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27/06/2026 1:59 pm
TL;DR: AI Data Analyst handles ~40% of metrics-and-trends queries, one in five board or CISO report requests, and 20% of active-threat questions, according to Abnormal AI. The bigger change is not speed, but the way executive communication and threat quantification become embedded in day-to-day security work, while customers save three to five hours weekly on reporting and analysis.
NHIMG editorial — based on content published by Abnormal AI: AI Data Analyst usage patterns and security reporting
By the numbers:
- Metrics and trends queries make up ~40% of AI Data Analyst usage.
Questions worth separating out
Q: How should security teams use natural-language analytics without weakening assurance?
A: Treat natural-language analytics as an acceleration layer, not a control authority.
Q: Why do board-level reports matter so much in security analytics?
A: Board-level reports matter because they translate technical activity into decisions about risk, budget, and prioritisation.
Q: What should teams measure when adopting AI-assisted security reporting?
A: Measure both productivity and trust.
Practitioner guidance
- Validate the metric definitions behind AI-generated reports Confirm how attack counts, averages, and trend lines are calculated before using them in board decks or KPI reviews.
- Separate acceleration from assurance in active-threat workflows Use natural-language analysis to speed up sender, role, and campaign review, but cross-check the outputs against native detections, case records, and investigation notes before escalation or closure.
- Standardise executive reporting inputs Define the few board-level questions the organisation expects to answer repeatedly, then align telemetry sources and reporting logic so the output remains comparable across periods.
What's in the full article
Abnormal AI's full article covers the operational detail this post intentionally leaves for the source:
- The exact customer question patterns behind each of the five usage categories.
- Example prompt wording for board reports, campaign analysis, and routine SOC tasks.
- The product's stated future direction for Abby as a broader security data concierge.
- Operational examples of how teams use the tool to save three to five hours weekly.
👉 Read Abnormal AI's analysis of AI Data Analyst usage patterns and security reporting →
AI Data Analyst and executive reporting: what changes for SOC teams?
Explore further
27/06/2026 3:33 pm
Security analytics is becoming an identity governance function, not just a SOC convenience. When leadership asks for board reporting, trend analysis, or campaign summaries through a natural-language layer, the system becomes part of how the organisation proves control effectiveness. That changes the governance burden because the quality of the answer now depends on classification, aggregation, and evidence integrity. Practitioners should treat AI-assisted analysis as a reporting control surface, not just a user experience feature.
A few things that frame the scale:
- From our research: Companies are dedicating an average of 32.4% of their security budgets to secrets management and code security, with US organisations leading at 40.8%, according to The State of Secrets in AppSec.
- Our research also found that the average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities.
A question worth separating out:
Q: How do AI-assisted reports affect identity and access programmes?
A: They raise the bar for evidence quality because identity programmes increasingly need to prove who accessed what, what changed, and whether controls worked. If reporting is driven by natural language, the programme still needs authoritative data sources and clear ownership for interpretation, especially across human, NHI, and autonomous access.
👉 Read our full editorial: AI Data Analyst shifts security reporting from dashboards to questions
