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18/08/2026 2:21 pm
TL;DR: Security, platform, and observability teams are still struggling under escalating telemetry volumes, with one analysis arguing that traditional SIEM and data-lake workflows cannot scale to hundreds of millions of daily events plus AI-generated traffic, according to Auguria. The practical shift is toward abstracting and grouping data earlier so analysts can act on relevance instead of raw volume.
NHIMG editorial — based on content published by Auguria: To improve the data experience, change the way you experience data
By the numbers:
- There are more than half a million unfilled US cybersecurity jobs in 2025 and 3.5 million globally, according to current reports.
Questions worth separating out
Q: How should security teams reduce telemetry overload without losing useful signals?
A: Start by separating data needed for compliance retention from data needed for live investigation.
Q: Why do AI-generated workloads make telemetry harder to manage?
A: AI-generated workloads create more events, more variability, and more ambiguous context than conventional application components.
Q: What breaks when telemetry is filtered too aggressively?
A: Over-filtering can hide early indicators of application failure, privilege misuse, or identity anomalies before they are correlated.
Practitioner guidance
- Measure relevance before storage cost Track how many collected events support detection, incident response, or identity investigation, then prune low-value sources instead of defaulting to maximum ingestion.
- Add identity context to telemetry triage Enrich logs with workload identity, service account, and agent context so analysts can see whether an event represents expected automation or a governance exception.
- Move semantic filtering earlier in the pipeline Test whether your platform can group similar events and surface relevance before ETL completion, because that is where the biggest triage gains are likely to appear.
What's in the full article
Auguria's full blog post covers the operational detail this post intentionally leaves for the source:
- The data experience management workflow the vendor uses to group and surface telemetry relevance before ETL completes.
- The pre-load inference approach and how it differs from conventional SIEM indexing and object-store tagging.
- The practical mechanics of handling high-volume cloud, AI-generated, and autonomous-agent event streams in production.
- The vendor's framing of semantic understanding as an operational improvement for SOC and SRE teams.
👉 Read Auguria's analysis of data experience management for SecOps and observability →
Data experience management in SecOps: can teams keep up?
Explore further
18/08/2026 3:12 pm
Telemetry relevance is now an identity governance problem, not just a SIEM problem. Once logs and traces include service accounts, API activity, and agent behaviour, the control question changes from storage to interpretation. If a team cannot distinguish ordinary automation from abnormal identity use, NHI oversight collapses into after-the-fact hunting. Practitioners should treat telemetry relevance as part of identity governance, not a separate observability concern.
A question worth separating out:
Q: Who is accountable for noisy telemetry that slows incident response?
A: Accountability usually spans SecOps, platform engineering, and the teams that own the applications or identities generating the noise. If the organisation treats telemetry as a storage problem only, no one owns relevance. Governance should assign ownership for event quality, identity tagging, and triage usefulness.
👉 Read our full editorial: Data experience management could reshape overloaded SecOps teams
