Introduction:
Security teams are under pressure to do more with less — more telemetry, more detections, more regulatory evidence — without endlessly increasing SIEM spend. Traditional log-ingestion models, where everything is streamed into hot analytics, are no longer sustainable at scale.
By adopting a Sentinel Data Lake and Graph-based architecture, customers can significantly reduce costs while improving security outcomes.
1. Decouple Data Retention from Analytics Spend
One of the biggest cost drivers in SIEM platforms is always-on analytics ingestion. Every log sent to hot analytics storage incurs ongoing cost — regardless of whether it is actively used for detection.
With Microsoft Sentinel Data Lake, organisations can:
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Store high-volume telemetry (sign-in logs, endpoint events, network flows) in low-cost storage
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Retain data for months or years without paying analytics pricing
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Promote only high-value data into analytics tiers when needed
Instead of paying premium rates for dormant logs, customers shift to a tiered data strategy — analytics when required, storage when not.
This alone can deliver substantial reductions in Sentinel ingestion and retention costs.
2. Replace Redundant Log Ingestion with Graph Queries
Many organisations ingest identity, device, and configuration data into Sentinel simply to query state — not to detect threats.
This is where Microsoft Graph changes the economics.
Using Graph, customers can retrieve:
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User risk and sign-in state
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Device compliance and posture
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Privilege assignments and role membership
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Conditional access and policy configuration
Instead of continuously ingesting static or low-change data, teams query Graph on demand, at detection time or during investigations.
This eliminates unnecessary log volume while maintaining full visibility — reducing both ingestion cost and query complexity.
3. Lower Detection Costs Through Smarter Analytics Design
A Data Lake + Graph architecture enables precision analytics.
Rather than running broad, high-cost queries across massive datasets, detections can:
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Trigger on lightweight signals
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Pull enrichment data dynamically from the Data Lake or Graph
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Correlate context only when risk thresholds are met
This reduces compute-intensive queries and improves signal-to-noise ratio.
Fewer false positives means fewer analyst hours wasted — an often overlooked but very real cost.
Where the Cost Savings Add Up
Customers adopting this architecture consistently reduce spend across multiple dimensions:
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SIEM ingestion costs — less always-on analytics data
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Retention costs — long-term storage without analytics pricing
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Operational costs — faster investigations, fewer false positives
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Engineering overhead — simplified data pipelines and parsers
At scale, this model can reduce Sentinel-related costs by tens of percent, while actually increasing data coverage and investigative depth.
A Smarter Way to Scale Security
The Sentinel Data Lake and Graph architecture isn’t about cutting corners — it’s about architecting for efficiency.
By separating storage from analytics, querying state instead of ingesting it, and designing detections that activate context only when needed, customers gain:
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Lower ongoing security spend
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Better scalability as data volumes grow
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Faster, more accurate threat response
In an era where telemetry is exploding and budgets are tightening, this approach allows security teams to scale intelligently — not expensively.
