Quick Summary: Oleria Trustfusion, an AI-native identity security platform, delivers AI Identity Insights and Summaries on every page — auto-drafted from real data so architects and engineers get an instant, verified picture without manual analysis.
Identity tools dump data and leave the operator to extract the picture. Each page lists thousands of rows, twenty filters, a dozen columns; the takeaway is left to the human to assemble. Across thirty pages and three audiences — the architect, the GRC lead, the IT engineer — the assembly cost is the bottleneck, not the data.
AI is the right layer because the data exists; the picture is what's missing. Auto-drafting the overview per page is a tractable AI problem — provided every numeric claim is grounded in real data, not generated. Hallucinated counts in an identity surface are worse than no overview.
The AI summarizes what you're looking at — wherever you are in Oleria. Numeric claims come from real data; the prose comes from AI. Today the surface is overview-and-explain; specific action recommendations come next.
Time to a page-level takeaway Seconds today
Hallucinated numbers in summaries Zero today (verified vs. real data)
AI insight coverage Every Oleria page today
Action recommendations in the surface Coming
On every Oleria page. The AI insight surface auto-drafts an overview of what's on the page — identities, accounts, groups, applications, review campaigns, risk surfaces, and so on. The summary refreshes when the underlying data changes. The intent is to make the picture immediately legible without forcing the operator to assemble it from rows and filters.
A structured overview drafted from real Oleria data for the page. The overview covers what stands out (concentrations, anomalies, recent changes), where attention is warranted (high-impact items), and where to act first. The format stays consistent across pages so operators don't relearn it on every screen.
By construction. Numeric claims, app names, identity counts, and references are pulled from real records — not generated. The AI's role is the prose around the data, not the data itself. The same correctness story as plain-English query (G-01).
Yes — drill in via Copilot. Any insight is a starting point for a conversation. Ask Copilot to narrow ("just for the finance department"), expand ("include service accounts"), or pivot ("now show me the same for the next quarter"). The conversation continues against the same data.
Specific action recommendations in the insight surface — "revoke X from Y," "review this group," "rotate this credential" — with confidence indicators. So the operator acts from the overview, not just reads it. Today's surface is overview-and-explain; tomorrow's adds the actionable next step.
