. We review your environment, recommend the right approach with honest pros and cons, and you choose. The control layer sits in front of the SIEM, never in place of it, and Logmetry is never a SIEM.

Ready to explore this further?

Let's discuss how this applies to your environment.

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