Last updated July 2026
Ask ChatGPT which SIEM platform to evaluate for a mid-market security team. Ask Perplexity to compare XDR vendors. Ask Google AI Overviews to explain the difference between CrowdStrike and SentinelOne.
In almost every answer, the same five or six names appear. Gartner Magic Quadrant leaders. The brands that CISO panels quote at RSA. The vendors reviewed in Dark Reading ten years running.
This is not because those vendors have better products in every case. It is because they have accumulated a citation profile that AI retrieval engines recognise and trust. If your brand is not in that profile, you are invisible to a buyer who never reaches a traditional search result page.
This piece is a playbook for closing that gap: how to run a citation teardown on publicly observable AI answers, what the winning domains share in common, and how to build the co-citation authority those domains carry.
Step 1: Run the citation teardown yourself
Before you can close a gap, you need to see the gap clearly.
A citation teardown is a structured audit of which brands and domains AI engines cite when a buyer asks a category question in your space. It is public and free to run. You need five prompts, four engines, and a spreadsheet.
Choose your prompt set
Pick prompts that mirror how a real security buyer phrases the question. Examples:
- “Best SIEM platforms for a 500-person company”
- “Compare CrowdStrike and SentinelOne for XDR”
- “What are the top EDR vendors for enterprise?”
- “Which SIEM integrates with Azure Sentinel?”
- “SIEM vs XDR: which do I need first?”
Group these into two categories: category queries (what are the top platforms?) and comparison queries (how does X compare to Y?). Track them separately. AI engines handle each type differently.
Run each prompt five times
Answer engines are probabilistic. A single response is one sample from a distribution. Run each prompt five times on ChatGPT, Perplexity, Google AI Overviews, and Gemini. Note:
- Which vendor names appear in each response
- Which source domains are cited or linked
- Whether your brand appears at all
- The sentiment of any description when your brand does appear
Five runs per prompt, four engines, five prompts: that is 100 data points. Enough to identify a pattern.
Map the source domains
The source domains are more important than the vendor rankings. When ChatGPT cites “according to Gartner” or links to a Dark Reading comparison, it is telling you where it gets its retrieval signal for this category.
Build a table:
| Source domain | Times cited | Source type | Your brand present in this source? |
|---|---|---|---|
| gartner.com | 12 | Analyst report | No |
| darkreading.com | 9 | Trade publication | Partial (one mention) |
| g2.com | 8 | Review platform | Yes (low review count) |
| reddit.com/r/netsec | 6 | Community forum | No |
| csoonline.com | 5 | Trade publication | No |
| crowdstrike.com | 4 | Vendor threat report | N/A |
This table tells you exactly where to direct your co-citation effort. Not everywhere. The specific source types that already dominate answers in your category.
Tools that automate this process at scale include Profound, Peec AI, and Scrunch AI. They run thousands of prompt variants across multiple engines and map citation sources systematically. For manual teardowns on a smaller prompt set, the approach above works fine and gives you hands-on familiarity with what buyers actually see.
Step 2: Understand the Magic Quadrant citation default
Gartner Magic Quadrant positions are not just analyst opinions. For AI retrieval, they function as a citation multiplier.
When a vendor is placed in a Magic Quadrant, the placement generates:
- A Gartner research note (high-authority domain, frequently crawled)
- Vendor press releases citing the placement
- Trade coverage of the results (Dark Reading, SC Media, CRN)
- LinkedIn posts from CISO audiences (community signal)
- Third-party reviews that reference the placement
Each of those creates a co-citation: your brand name appearing alongside “Gartner,” “leader,” and the category name. AI engines learn from that pattern. Over time, the brand becomes the expected answer to the category query.
This is why a vendor who placed in the Leaders quadrant five years ago still appears in AI answers even if a challenger has since matched them on features. The citation archive from that placement continues to influence retrieval.
Your playbook starts with the same mechanics, applied at accessible scale.
You do not need a Gartner placement to begin. You need co-citation in the same source types that a Magic Quadrant placement generates.
Step 3: Build co-citation authority in the right source types
The citation teardown showed you which source types win. Now build presence in each.
Threat-intelligence reports
Threat-intelligence reports are the highest-authority citation asset in cybersecurity AI answers. A vendor publishing original research on ransomware trends, zero-day exploit analysis, or threat actor profiles earns citations in the same breath as the category query.
The pattern AI engines follow: when a buyer asks “best SIEM for detecting APT activity,” the engine retrieves sources about APT detection. If your brand has published a credible threat report that those sources reference, you appear in the answer.
Cadence matters more than length. A 10-page focused report on a specific threat type, published quarterly and picked up by Dark Reading or SC Media, accumulates citation authority faster than a 60-page annual report no journalist covers.
CISO-level placement in trade media
CSO Online, Dark Reading, and SC Media are the trade publications that appear most consistently in AI citations for security vendor queries. A contributed byline from a CISO or VP of Security at your organisation, placed in one of these publications monthly, builds the co-citation profile that AI engines recognise.
The byline content should directly answer the buyer questions in your prompt set. If your prompt set includes “how to choose between SIEM and XDR,” your byline should be titled something close to that question. AI engines use question-answer alignment to retrieve sources.
Analyst submission cadence
You do not need a Magic Quadrant placement to get analyst coverage. Forrester Wave, IDC MarketScape, and G2 category reports are all retrieval sources for security queries.
For Gartner coverage: submit for inclusion in the Market Guide if a Magic Quadrant placement is not yet in scope. Market Guide inclusions generate a named mention in a Gartner publication. That mention creates co-citation.
For Forrester: engage with the Wave evaluation process even if you do not expect a high placement. Being evaluated and named generates a citation record in Forrester’s domain.
For G2: increase the number of verified reviews. According to the citation patterns in SIEM and XDR queries, G2 category pages appear in AI answers regularly. A vendor with 15 reviews and a 4.5 rating appears in far fewer answers than a vendor with 300 reviews and a 4.2 rating. Review volume matters for retrieval probability.
Structured comparison pages
According to AirOps Research (April 2026), comparison pages containing three or more data tables earn 25.7% more AI citations than comparison content without tables, specifically for head-to-head product queries. This is a vendor-published study, so treat the exact figure with caution, but the directional finding is consistent with observable citation patterns.
Build a comparison page for each head-to-head prompt in your teardown set. If buyers ask “CrowdStrike vs SentinelOne XDR,” and you compete with both, you need a page titled exactly that, with a three-table structure:
- Feature comparison table (columns: your product, Competitor A, Competitor B)
- Pricing and deployment model table
- Use-case fit table (enterprise vs. mid-market, cloud-native vs. hybrid)
These pages get cited when AI engines handle comparison queries. They are also the entry point for buyers who already know the major names and are trying to evaluate alternatives.
Link your comparison pages from your glossary and product documentation. Internal linking signals that the page is canonical for the topic.
Step 4: Monitor your AI share of voice on a defined prompt set
Co-citation work takes time. You need a monitoring system to tell you when it starts moving.
Define your prompt cluster. Your teardown produced five to ten prompts. Group them by intent: evaluation queries, comparison queries, use-case queries. Track each group as a separate unit. If your share of voice improves on evaluation queries but not comparison queries, that tells you where the next intervention should go.
Track five runs per prompt per engine, weekly. Single-run dashboards report noise. Week-over-week drift on a defined prompt set is the signal.
Watch for source domain changes, not just brand mention rates. If a new threat-intelligence report you published starts appearing as a cited source, that is an earlier signal than a brand mention rate increase. Source-domain citations appear faster than brand mentions because they do not require your brand name to be in the retrieved text.
Track sentiment alongside presence. AI engines do not just mention you. They describe you. If Perplexity says “strong for enterprise but lacks mid-market pricing transparency,” that is a retrievable description that influences buyer decisions. Monitoring sentiment lets you correct the underlying sources before the description solidifies.
Tools that handle this at scale include Profound for citation-level attribution across 9-plus engines, Peec AI for unlimited-seat monitoring with source gap analysis, and Temso as an all-in-one option from $89 per month that converts monitoring data into a prioritised action queue. Scrunch AI adds an Agent Experience Platform that serves AI-optimised content directly to LLM crawlers, which can reduce the latency between publishing and retrieval. The full comparison is at /rankings/ai-visibility-tools.
Step 5: The recognition prerequisite pattern
One finding from the GrackerAI 2026 benchmark is worth treating as a structural constraint: 73% of cybersecurity vendors receive zero ChatGPT citations on category queries. (GrackerAI is a vendor selling AI visibility services, so the figure reflects a proprietary dataset, not an independent audit. Treat it as directionally accurate rather than a precise industry-wide measure.)
The practical implication is that citation recovery is binary at the start. You are not fighting for position 2 versus position 3. You are fighting to appear at all.
The recognition prerequisite pattern describes the condition for crossing that threshold. AI engines cite brands in category queries only after those brands have accumulated enough co-citation authority that the model treats them as a known answer. That threshold is not a fixed number. It is relative to the other brands in your category.
In a category where every other vendor has 20 trade media mentions and three analyst inclusions, you need roughly the same. In a category where competitors have 200 mentions each, you need more.
The teardown gives you the relative gap. The co-citation playbook above closes it. The monitoring system tells you when you have crossed the threshold.
What to do in the next 30 days
Start with the teardown. It costs nothing and gives you the specific source domains to target.
Then choose one co-citation channel to activate first. If your organisation has a security research team, a threat-intelligence report is the highest-authority asset you can build. If you have a CISO with a perspective on SIEM versus XDR, a contributed byline on Dark Reading is faster to produce and starts earning citations within weeks of publication.
Set up a monitoring system before you begin. You want a baseline. Without a baseline, you cannot tell whether the co-citation work is moving your AI share of voice or not.
Start tracking your prompt cluster in Profound, Peec AI, or Temso before you publish anything new. Run five prompts, five times each, across four engines. Record the results. That is your week-zero baseline.
Then publish. Then track the delta.
The full AI visibility tool ranking and the methodology behind it are at the links below. The glossary covers share of voice, citation rate, co-citation, and the other terms used in this piece.