Kaspersky Appears in 44.6% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, Kaspersky appeared in 215 of 482 AI answers included in the Antivirus Software benchmark (44.61%). It received 66 recommendations (13.69%), with 33 Top 3 placements and 6 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

• How do Kaspersky mentions, recommendations and placements compare with its Antivirus Software peers?

Kaspersky recorded 215 mentions (44.61%) and 66 recommendations (13.69%) across 482 answers included in the benchmark. Top 3 placements numbered 33; first-place recommendations numbered 6. Framing among its mentions was 78 positive, 123 neutral and 14 negative.

Kaspersky appears in a large share of the answers reviewed, yet it is recommended far less often than it is mentioned. That gap is the central finding: the brand is present in the conversation without being the answer the assistant puts forward. Reach and endorsement are separate measures here, and the report treats them that way.

Placement is thinner still. Shortlist and first-place appearances sit inside the recommendation count rather than adding to it, so the visible presence is concentrated in passing mentions. For a security brand, being named without being endorsed leaves the choice to competitors.

The proposed work starts with a visibility scorecard that separates mention reach, recommendation and position, so the client can see where the gap is widest before committing effort.

What changed since last month

Questions This Section Answers

• How did Kaspersky counts and coverage differ across the available monthly snapshots?

From October 2026 to November 2026, Kaspersky's mention count fell from 235 to 215, while its recommendation count fell from 154 to 66. The recommendation rate was 23.91% and 13.69% respectively.

Across the monthly samples, mentions and recommendations moved in different directions. The most recent sample shows fewer mentions than the prior month and a markedly lower recommendation count, while the recommendation rate also fell. The earlier months show the recommendation rate drifting downward before this.

These are separate selected samples, and the answer totals differ between months. The observed movement should be read as a record of what the captured answers said, not as evidence about why the counts or rates changed. A smaller sample does not by itself explain a lower rate.

A useful next step is a month-by-month scorecard that keeps mention reach, recommendation and placement distinct, so the client can judge whether the pattern is steady or erratic before acting on it.

Step 1. Measure

Questions This Section Answers

• What should the agency measure and verify first for Kaspersky?

AI answers contained 0 conflicting claims about Kaspersky: 0 critical, 0 high, 0 medium, 0 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

No conflicting claims about Kaspersky were recorded in the captured answers, so there is no fact-check queue to work through from this evidence. That is a clean starting position rather than a reason to skip verification, because the absence of recorded conflicts does not confirm that every statement is accurate.

The proposed work is a visibility comparison that sets Kaspersky's mention and recommendation reach against the leading brands, paired with a scoped list of claims worth checking against primary sources. Each item would carry an owner, an evidence requirement and a review status, so priorities rest on verified facts rather than on what an assistant happened to say.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about Kaspersky should be verified before proposed page edits?

No results are available for this part of the analysis.

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

This part of the analysis returned no results, so there are no disputed topics to brief against from the current evidence. The absence of findings is not a finding of accuracy; it means the consistency review has nothing to work from yet.

The proposed work is a verification pass that resolves the entity, date and source behind any statement before deciding whether a page brief or correction is warranted. Different dates can make two statements look contradictory when they are not. Page briefs would be drafted only after that check, and only for claims that survive it.

Step 3. Align the external narrative

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• Which cited third-party pages are relevant to fact review for Kaspersky?

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

The cited pages selected for review cluster around free-trial downloads and third-party commentary on the software ban, with review sites and compliance blogs also appearing. None of these pages is linked to a recorded conflicting claim, and a citation does not show that a page shaped an answer.

The proposed work is a source review that checks each page's current contents against primary evidence before any request is made. Where a page is accurate, the outcome is a no-change decision recorded alongside the rest. Where a request is justified, it would be framed as an evidence-backed ask, without assuming that the publisher controls or caused the wording.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed Kaspersky website pages should receive review briefs?

The company's website was cited 97 times across 27 distinct page addresses; the table shows 8 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

The company's own pages are cited widely, and the free virus removal tool page accounts for most of that activity. Several other download and resource pages appear far less often, and some cited pages drew no recommending answers at all. A citation is not the same as a page that accompanied a recommendation.

The proposed work is a set of page briefs for the priority pages, built from approved facts and the questions buyers actually ask. Each brief would state what the page should establish and which claims need sign-off, so edits rest on verified material rather than on assumptions about what a page currently says.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for Kaspersky?

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions are broad and list-oriented: top antivirus names, trusted brands, tier lists and free removal. Kaspersky is mentioned in most of them but recommended in only some, which suggests the opportunity lies in how answers frame the brand rather than in whether it appears.

These are answer observations, not search volume or proof of missing content. The proposed work is to check existing coverage first, then draft buyer-question briefs only where a genuine gap remains. Each brief would pair the question with approved facts and a clear reason to recommend, giving writers a defensible basis for endorsement.

Our 90-day action plan

Questions This Section Answers

• How should the agency sequence the proposed Kaspersky work and judge completion?

The first month produces a baseline scorecard, a scoped fact-review register and a shortlist of pages and sources, each with an owner, an evidence requirement and a review status. Nothing is corrected in this phase; the aim is a clean, auditable starting point.

The second month turns verified material into approved page briefs and justified publisher requests, with confirmed edits, no-change decisions, requests and responses recorded separately. The third month adds buyer-content briefs and a fresh scorecard.

Delivered work is recorded apart from measured answer outcomes. Completing a brief or sending a request does not establish that visibility improved, and the comparability limits of each sample stay attached to the results.

Methodology

Questions This Section Answers

• How are Kaspersky answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 482 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 800 captured answers and 5404 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The report covers a single reporting month, and the exact dates when the original answers were collected have not been verified. Visibility rates rest on the answers that met the benchmark's inclusion rules, and a company counts as recommended only when an answer endorses it with positive wording. Shortlist and first-place appearances sit inside recommendations rather than adding to them.

An answer can mention several companies, so company counts cannot be summed into distinct answers. Sentiment describes answer wording, not customer satisfaction. The citation analysis covers a wider set of captured answers than the benchmark, and cited pages still need independent checking. Monthly samples differ, so comparisons show what was recorded, not why it changed.

Our first three priorities

The first priority is a visibility scorecard that keeps mention reach, recommendation and placement separate, because the gap between being named and being endorsed is the clearest signal in this evidence. It gives the client a stable way to judge progress without conflating the measures.

The second is a verification pass over the cited pages and any statements worth checking, resolving entity, date and source before a page brief or publisher request is drafted. Items that cannot be tied to a verified source stay out of the correction queue.

The third is buyer-question content built on approved facts, starting with existing coverage and adding briefs only where a real gap remains. Each brief should give writers a defensible reason to recommend Kaspersky, not just to mention it.