Last updated August 2026.
The problem in one number
73%.
That is the share of cybersecurity vendors who received zero ChatGPT citations when buyers asked which SIEM, XDR, EDR, or CASB to use, according to GrackerAI’s 2026 AI Search Visibility benchmark of 100 vendors tested across 250 buyer-intent prompts. (Note: GrackerAI is a vendor selling AI visibility services, so treat this as a vendor-published figure, not an independent audit. The directional finding matches what practitioners observe when they run their own teardowns.)
The implication is straightforward. B2B security buyers now routinely start purchase research in ChatGPT or Perplexity before they visit your website. If your brand does not appear in the answer, a competitor fills the slot. You do not get a notification. You just get less pipeline.
The visible minority of security vendors (the ones appearing consistently inside AI answers) do not get there by accident. They run a repeatable citation-building programme built on three pillars. This post breaks each pillar down.
Step 1: Run a category-citation teardown first
Before building anything, you need a clear picture of the current citation landscape in your category. This teardown takes about 30 minutes to run manually. Automation tools cut that to minutes per week.
How to run the teardown:
- Choose three to five buyer-intent queries for your category. Examples: “what SIEM should a 500-person financial services company use?”, “compare CrowdStrike vs SentinelOne for endpoint detection”, “best XDR platforms for a SOC team of 10”.
- Run each query across ChatGPT, Perplexity, and Google AI Overviews. Do this three times each across two different sessions to account for answer variation.
- Record: which vendor names appear, which source URLs are cited, and which editorial domains those URLs belong to.
- Note the brand-recognition prerequisite pattern: the vendors cited most consistently tend to already appear in Gartner Magic Quadrant reports, Dark Reading bylines, or CSO Online features. AI engines are not discovering obscure vendors from scratch. They are pattern-matching against their training data.
The teardown tells you two things: who is winning your category queries right now, and which specific third-party domains those winners are being mentioned on. That second piece of data is your content target list.
Tools that automate this: Profound tracks citation share across nine or more AI platforms and shows you the exact URLs each engine cites. Scrunch adds LLM crawler control so you can also see how AI crawlers retrieve your own pages. Peec AI runs citation monitoring across platforms including Google AI Overviews. Temso covers the full loop (track, diagnose, and execute) from a single $89/mo subscription. Use whichever fits your budget and team; the output you need is the same: a ranked list of editorial domains that are already generating citations for your category. The full tool comparison lives at /rankings/geo-tools.
Step 2: Build co-citation through trusted third-party domains
Here is what the teardown almost always reveals in cybersecurity: the most-cited vendors are not cited from their own websites. They are cited from a small cluster of editorial and analyst domains that AI engines treat as authoritative for security queries.
The core cluster for cybersecurity includes:
| Domain type | Examples | Why AI engines weight them |
|---|---|---|
| Trade editorial | Dark Reading, CSO Online, SC Media, Help Net Security | High publication volume, strong E-E-A-T signals, indexed by AI crawlers with high trust |
| Analyst reports | Gartner Magic Quadrant, Forrester Wave, IDC MarketScape | Explicit vendor rankings and comparisons; directly answer buyer-intent queries |
| Security research | MITRE ATT&CK, SANS Institute, CVE databases | High factual density; cited by the editorial outlets above |
| Review platforms | G2, Gartner Peer Insights | User-generated comparisons that AI engines use for recommendation queries |
The co-citation strategy in practice:
Get your brand mentioned (with your category, use case, and a concrete differentiator) on as many of these domains as possible. Each mention is a signal that trains the pattern the AI engine uses when it retrieves your category.
For Dark Reading, CSO Online, and SC Media, the fastest route is a contributed byline from your CISO or a senior security researcher. The pitch needs a named finding, a specific threat actor, or a concrete data point. Generic thought leadership does not get placed, and even if it did, AI engines are less likely to cite it.
For Gartner and Forrester, the lever is the formal analyst submission cycle. Most vendors underinvest in this. Analysts report directly and cite vendor materials when they write Magic Quadrant entries and Waves. A vendor that is not in the quadrant is rarely cited by AI engines for comparison queries, because the Gartner report itself is one of the most-cited sources across those queries.
For G2 and Gartner Peer Insights, the lever is review velocity. AI engines pull from review platforms for recommendation queries. A vendor with three reviews and a 4.0 rating appears less often than one with 200 reviews and a 4.6 rating. Running a structured review-generation programme is unglamorous, but it directly affects citation share for the queries that matter most.
Step 3: Publish structured comparison pages that answer the query directly
The most consistent citation-generating content format in cybersecurity is the structured comparison page. Not a product brochure. Not a whitepaper. A page that directly answers the exact question a buyer types into an AI engine.
According to AirOps Research’s April 2026 study of 217,508 retrieved pages across 7,500 commercial prompts, comparison pages containing three or more HTML tables earned 25.7% more AI citations than those without. (Note: single-vendor study, not independently verified.) The mechanism is straightforward: AI engines are looking for structured content that directly answers comparison queries, and a well-formatted table is the easiest thing to retrieve and quote.
What a citation-worthy cybersecurity comparison page looks like:
- Opens with a 40 to 60 word direct answer to the comparison question. This is what the AI engine quotes.
- Includes a Markdown or HTML table comparing named vendors on specific, verifiable dimensions (pricing tier, deployment model, detection coverage, integrations, certifications).
- Names the use case and buyer profile explicitly (“for a 500-person financial services firm running a hybrid environment”).
- Answers the top four to six buyer questions in FAQ format at the bottom.
- Uses sequential heading structure (H1, H2, H3) throughout.
According to Kevin Indig’s 2026 analysis of 1.2 million ChatGPT responses (published February 2026 in Growth Memo), 44.2% of ChatGPT citations came from the first 30% of a page’s content, a pattern Indig calls the “ski ramp.” This means the opening paragraph of your comparison page carries disproportionate weight. If your opening sentence is “In today’s complex threat landscape, organizations face increasing challenges,” you are not getting cited. If it is “For a 500-person financial services firm, CrowdStrike Falcon and SentinelOne are the two most commonly shortlisted EDR platforms; the key differentiators are deployment complexity, pricing model, and MDR add-on availability,” you have a real shot.
Step 4: Maintain an analyst-submission cadence
Most cybersecurity vendors treat Gartner, Forrester, and IDC as annual checkboxes tied to the marketing calendar. The vendors who consistently appear in AI answers treat them as an ongoing content channel.
The distinction matters because AI engines do not just cite the Gartner Magic Quadrant PDF. They cite every piece of editorial content that discusses, references, or links to analyst rankings. When a Dark Reading article references a Gartner position, that article becomes a citation vehicle. When a G2 comparison page links to your Gartner Peer Insights profile, that page benefits from the analyst halo.
A practical cadence:
- Quarterly: Submit updated vendor materials to relevant analyst firms. Flag significant product releases, customer wins, and certification updates. Analysts cannot include what they do not know about.
- Monthly: Pitch contributed content to Dark Reading, CSO Online, and SC Media around a named threat finding or a concrete case study. Not a product announcement; a finding.
- Weekly: Track citation share using a GEO monitoring tool. Note which new editorial mentions produce citation movement and which do not. Iterate toward what moves the number.
The monitoring step is not optional. Without a baseline, you cannot tell whether your programme is working. Tools like Profound, Peec AI, Scrunch AI, and Temso all give you weekly or daily citation share numbers. The choice between them depends on your budget and how much attribution depth you need. Profound gives the deepest citation source maps. Temso is the most accessible entry point at $89/mo.
Step 5: Fix the brand-recognition prerequisite
The teardown in Step 1 often reveals something uncomfortable: the vendors getting cited are already known. AI engines are not neutral arbiters. They pattern-match against brands that already appear across their training data and the editorial sources they retrieve from.
This is the brand-recognition prerequisite. A vendor that has never appeared in Dark Reading, never been in a Gartner quadrant, and has zero G2 reviews is starting from a substantial handicap. Not because the product is weak, but because the brand is invisible to the sources AI engines trust.
The fix is not a single article. It is a systematic effort to build brand-surface-area across the editorial, analyst, and review ecosystem:
- Security research publications: Publish named threat research (even one report per year with a distinctive finding or dataset) gives journalists and analysts a reason to mention you. Named findings travel far in the security community.
- CISO and practitioner placement: CISOs read a short list of outlets. A byline from your CISO in CSO Online or SC Media reaches the people who shortlist vendors, and it generates the editorial mentions that AI engines retrieve.
- Conference presence: RSA, Black Hat, and DEF CON presentations get covered by trade press. Coverage generates the editorial mentions. The conference appearance is a mechanism for generating citations, not just brand awareness.
- Structured comparison pages: As covered in Step 3, these are the pages most likely to be retrieved directly when a buyer runs a comparison query.
According to the TrustRadius 2024 B2B Buying Disconnect Report, 78% of B2B tech buyers shortlist only products they had already heard of before starting their research. The cybersecurity buying process amplifies this. Buyers in a SOC or a CISO office operate under risk constraints that make unfamiliar vendors a harder sell. Brand recognition is not a soft metric. It is a purchase-process filter, and AI engines reflect it.
The citation-building stack for cybersecurity vendors
| Layer | What to do | Primary tools |
|---|---|---|
| Measurement | Track citation share weekly across ChatGPT, Perplexity, Google AI Overviews, and Copilot | Profound, Peec AI, Scrunch, Temso |
| Co-citation | Earn byline placements on Dark Reading, CSO Online, SC Media; submit to Gartner/Forrester cycles | Manual outreach + PR team |
| Review velocity | Drive G2 and Gartner Peer Insights review volume through structured customer programmes | G2 review campaigns |
| Comparison pages | Publish structured comparison pages with direct-answer openings and data tables | Internal content team |
| Research anchor | Publish at least one named threat-intelligence report per year with a distinctive finding | Internal threat research team |
What the top-cited cybersecurity vendors have in common
Based on the pattern visible in category-citation teardowns across SIEM, XDR, and EDR queries, the vendors cited most consistently share a set of characteristics:
- They appear in Gartner Magic Quadrant or Forrester Wave for their category (or an adjacent category with overlapping buyer intent).
- They have a recent byline or named citation in Dark Reading or CSO Online within the past 90 days.
- They have a comparison page that directly names competitors and answers comparison queries in the opening paragraph.
- They have at least 100 G2 reviews with a rating above 4.3.
- Their brand name appears in security practitioner communities (Reddit, Slack, specialized forums) in the context of real-world use.
None of these individually guarantees citation share. Together, they build the brand surface area that gives AI engines enough evidence to retrieve and mention you confidently.
The vendors who are invisible across AI answers share a different profile: they publish technical documentation, product datasheets, and compliance-focused whitepapers. That content is valuable to existing customers. It does not generate citations in buyer-intent AI answers, because it does not answer the questions buyers actually ask.
Start with the teardown
The fastest way to understand your citation position is to run the manual teardown described in Step 1. It costs nothing and gives you the specific editorial domains and competitor pages you need to target.
From there, the programme builds in order: measurement baseline, co-citation placements, comparison pages, and analyst submissions.
If you want a tool to automate the measurement layer and track weekly citation share across platforms, the GEO tool rankings cover the full range from accessible options like Temso to deeper enterprise platforms like Profound and Scrunch. See also the GEO glossary for plain-language definitions of citation share, share of voice, and co-citation, and the methodology page for how we score and rank these tools.
The 73% zero-citation figure is a real problem. It is also a competitive opening for the vendors who fix it first.