A multi-engine, multi-market benchmark study of how six AI search engines — ChatGPT, Perplexity, Gemini, Microsoft Copilot, Google AI Overview and Google AI Mode — answer buyer-intent questions about enterprise attack surface management, external attack surface management and cyber asset attack surface management across the United States, Canada, India and Germany.
A multi-engine benchmark of how six AI search engines answer buyer-intent questions about enterprise attack surface management. Covers 240,000 AI engine responses drawn from 10,000 queries across ChatGPT, Perplexity, Gemini, Microsoft Copilot, Google AI Overview and Google AI Mode in four markets, together with 1,172,000 cited sources classified by source type and 124 distinct product entries. Reports vendor share of voice, first-mention position, cross-engine cited-source overlap measured by Jaccard similarity, market-by-market variance, and the share of cited pages that sit on vendors' own sites.
The six engines converge on a narrow vendor shortlist while drawing on almost entirely separate evidence bases, so a visibility programme measured on one engine does not generalise to another. The report quantifies that gap, separates mention volume from ordinal position, classifies every cited source by type, and finds that vendor sites carry half of all citations while dead links are under 1%.
The opening pages of the PDF, reproduced here: study scale, abstract, table of contents and introduction. Download the full report for the results, discussion and appendices.
Responses recorded and sources cited, per engine.
| AI engine | Responses | Cited sources | Markets |
|---|---|---|---|
| ChatGPT | 40,000 | 357,000 | 4 |
| Microsoft Copilot | 40,000 | 156,000 | 4 |
| Gemini | 40,000 | 112,000 | 4 |
| Google AI Mode | 40,000 | 175,000 | 4 |
| Google AI Overview | 40,000 | 351,000 | 4 |
| Perplexity | 40,000 | 21,000 | 4 |
| Total | 240,000 | 1,172,000 | 4 |
Enterprise security buyers now begin an attack surface management evaluation inside a generated response, so the vendor set an AI engine returns forms the consideration set before any vendor is contacted. This study measures how six commercial AI search engines answer buyer-intent questions about enterprise attack surface management. Each engine received 10,000 enterprise buyer-intent queries in each of four markets during a single collection window in August 2026, producing 240,000 responses that contained 1,172,000 cited sources and covered 124 distinct product entries. For every response the study recorded length, cited pages and their source type, page reachability, hedging, recency anchoring, truncation, refusal, and each brand mention with its ordinal position. The engines converge on a narrow core: 7 of 87 brand strings were named by all six engines, led by CyCognito at 160,000 mentions and Palo Alto Networks at 157,000, 3,000 apart. Visibility below that core is thin, with 47 brand strings named by a single engine. The brand named most often is not the brand named first in most engines: Palo Alto Networks holds the earlier mean ordinal position in 4 of the six. The engines cite largely separate domains, with a mean pairwise overlap of 0.16, and no two engines share a most-cited domain, yet 51.3% of everything they cite sits on a vendor's own site. Dead links are rare, at 0.4% of cited pages. The same two vendors lead every market, in an order that changes in one of the four.
An enterprise buyer evaluating attack surface management now begins inside a generated response. The buyer asks an AI search engine which products discover unknown internet-facing assets, attribute them to the organisation and its subsidiaries, and feed the result into the tools the security team already runs. The engine returns a short ordered list of named vendors with a paragraph of justification for each. That list is not a ranking of the market. It is the output of one system reading one collection of web sources at one moment, and it arrives before the buyer has opened a single vendor site.
The structural consequence is narrow. A vendor absent from the returned list is not ranked low; it is not present. A generated response has no second page, so the buyer has no mechanism equivalent to scrolling further. Where a search results page offered ten positions and a set of related queries, a generated response typically names between three and six vendors and closes. Visibility in this setting is closer to inclusion than to ranking, and the difference between being inside and outside the returned set is discontinuous.
The measurement problem that follows is that no single engine describes the market. Engines differ in the sources they read, the length at which they answer, and the order in which they name vendors. A vendor that measures its position in one engine learns about that engine. This study measures six at once, in four markets, over one collection window, so that agreement and divergence between them can be separated from the behaviour of any one system.
Enterprise attack surface management is a suitable instrument for this measurement. The vendor set is mature and well documented, so the engines have abundant published material to draw on, and 7 of the 87 brand strings recorded were named by every engine. A category with a settled core isolates engine behaviour from genuine market churn.
The category also has a stable and publicly articulated capability vocabulary, organised around external attack surface management, cyber asset attack surface management and continuous threat exposure management. Across the corpus the engines returned the same selection factors: discovery of unknown and subsidiary assets, seedless or low-seed attribution, internet-scale scanning, and integration with ticketing, workflow and analytics tools already in place. These criteria are specific enough that responses can be compared to each other rather than to a general description of security software.
Buying behaviour in the category is research-intensive and procurement cycles are long, so the cost of omission from an early shortlist is high and difficult to reverse later in the cycle. A vendor omitted from the generated shortlist loses the opportunity at the earliest stage, before any conversation in which the omission could be corrected.
This study provides four things a single-engine measurement does not. It establishes how far six engines agree on the vendor set in one category, separating the shared core from the long tail each engine holds alone. It measures the overlap between the engines' evidence bases directly, as a pairwise coefficient over cited domains, and classifies every cited page by source type, so that agreement on vendors can be set against what the engines actually read. It reports mention volume, first-mention position and mean ordinal position as three separate quantities, which allows them to be shown moving independently. And it tests the vendor set across four markets in the same window, which distinguishes market-specific results from a set the engines return everywhere. All four rest on a single corpus of 240,000 responses collected under one design.
Research questions, methodology, results, discussion and appendices.
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