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02/08/2026 12:20 pm
TL;DR: Security teams ingest petabytes of telemetry but typically analyze less than 5% of it, while one study found analysts spend 25% of their time on false positives, according to DataBahn. The independent implication is that context, not volume, now determines whether a SOC can move from reactive monitoring to usable security insight.
NHIMG editorial — based on content published by DataBahn: why legacy SIEMs are a problem and how Reef shifts teams from dashboards to insights
By the numbers:
- One study found that as much as 25% of a security analyst’s time is spent chasing false positives.
- The average enterprise juggles 83 different security tools from 29 vendors.
Questions worth separating out
Q: How should security teams reduce dashboard dependency in the SOC?
A: They should shift from static views to contextual investigation workflows.
Q: Why do fragmented security tools create a detection problem?
A: Fragmented tools force analysts to reconstruct the story across separate consoles, which adds delay and increases the chance that important signals are missed.
Q: What breaks when alerts are not enriched with context?
A: Without enrichment, alerts stay noisy and disconnected, which forces analysts to reconstruct the story manually.
Practitioner guidance
- Map investigation latency across your SOC workflow Measure the time from event creation to decision for the most common identity, cloud, and endpoint incidents.
- Enrich identity and asset context before analyst triage Attach user, workload, host, privilege, and recent-alert context to suspicious events before they enter the primary investigation queue.
- Separate high-value events from ingestion noise Route enriched telemetry by investigative value, not by raw volume alone, so that expensive SIEM retention is reserved for events with clear detection utility.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- A closer breakdown of Reef's conversational investigation workflow and how analysts query telemetry in plain language.
- The vendor's explanation of Model Context Protocol and how it grounds AI responses in enterprise data.
- Examples of how the insight layer enriches suspicious login investigations with host, user, and alert context.
- The operational claims around faster query resolution, reduced analyst workload, and root-cause acceleration.
👉 Read DataBahn's analysis of dashboard fatigue and contextual security insights →
Dashboard dependency and SOC context gaps: are your controls keeping up?
Explore further
02/08/2026 1:21 pm
Dashboard sprawl has become a governance problem, not just a tooling problem. When analysts need to mentally merge identity, cloud, endpoint, and network data, the organisation is relying on human memory as a control layer. That is an unstable operating model because the security question is no longer whether data exists, but whether the right context arrives before the attack does. The practical conclusion is that investigation design now belongs in the governance conversation.
A question worth separating out:
Q: Why does AI change the way SOC teams think about accountability?
A: AI changes accountability because the first decision may be made by a system, while the legal and operational responsibility still sits with the organisation and its operators. Teams must identify who owns the model, who approves high-impact actions, and who can override outputs when context is incomplete.
👉 Read our full editorial: Dashboard dependency is failing SOC teams, and context is the missing layer
