SIEM Software: AI Visibility Market Discovery Index

Benchmark Summary

Questions This Section Answers

• Who leads the SIEM Software benchmark in October 2026, and by how much?
• Which brands moved beyond normal variation this month?
• How did recommendation-shaped answer share change since the July 2026 baseline?

Splunk leads the SIEM Software benchmark with valid recommendation coverage of 42.0% in October 2026, up from 35.8% in July 2026. The gap to the next brand is 13.7 points, with Elastic Security at 28.3% in October 2026. Elastic Security recorded the largest coverage increase, rising beyond normal variation. The largest coverage decline belonged to IBM QRadar, falling from 17.5% in July 2026 to 0.0% in October 2026.

Over the four comparable months, Splunk moved from 35.8% in July 2026 to 42.0% in October 2026, a rise within normal variation across the full series but including a significant prior-to-current move of 10.1 points. Elastic Security rose significantly in the current month, moving from 17.4% in September 2026 to 28.3% in October 2026. IBM QRadar declined in each of the two months since August 2026. The month was mixed, with one significant riser and one significant decliner.

The benchmark began with 780 prompt-surface observations in October 2026 and produced 283 qualified observations after qualification.

Current Benchmark at a Glance

Recommendation-shaped answer share rose 8.9 points while the qualified denominator fell by 72 observations. Qualified surface breadth counts the canonical AI/search surface families that produced at least one qualified observation. The six families are ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending SIEM Software

Research Scope and Qualification

Questions This Section Answers

• What is the difference between source prompt-surface observations and the qualified benchmark denominator?
• How many brand and competitor mentions qualified for the public SIEM Software benchmark in October 2026?

The public benchmark is narrower than the raw collection universe by design. Brand-level percentages use the qualified observations as the public denominator, not the raw collection.

Irrelevant observations rose from 279 to 341 while qualified observations fell, narrowing the public benchmark denominator.

For methodology details, see AI Visibility Market Discovery Methodology.

Current Brand Standings

Questions This Section Answers

• Which SIEM vendors have the highest valid recommendation coverage in October 2026?
• How do top-three and rank-one rates differ across the leading SIEM brands?
• Which brands are mentioned but not actually recommended?

The table below is the primary current-month benchmark view, sorted by valid recommendation coverage.

How to Read the Standings

Presence rate is the share of qualified observations in which the brand is mentioned at all. Valid recommendation coverage is the share of qualified observations in which the brand receives a clear, actionable recommendation. Top-three rate is the share of qualified observations in which the brand appears among the top three recommended options. Rank-one rate is the share of qualified observations in which the brand is the single first recommendation. Net sentiment reflects the balance of positive versus negative mentions, from -1 to +1.

For formulas and denominator rules, see AI Visibility Market Discovery Metric Definitions.

Recommendation Coverage Movement

Questions This Section Answers

• Which SIEM brand gained the most recommendation coverage since the July 2026 baseline?
• Which brand declined most, and how did that decline unfold month over month?
• Which September-to-October moves fell outside normal variation?

The table below tracks each brand's valid recommendation coverage from the July 2026 baseline to the October 2026 current period.

In the prior-to-current month comparison, Splunk, Elastic Security, and IBM QRadar all moved beyond normal variation. Three brands broke out of normal variation in a single month. Securonix and Rapid7 InsightIDR moved within normal variation.

Largest Increase: Elastic Security

Elastic Security recorded the largest coverage increase, rising from 17.2% in July 2026 to 28.3% in October 2026, a move of 11.1 points beyond normal variation. Its top-three rate rose significantly from 3.4% to 10.6%, but its rank-one rate remained at 0.0%. Presence rate rose from 41.1% to 53.0%, and net sentiment held steady at 0.6.

Largest Decline: IBM QRadar

IBM QRadar recorded the largest coverage decline, falling from 17.5% in July 2026 to 0.0% in October 2026, a move of 17.5 points beyond normal variation. The decline compounded across two consecutive months, ending at zero coverage. Its presence rate was 45.6% in July 2026; no presence rate is recorded for October 2026. Its rank-one rate was 0.0% in both months.

Category Leader: Splunk

Splunk remained the coverage leader in October 2026 at 42.0%, up from 35.8% in July 2026, though the full-series movement was within normal variation. A stable full-series reading still included a 10.1-point prior-to-current jump beyond normal variation. Presence rate rose from 90.4% to 93.3%, top-three rate rose from 30.7% to 34.6%, and rank-one rate rose from 13.5% to 14.8%. Net sentiment held steady at 0.6.

Recommendation Placement Snapshot

Questions This Section Answers

• How do top-three and rank-one placement rates compare across the leading SIEM brands?
• Which brands hold comparable coverage but fall short in first-position recommendations?

Coverage alone does not show how prominently a brand is recommended. Among the leading brands, placement patterns in October 2026 showed Splunk far ahead on both top-three and first-position rates.

Splunk holds a 14.8% rank-one rate while the next two brands by coverage hold 0.0% and 0.7%. Close coverage can still hide very different first-position strength.

Buyer-Intent Distribution

Questions This Section Answers

• What types of buyer intent does the current SIEM Software series measure?
• Which buyer-intent classes have no qualified observations yet?

All qualified observations in both months fell into the Brand Recommendation class, representing discovery and consideration intent.

Pricing, value, and head-to-head comparison have no qualified observations in the current public series.

Historical Measurement Record

This is an evergreen benchmark URL. New measurements are added to the same report.

Evidence and Source Layer

Questions This Section Answers

• What does the benchmark track at the prompt-observation level?
• Can source presence prove that a citation caused a recommendation in this benchmark?

The benchmark is built from prompt-level observations that retain the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and where exposed, citations or attributable evidence sources. Source presence is evidence about the information environment. It is not automatically proof that the source caused the recommendation.

Scope Boundaries

This public benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, social mention volume, or private and sponsored channels. A metric movement alone does not establish causality.

Top 10 Cited Domains

Questions This Section Answers

• Which domains are most frequently cited across AI platform responses for SIEM Software?
• How concentrated are citations, and which tracked brands own domains appear in the top 10?
• What kinds of sources do AI recommendations in this category draw on?

Across all AI platform responses in October 2026, the benchmark observed 5,770 citations across 1,860 unique domains.

Citations are broadly distributed rather than concentrated, with the top 10 domains accounting for roughly 25% of all citations. Two tracked brands' own domains appear in the top 10: splunk.com at rank 2 and exabeam.com at rank 8. The source mix spans video, community, encyclopedia, search, analyst, and vendor sites, indicating no single dominant source category.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index tracks how AI and search surfaces present and recommend brands within a vertical, using standardized prompt collections across canonical AI surfaces each month. All metrics follow documented definitions and research standards.

• AI Visibility Market Discovery Methodology
• AI Visibility Market Discovery Metric Definitions
• AI Visibility Market Discovery Research Standards
• Modeled AI Visibility Authority Value

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