AI Visibility vs. Competitor Share of Voice: Cybersecurity Category Map

Published February 2026 · 12 Categories Mapped · 120+ Vendors Scored · 6 AI Platforms · 500+ Buyer Prompts

Key Findings

• Top 5 vendors in each cybersecurity category capture 65–85% of all AI citations — the "winner take most" effect is stronger in AI search than Google search
• 3 of 12 cybersecurity categories have significant disruption opportunity — where no single vendor holds more than 25% AI share of voice
• 42% gap between the top-cited vendor and the #6 vendor in the average cybersecurity category — the citation cliff is steep
• 2.3× variance in share of voice rankings between AI platforms — a vendor may dominate on ChatGPT but be invisible on Perplexity

1. Understanding AI Share of Voice

1.1 Definition and Methodology

AI Share of Voice (AI-SoV) measures the percentage of AI-generated responses in a given category that cite or recommend a specific vendor. It's calculated by testing a set of category-relevant buyer prompts across AI platforms and tracking which vendors appear in each response.

Formula: AI-SoV = (Number of responses citing Vendor X / Total responses tested in category) × 100. Example: If we test 50 "best EDR" prompts and CrowdStrike appears in 38 responses, CrowdStrike's AI-SoV for EDR = 76%.

1.2 Why AI-SoV Differs from Traditional SoV

1.3 The Concentration Effect

The most important structural difference: AI search concentrates share of voice far more than traditional search. In Google's organic results, 10 sites share page 1 for any given keyword — plus ads, knowledge panels, and People Also Ask. In an AI response, typically 3–5 vendors are mentioned, and the first vendor named receives disproportionate attention. This means AI-SoV is a winner-take-most game where the top 3 vendors in each category capture the vast majority of buyer attention.

2. Category-by-Category AI Share of Voice Maps

The following analysis covers 12 major cybersecurity categories. For each category, we tested 40–50 buyer prompts across all major AI platforms and mapped the resulting citation patterns.

2.1 Endpoint Detection & Response (EDR/XDR)

Category assessment: Highly concentrated. CrowdStrike has near-dominant AI-SoV. Disruption is difficult but not impossible — emerging vendors can compete on specific verticals (healthcare EDR, OT/IoT endpoint protection) where the leaders have less specialized content.

2.2 SIEM / Security Analytics

Category assessment: Moderate concentration with legacy bias. AI platforms over-cite Splunk and IBM QRadar relative to their current market position, likely due to training data weight. Next-gen SIEM vendors have an opportunity to gain AI-SoV through content that emphasizes modern architecture differentiators.

2.3 Cloud Security (CNAPP / CSPM / CWPP)

Category assessment: Disruption opportunity. This is one of the most dynamic categories, with no vendor exceeding 65% AI-SoV. Wiz leads but hasn't locked in dominance. Newer entrants with strong cloud-native content can realistically compete for top-3 positions, particularly on Perplexity and Google AI Overviews.

2.4 Identity & Access Management (IAM / PAM)

Category assessment: Moderately concentrated with clear segment leaders. Okta dominates general IAM, CyberArk dominates PAM. Opportunity exists in emerging sub-categories like machine identity management and decentralized identity.

2.5 Additional Categories — Summary View

3. Cross-Platform Variance: The Hidden Complexity

3.1 The Platform Divergence Problem

One of the most important findings: AI share of voice rankings differ significantly across platforms. A vendor that dominates ChatGPT recommendations may be nearly invisible on Perplexity, and vice versa. This means tracking your AI-SoV on a single platform gives an incomplete — and potentially misleading — picture of your competitive position.

3.2 Why Rankings Diverge

3.3 Strategic Implication

The platform divergence means cybersecurity vendors need platform-aware competitive strategies. If your primary competitor dominates ChatGPT (due to training data incumbency) but you can compete on Perplexity (where content quality and recency matter more), you should prioritize Perplexity-optimized content to build AI-SoV where the playing field is more level.

4. Identifying Your Competitive Opportunities

4.1 The Opportunity Matrix

Not all AI-SoV gaps are equally worth pursuing. Use this framework to prioritize where to invest:

4.2 Sub-Category Opportunities

Even in highly concentrated categories, AI-SoV opportunities exist at the sub-category level. For example, while CrowdStrike dominates EDR overall, no vendor dominates queries for:

• "Best EDR for healthcare with HIPAA compliance"
• "EDR solutions for OT/ICS environments"
• "Lightweight EDR for Linux servers"
• "EDR with built-in vulnerability management for SMBs"

These sub-category queries represent specific buyer needs where targeted content can capture AI-SoV regardless of the overall category leader's dominance. The strategy: own the long-tail of AI recommendations through deeply specific, use-case-focused content.

5. Building Your AI Share of Voice Strategy

5.1 The AI-SoV Growth Playbook

1. Baseline your AI-SoV. Test 40–50 buyer prompts per category across all major platforms. Document your current share of voice and identify the top 5 competitors in each category.
2. Identify citation gaps. For each prompt where you're not cited but a competitor is, analyze what content the competitor has that you don't. Is it a comparison page? Technical documentation? Third-party coverage?
3. Build a citation content calendar. Prioritize content creation based on the gap analysis: comparison pages first (highest citation impact), then technical documentation, then educational content.
4. Optimize for platform-specific strengths. If you can't compete on ChatGPT (training data incumbent advantage), focus on Perplexity (content quality wins) and Google AI Overviews (SEO foundation + structure).
5. Monitor weekly and adjust. AI-SoV shifts are detectable within 2–4 weeks of content improvements on retrieval-based platforms. Track weekly and adjust content priorities based on what's working.

5.2 Defensive Strategies for Category Leaders

If you currently hold a strong AI-SoV position, maintaining it requires active investment:

• Content freshness: Update key pages monthly. Competitors can overtake you on Perplexity and Google AIO if their content is more current.
• Sub-category coverage: Don't leave niche queries undefended. As competitors create vertical-specific content, they chip away at your overall AI-SoV.
• Third-party amplification: Invest in analyst relations, media coverage, and review site presence. These third-party citations reinforce your first-party content advantage.
• Monitor competitor content. When a competitor launches new comparison pages or technical documentation, respond quickly with equivalent or better content.

6. The AI-SoV Flywheel Effect

AI share of voice exhibits a powerful flywheel effect that benefits early movers:

1. High AI-SoV → more buyer exposure → buyers become familiar with your brand through AI recommendations
2. More buyer exposure → more branded searches → buyers search specifically for your brand, reinforcing your authority signal
3. More branded searches → more third-party coverage → media and analysts write about brands buyers are searching for
4. More third-party coverage → higher AI-SoV → AI engines have more sources to cite when recommending you

This flywheel makes AI-SoV increasingly difficult for competitors to challenge once established. The competitive implication is clear: the cost of building AI-SoV increases every quarter you delay, as leaders' flywheel effects compound.

7. Measuring and Tracking AI Share of Voice

7.1 Recommended Tracking Cadence

• Weekly: Track AI-SoV for your top 10 most important buyer prompts. This provides early warning of competitive shifts.
• Bi-weekly: Expand to full 40–50 prompt testing across all categories where you compete.
• Monthly: Full cross-platform analysis with competitive benchmarking report.
• Quarterly: Strategic review including sub-category opportunities, platform-specific trends, and content gap analysis.

7.2 Key Metrics Dashboard

8. Frequently Asked Questions

How is AI share of voice different from traditional share of voice?

Traditional SoV measures keyword ranking coverage. AI-SoV measures how often AI platforms recommend your brand in response to buyer queries. AI-SoV has a much more direct impact on purchase decisions because AI recommendations are treated as personalized endorsements, not just search results.

Can a smaller cybersecurity vendor realistically compete with CrowdStrike's AI-SoV?

In the overall EDR category, overtaking CrowdStrike's 78% AI-SoV is extremely difficult. However, smaller vendors can build dominant AI-SoV in specific sub-categories: healthcare EDR, OT/ICS endpoint protection, SMB-focused solutions. These targeted niches collectively represent significant pipeline value.

How quickly can AI-SoV change?

On retrieval-based platforms (Perplexity, Google AIO), content improvements can shift AI-SoV within 2–4 weeks. On training-data platforms (ChatGPT base model), shifts take longer — typically aligning with model update cycles (quarterly). A comprehensive strategy targets both.

What's the relationship between AI-SoV and pipeline?

Research suggests a 10-percentage-point increase in AI-SoV in a cybersecurity category correlates with approximately 15–25% more inbound demo requests from buyers who cite AI as a discovery channel. The correlation strengthens for high-intent prompt categories (comparisons, alternatives).

9. About This Research

About GrackerAI

GrackerAI is the pioneering AI-powered AEO and GEO platform built specifically for B2B SaaS companies. The platform helps businesses get discovered and cited by AI search engines including ChatGPT, Perplexity, Claude, Gemini, and Microsoft Copilot.

Methodology

This report analyzed 500+ cybersecurity buyer prompts tested across six AI platforms between September 2025 and January 2026. Share of voice calculations are based on citation frequency across all tested prompts per category. Vendor rankings reflect AI citation frequency, not market share or product quality assessments. Results are directional benchmarks — AI platform behaviors change frequently.