How Can My Security Firm Get Recommended by AI?

The shift: from analyst reports to “ask the AI”

Cybersecurity procurement has always been research-intensive. CISOs consult Gartner Magic Quadrants, read Forrester Wave reports, attend RSA and Black Hat, and rely on peer recommendations. But the starting point of that research journey is shifting — from analyst portals and peer networks to AI chatbots.

83% of enterprise technology purchases now involve AI-assisted research in the discovery phase, according to IDC’s 2024 B2B Buyer Behavior Study. Gartner’s own data shows that 75% of B2B buyers prefer a rep-free buying experience, meaning they want to self-serve through the research and shortlisting process before engaging a vendor. In cybersecurity, where technical complexity makes self-directed research difficult, AI chatbots are filling that gap fast.

The queries are changing. Instead of downloading a 40-page Gartner Magic Quadrant, a security architect asks ChatGPT: “Compare the top XDR platforms for a mid-market company with 2,000 endpoints.” Instead of reading through G2 reviews, a CISO asks Perplexity: “What are the best SIEM solutions for compliance-heavy industries?” Instead of scheduling five vendor demos, a security team asks Gemini: “Rank the top managed security service providers for cloud workloads.”

Gartner forecast in February 2024 that traditional search engine volume will drop 25% by 2026 due to AI chatbots and virtual agents. ChatGPT surpassed 5.8 billion monthly visits by mid-2025. Perplexity AI grew to over 100 million monthly visits by Q4 2024. For cybersecurity buyers — who are by nature technically sophisticated and early adopters — AI-assisted vendor research adoption is even higher than the general population.

The Cybersecurity Ventures 2024 Annual Report estimated that a new cyberattack occurs every 39 seconds, creating constant urgency for security teams to evaluate and procure solutions quickly. AI chatbots promise to compress weeks of vendor research into minutes. That speed advantage is accelerating the shift away from traditional discovery channels — and reshaping which vendors make the shortlist.

The traditional funnel — Gartner report → peer recommendation → vendor demo → POC → purchase — is being compressed. AI is inserting itself at the top, and the vendors it names in its initial response often define the entire consideration set. If your firm isn’t in that first AI-generated shortlist, you may never reach the RFP stage.

Who AI actually recommends for cybersecurity

We tested extensively. Across hundreds of queries to ChatGPT, Perplexity, Gemini, Claude, and Grok, using buyer-intent prompts like “What are the best endpoint protection platforms?” “Top SIEM solutions for enterprises,” “Best managed security service providers,” and “Compare cloud security platforms” — the same names dominate:

* AI mention rate based on Metricus internal testing across ChatGPT, Perplexity, Gemini, Claude, and Grok using 200+ buyer-intent cybersecurity queries (2026). Rates vary by query category — endpoint queries favor CrowdStrike/SentinelOne; cloud security queries favor Wiz/Palo Alto.

The pattern is stark. CrowdStrike — publicly traded (NASDAQ: CRWD), with $3.44 billion in annual recurring revenue (FY2025 earnings report) and relentless marketing presence at every security conference — dominates AI responses. Palo Alto Networks, with $6.9 billion in annual revenue (FY2024 annual report) and leadership positions across multiple Gartner Magic Quadrants, follows closely. Fortinet, the third publicly traded cybersecurity giant with $5.3 billion in revenue (FY2023), rounds out the top tier.

The vast majority of cybersecurity vendors — including hundreds of firms with proven technology, deep domain expertise, and loyal customer bases — are virtually invisible in AI responses. Regional MSSPs with 99.9% SLA track records, niche application security firms with superior detection rates, and specialized OT/ICS security companies protecting critical infrastructure are all absent from AI’s recommendations.

This isn’t a quality judgment. It’s a corpus frequency problem. And for an industry where vendor selection directly impacts organizational security posture, the consequences extend beyond lost revenue — they affect the security of the enterprises relying on AI for vendor discovery.

Why your security firm is invisible to AI

AI chatbots generate recommendations based on patterns in their training data — billions of web pages, analyst reports, CVE databases, threat intelligence publications, Reddit threads, and enterprise review platforms. The brands that appear most frequently and authoritatively in that data are the ones AI recommends.

Consider the math:

• CrowdStrike generates roughly 15–18 million monthly website visits (SimilarWeb, 2024), publishes hundreds of threat intelligence reports annually, has thousands of analyst mentions, SEC filings, conference presentations, and is referenced in CISA advisories.
• Palo Alto Networks generates approximately 12–15 million monthly visits, publishes Unit 42 threat research cited by media globally, and holds leadership positions in 5+ Gartner Magic Quadrants simultaneously.
• Fortinet generates approximately 8–10 million monthly visits and has an extensive network of FortiGuard Labs research publications and certifications content.
• The average mid-market cybersecurity vendor receives 5,000–50,000 monthly website visits, has limited analyst coverage (maybe a single Gartner mention or one Forrester Now Tech inclusion), and appears on perhaps 10–20 third-party sites (G2, Gartner Peer Insights, a few industry publications).

That’s a 300x–3,000x gap in web presence. And web presence is what AI systems learn from.

Four specific factors determine whether AI mentions your cybersecurity brand:

1. Corpus frequency: How often your brand appears across the web. CrowdStrike has tens of thousands of mentions across analyst reports, news coverage, earnings analyses, conference recaps, and security community discussions. A mid-market endpoint vendor might have 500–2,000 total web mentions.
2. Source authority: AI weights authoritative sources more heavily. CrowdStrike gets covered in the Wall Street Journal, cited in CISA advisories, referenced in Gartner Magic Quadrants, and evaluated in MITRE Engenuity ATT&CK assessments. A smaller vendor gets a mention in a niche security blog — which AI weights far less.
3. Terminology alignment: This is the #1 hidden factor. If your website describes your offering as “next-gen unified threat management” but buyers ask AI about “XDR platforms,” AI cannot connect your product to the query. More on this below.
4. Content structure: The Princeton/Georgia Tech GEO study (Aggarwal et al., 2023) found that content with statistical citations and clear factual claims was up to 40% more likely to be cited by generative AI systems. Most cybersecurity vendor websites have marketing copy (“industry-leading protection,” “AI-powered security”) with no benchmarks, detection rates, or measurable claims AI can extract and cite.

Most mid-market cybersecurity vendors fail on all four. They have low corpus frequency, limited authoritative mentions, misaligned terminology, and marketing-heavy content with no structured data AI can use. To understand these dynamics more broadly, read our guide on how brands show up in AI recommendations.

The terminology mismatch problem

This deserves its own section because it’s the single highest-impact finding from our cybersecurity AI visibility research — and it’s the most fixable.

62% of cybersecurity firms we analyzed describe their offerings using terminology that doesn’t match how buyers query AI. This creates a fundamental disconnect: the firm has the capability the buyer needs, but AI can’t bridge the vocabulary gap.

Cybersecurity is uniquely vulnerable to this problem because the industry has a proliferation of overlapping, evolving category names. Consider just the endpoint security space:

• EPP (Endpoint Protection Platform) — the legacy Gartner category
• EDR (Endpoint Detection and Response) — the detection-focused evolution
• XDR (Extended Detection and Response) — the current Gartner-defined category combining endpoint, network, cloud, and identity telemetry
• MDR (Managed Detection and Response) — the managed service wrapper
• NGAV (Next-Generation Antivirus) — the marketing term many vendors still use

A vendor whose website prominently features “next-generation endpoint security” without explicitly mapping to XDR, EDR, or MDR terminology is invisible to every buyer who asks AI about those categories — even if their product delivers exactly those capabilities.

The same pattern repeats across every cybersecurity domain:

• Cloud security: CSPM vs. CWPP vs. CNAPP vs. “cloud workload protection” vs. “cloud-native security”
• Network security: SASE vs. SSE vs. ZTNA vs. “zero trust network access” vs. “secure access”
• Security operations: SIEM vs. SOAR vs. “security analytics” vs. “threat intelligence platform”
• Application security: SAST vs. DAST vs. SCA vs. “DevSecOps” vs. “software supply chain security”

When a CISO asks ChatGPT “What are the best CNAPP solutions?” the AI searches its training data for content that explicitly discusses CNAPP capabilities with measurable claims. If your cloud security platform’s website never uses the term “CNAPP” and instead describes itself as a “comprehensive cloud security platform,” you are invisible to that query — regardless of your actual capabilities.

The fix is straightforward but requires discipline: map every capability your product delivers to the exact Gartner category name, the MITRE ATT&CK techniques it addresses, and the NIST Cybersecurity Framework (CSF) functions it fulfills. Use those terms explicitly and repeatedly in your web content. AI is literal — it matches vocabulary, not intent.

What AI gets wrong about cybersecurity vendors

Even when AI does mention a cybersecurity vendor, accuracy is a significant problem. Our testing found AI gives incorrect or outdated information in approximately 40–55% of cybersecurity-specific vendor queries. In an industry where technical accuracy directly impacts security posture, this error rate is dangerous. For more on this problem, see our deep dive on fixing AI hallucinations about your brand.

The most common errors we find in AI responses about cybersecurity vendors:

Product capabilities and category placement

Cybersecurity vendors frequently acquire companies and expand into adjacent categories. AI often lags these changes by 12–24 months. A vendor that acquired a SOAR company in 2024 may still be described by AI as “primarily an endpoint security vendor” because the training data pre-dates the acquisition. Palo Alto Networks, which has made over 20 acquisitions in the past decade (Crunchbase, 2024), is frequently described with outdated capability sets. CrowdStrike’s expansion from endpoint into identity protection, cloud security, and log management (through its Falcon platform) is often incompletely represented.

Pricing and licensing models

Cybersecurity pricing is notoriously opaque and complex — per-endpoint, per-user, per-asset, consumption-based, or platform bundles. AI frequently fabricates specific pricing that bears no relationship to actual contract values. When asked “How much does CrowdStrike cost per endpoint?” AI often cites figures from outdated blog posts or comparison sites that don’t reflect current list pricing (which itself varies dramatically by deal size, term length, and module selection). For a mid-market company, the difference between AI’s cited price and actual contract price can be 40–60%.

MITRE ATT&CK evaluation results

The MITRE Engenuity ATT&CK evaluations are the closest thing cybersecurity has to standardized product testing. AI frequently misrepresents these results — citing detection percentages from old evaluation rounds (the evaluations run annually with different threat scenarios), conflating “visibility” with “detection,” or attributing results from one evaluation round to a different vendor. Since security teams use MITRE results as a key evaluation criterion, these errors directly influence procurement decisions.

Compliance and certification claims

AI frequently makes incorrect claims about vendor certifications — FedRAMP authorization status, SOC 2 Type II compliance, ISO 27001 certification, or StateRAMP readiness. Given that compliance requirements often gate vendor selection (a vendor without FedRAMP authorization cannot sell to most federal agencies, per CISA guidance), these errors can cause buyers to include or exclude vendors based on false information.

Integration ecosystem

Modern cybersecurity is platform-centric, and integration capabilities are a top-3 buyer concern. AI frequently lists incorrect or outdated integration partners, cites deprecated APIs, or claims compatibility that doesn’t exist. A security team that builds a shortlist based on AI’s claim that Vendor X integrates with their existing SIEM may discover during POC that the integration is limited or nonexistent.

The $225 billion market AI is reshaping

The global cybersecurity market is massive — and accelerating:

• The global cybersecurity market was valued at $225.6 billion in 2024 and is projected to reach $562 billion by 2032, growing at a 12.3% CAGR (Fortune Business Insights, 2024).
• Cybersecurity Ventures projects global cybersecurity spending will exceed $1.75 trillion cumulatively from 2021 to 2025 (Cybersecurity Ventures Annual Report, 2024).
• The average enterprise spends $2,700 per employee annually on cybersecurity (Deloitte/NASCIO Cybersecurity Study, 2024), with financial services and healthcare spending significantly more.
• CrowdStrike alone reached $3.44 billion in annual recurring revenue in FY2025, growing 32% year-over-year (Q4 FY2025 earnings).
• Palo Alto Networks reported $6.9 billion in total revenue for FY2024, with next-gen security ARR surpassing $3.8 billion (FY2024 annual report).
• Wiz reached $500 million in ARR in record time for a cybersecurity startup, demonstrating how cloud security demand is creating new market leaders at unprecedented speed (TechCrunch, 2024).

Yet despite the market’s size, vendor discovery remains remarkably concentrated. A 2024 IANS Research study found that 68% of CISOs evaluate no more than 3–4 vendors for any given security purchase. The vendors that make that initial shortlist win the deal more than 80% of the time. AI is increasingly determining that shortlist.

The managed security services (MSSP/MDR) segment alone is projected to reach $68 billion by 2028 (MarketsandMarkets, 2024). This segment is especially vulnerable to AI-driven discovery shifts because MSSP selection has traditionally relied on peer referrals and Gartner’s annual Market Guide — both of which AI is now synthesizing and replacing with its own narrative recommendations.

You can’t buy your way into a ChatGPT recommendation. There are no ad slots. You have to earn it through web presence, authoritative technical content, and structured data. And right now, only 5–7 vendors are earning it in any given security category. For more on why this matters across B2B, see why B2B SaaS brands are invisible in ChatGPT.

How security buyers actually evaluate vendors — and what AI misses

Understanding what drives cybersecurity purchase decisions reveals the depth of AI’s blindspot. The IANS Research CISO Survey (2024), Gartner’s Technology Buyers research, and SANS Institute procurement studies consistently identify these top decision factors:

1. Detection efficacy and false positive rates — 91% of security leaders rate this as “critical” (SANS, 2024). MITRE ATT&CK evaluation results, AV-TEST scores, and SE Labs certifications provide objective benchmarks. AI rarely cites specific, current benchmark data and often conflates results across evaluation periods.
2. Integration with existing stack — 86% of buyers need confirmed compatibility with their SIEM, SOAR, identity provider, and cloud platforms. AI provides generic integration claims without validating specific version compatibility or API depth.
3. Total cost of ownership (TCO) — 82% of CISOs evaluate 3-year TCO, not just license cost. AI cannot calculate TCO because it requires deployment-specific variables (endpoints, data volume, FTE requirements, professional services). AI’s pricing guidance is almost always misleading.
4. Vendor financial stability and viability — 78% of enterprise buyers evaluate this, especially post-startup-bust. AI doesn’t systematically assess financial health — it’s equally likely to recommend a well-funded company and one burning through its last funding round.
5. Compliance coverage — 75% need specific regulatory framework alignment (NIST CSF, SOC 2, HIPAA, PCI DSS, FedRAMP, CMMC). AI frequently makes incorrect compliance claims.
6. Mean time to detect (MTTD) and mean time to respond (MTTR) — 72% of buyers want quantified response metrics. These are the most critical operational KPIs for security tools, yet AI almost never provides vendor-specific MTTD/MTTR data.
7. Customer support and SLA guarantees — 69% evaluate support quality and contractual SLAs. AI cannot assess support quality, which varies dramatically between vendors and even between account tiers within the same vendor.

The fundamental mismatch: security buyers need specific, current, validated technical data. AI provides generic, often outdated, surface-level brand recommendations. This is the gap your cybersecurity firm can fill — if AI knows you exist.

The gap between traditional analyst-driven discovery and AI-driven discovery for cybersecurity is widening. On Gartner Peer Insights or G2, a well-reviewed mid-market vendor can compete by accumulating genuine customer reviews. In AI chatbot responses, those reviews barely register against the sheer volume of content the top vendors generate. Learn more about how we measure AI visibility across these channels.

What actually works: the AI visibility playbook for cybersecurity

The good news: AI visibility is a solvable problem. And because the cybersecurity industry is heavily focused on traditional analyst relations and SEO while largely ignoring AI-specific optimization, early movers have a disproportionate advantage. Here’s what works, based on our research into turning AI visibility data into action.

1. Audit what AI currently says about you

Before fixing anything, you need to know what’s broken. Query ChatGPT, Perplexity, Gemini, and Claude with prompts your buyers would actually use:

• “What are the best [your category: XDR / SIEM / CNAPP / MSSP] solutions?”
• “Compare [your company name] vs. [top competitor]”
• “Best cybersecurity solutions for [your target industry: healthcare / financial services / manufacturing]”
• “What is the best managed security service provider for mid-market companies?”
• “Does [your company name] have FedRAMP authorization?”

Document every mention (or absence), every error, and every competitor that appears instead of you. Or run a Metricus AI visibility report that does this across hundreds of query variations automatically. For a quick start, try our free AI visibility check.

2. Fix your terminology alignment

This is the highest-ROI action for most cybersecurity vendors. Map every product capability to:

• Gartner category names: If Gartner calls it “Cloud-Native Application Protection Platform (CNAPP),” your website needs to use “CNAPP” explicitly — not just “cloud security platform.”
• MITRE ATT&CK techniques: Specify which ATT&CK tactics and techniques your product detects or prevents. “We detect lateral movement” is weaker than “We detect 47 of 52 MITRE ATT&CK lateral movement techniques including T1021 (Remote Services), T1072 (Software Deployment Tools), and T1570 (Lateral Tool Transfer).”
• NIST CSF functions: Explicitly map capabilities to Identify, Protect, Detect, Respond, and Recover functions. CISA and NIST documentation are among the most authoritative sources in AI training data.
• Compliance frameworks: List every framework your product helps address (NIST 800-53, PCI DSS 4.0, HIPAA Security Rule, SOC 2 Type II, CMMC 2.0, FedRAMP) with specific control mappings where possible.

3. Publish data-rich, citable technical content

AI systems cite content that contains structured claims, benchmarks, and authoritative data. The GEO research from Princeton/Georgia Tech found that content with statistical citations was up to 40% more likely to be cited by generative AI.

For cybersecurity, this means:

• Detection benchmark pages with specific metrics: “99.7% malware detection rate in AV-TEST independent evaluation (January 2026), with 0.02% false positive rate across 15,000 clean samples.”
• MITRE ATT&CK coverage pages with technique-level specifics: “Coverage for 142 of 196 ATT&CK techniques across 14 tactics in the Enterprise matrix, with analytic detection for 89% and telemetry for 96%.”
• Mean time to detect (MTTD) and respond (MTTR) data with methodology: “Average MTTD of 4.2 minutes across our customer base of 850 enterprise deployments (Q4 2025 data), measured from first telemetry event to alert generation.”
• Compliance mapping documentation that explicitly maps product capabilities to specific framework controls — not just “we help with PCI DSS” but “Requirement 6.4.2: We perform automated code reviews using SAST scanning that detects OWASP Top 10 vulnerabilities.”
• Threat intelligence research — original threat research, vulnerability analyses, and incident response findings that demonstrate domain expertise and get cited by other publications.

4. Build citations on authoritative third-party sources

AI doesn’t just read your website. It reads everything about you across the web. The sources that carry the most weight for cybersecurity:

• Gartner Peer Insights with detailed, verified customer reviews (aim for 50+ reviews with technical depth)
• G2 profile with comprehensive product information and active review management
• MITRE Engenuity ATT&CK evaluations (if eligible) — participation is one of the strongest signals
• AV-TEST and SE Labs certifications — independent testing results AI heavily weights
• CISA advisories and NIST NVD references — if your threat intelligence or vulnerability disclosures are referenced by CISA, this is extremely high-authority
• Industry publications: Dark Reading, SC Magazine, CSO Online, SecurityWeek, The Record — even a single feature article in a major security publication generates more AI weight than 100 blog posts on your own site
• Reddit security communities: r/cybersecurity, r/netsec, r/sysadmin, r/blueteamsec — AI heavily weights community discussions, and genuine mentions in these subreddits carry significant influence
• GitHub presence: Open-source contributions, security tools, and documentation on GitHub are high-authority signals for AI

5. Implement structured data

Add comprehensive schema markup to your website:

• Organization schema with complete company information, founding date, employee count, and service areas
• Product schema for each product line with features, specifications, and pricing model descriptions
• FAQPage schema for common buyer questions (pricing, deployment, integrations, compliance)
• Review and AggregateRating schema where applicable
• SoftwareApplication schema with operating system compatibility and feature sets

Structured data helps AI systems understand what your business offers and how it maps to buyer queries — even when your website has less raw content than the publicly traded giants.

6. Correct errors at their source

If AI is getting your capabilities, pricing model, compliance status, or integration ecosystem wrong, the error is coming from somewhere. Usually it’s an outdated G2 profile, stale analyst mention, an old comparison blog post, or inconsistent data across your own web properties. Find the source, fix it, and the AI corrections will follow over time as models retrain on updated data.

The case for auditing your AI visibility now

The cybersecurity market is at an inflection point. Cybercrime damage is projected to reach $10.5 trillion annually by 2025 (Cybersecurity Ventures, 2024), up from $3 trillion in 2015. This escalation is driving unprecedented enterprise security spending — and the vendors that capture that spend are increasingly the ones AI recommends.

The math for cybersecurity vendors is stark. The average enterprise cybersecurity deal ranges from $50,000 to $500,000+ annually depending on organization size and scope. For managed security services, multi-year contracts regularly exceed $1 million in total contract value. If even 10% of initial vendor discovery is now AI-mediated (a conservative estimate given IDC’s 83% AI-assisted research finding), the pipeline impact of AI invisibility is measured in millions.

Consider a mid-market cybersecurity vendor with 500 qualified leads per year and an average deal value of $150,000. If 10% of those leads are now starting their research with AI, that’s 50 leads whose shortlist is determined by AI. If AI never mentions your firm, you lose access to those 50 leads entirely — representing $7.5 million in potential annual pipeline. At a 20% close rate, that’s $1.5 million in lost revenue per year.

For managed security service providers (MSSPs), the calculation is even more dramatic. MSSP contracts are typically 3–5 year commitments. A single lost $300,000/year contract due to AI-driven discovery failure costs $900,000–$1.5 million in lifetime value. Multiply that across the dozens of procurement cycles happening monthly in your target market, and the cumulative cost of AI invisibility becomes a board-level concern.

The cybersecurity vendors that understand their AI visibility now — while competitors are still focused exclusively on Gartner analyst relations and Google Ads — will have a structural advantage that compounds over time. Every piece of authoritative, data-rich, properly-categorized content you publish today enters the training data that shapes AI recommendations tomorrow.

The CISA Cybersecurity Performance Goals (CPGs) released in 2024 further emphasize the importance of vendor visibility: as more organizations adopt standardized security frameworks, the vendors whose capabilities map cleanly to CISA CPGs and NIST CSF functions will be the ones AI recommends to the growing number of organizations using AI to evaluate their security posture.

Sources: Gartner B2B Buying Survey (2024); IDC B2B Buyer Behavior Study (2024); Cybersecurity Ventures Annual Cybercrime Report (2024); CISA Cybersecurity Performance Goals (2024); NIST Cybersecurity Framework 2.0 (2024); Fortune Business Insights cybersecurity market report (2024); MarketsandMarkets managed security services forecast (2024); IANS Research CISO Survey (2024); SANS Institute procurement study (2024); Deloitte/NASCIO Cybersecurity Study (2024); CrowdStrike FY2025 earnings report; Palo Alto Networks FY2024 annual report; Fortinet FY2023 annual report; Wiz ARR reporting (TechCrunch, 2024); MITRE Engenuity ATT&CK evaluations (2024); Gartner Magic Quadrant for Endpoint Protection Platforms (2024); Princeton/Georgia Tech GEO study (Aggarwal et al., 2023); SimilarWeb traffic estimates (2024); Crunchbase acquisition data (2024). AI mention rates based on Metricus internal testing across ChatGPT, Perplexity, Gemini, Claude, and Grok (2026). Learn more about how we measure AI visibility.

Related reading

• The 5-step AI visibility action plan — the general framework for turning audit findings into fixes.
• Fixing AI hallucinations about your brand — the deep dive on correcting factual errors at their source.
• What is AI visibility? — the complete explainer on how brands appear in AI.
• Why B2B SaaS brands are invisible in ChatGPT — the same dynamic in a different industry, with transferable strategies.
• Free AI visibility check — run a quick manual check before ordering a full report.
• AI visibility scores explained — how Metricus measures and benchmarks AI visibility.