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20/08/2026 5:43 pm
TL;DR: Across more than 100 integrations, an Autotune algorithm adjusts polling step sizes to reduce API requests, lower memory use, and speed alert ingestion, according to Expel. The governance issue is not the algorithm itself, but the operational trade-off between freshness, resource use, and rate-limit pressure in high-volume security pipelines, with examples including a Microsoft Sentinel poller that cut request volume by half and another that completed steps four times faster.
NHIMG editorial — based on content published by Expel: Autotune improves polling efficiency for security integrations
By the numbers:
- NHIs outnumber human identities by 25x to 50x in modern enterprises.
- Only 5.7% of organisations have full visibility into their service accounts.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
Questions worth separating out
Q: How should security teams tune API polling without losing alert freshness?
A: Use per-integration thresholds for latency, memory, and request volume, then tune step sizes to stay inside those limits.
Q: Why do fixed polling intervals break down in large security environments?
A: Fixed intervals assume data volume and service limits stay stable, but real integrations vary widely.
Q: What do teams get wrong about reducing API request volume?
A: They often treat fewer requests as an automatic improvement.
Practitioner guidance
- Measure ingestion latency by integration. Track how long it takes each poller to retrieve and process new alerts, then compare that baseline against API quota usage and memory consumption.
- Set separate operating thresholds for freshness and resource use. Define acceptable limits for step duration, memory growth, and request volume so an optimisation change cannot improve one metric while degrading another.
- Review service-account and API-token exposure in polling workflows. Inventory the identities used by polling systems, verify their scope is minimal, and confirm that each integration can be rotated or revoked without breaking telemetry.
What's in the full article
Expel's full post covers the operational detail this post intentionally leaves for the source:
- Implementation detail for step-value tuning across different poller types and integration defaults
- Examples of the metrics Autotune monitors, including timing and memory observations per step
- Production examples showing how request volume and completion time changed for Microsoft Sentinel and Microsoft Defender for Cloud Apps
- Operational reasoning for when larger polling windows improve efficiency without crossing resource limits
👉 Read Expel's analysis of Autotune and API polling efficiency →
API polling efficiency for security integrations: what it means for SOC teams?
Explore further
20/08/2026 6:26 pm
Polling efficiency is now a security governance issue, not just an engineering optimisation. When alert ingestion depends on many API-connected systems, poor tuning can create blind spots, overload shared quotas, or delay detection. That makes the polling layer part of operational security control rather than a background implementation detail. Practitioners should evaluate ingestion performance as a governance signal, not merely a service metric.
A question worth separating out:
Q: How should teams decide whether to automate poller optimisation?
A: Automate when the environment has many integrations, variable event volumes, or shared quotas that make manual tuning unreliable. Keep human oversight on the thresholds and exceptions so automation cannot silently degrade coverage. The right test is whether the system maintains stable ingestion with less operational effort.
👉 Read our full editorial: Autotune reduces API polling overhead in security integrations
