How AI Search Is Recommending Managed Detection and Response: Monthly Trends

Executive Summary

CrowdStrike Falcon extended its lead in October 2026, and by a margin the benchmark flags as significant. The brand holds 72.7% valid recommendation coverage, up 16.4 points from 56.3% in September and up 6.7 points against the July 2026 baseline of 66.0%. SentinelOne remains second at 62.4%, a gap of 10.3 points to the leader, after rising 13.6 points month over month. The two brands still sit well clear of the rest of the field, but the distance between them widened again in October.

CrowdStrike Falcon is also the largest riser of the month, with its top-three rate up 10.1 points to 67.8% and its rank-one rate up 11.7 points to 50.3% against the July baseline. Secureworks Taegis posted the second significant rise, up 3.4 points from 1.5% in July to 4.9% in October, with raw mention presence up 4.0 points to 6.5%. No tracked brand recorded a significant decline this month. Sophos Intercept X posted the sharpest drop against the baseline, down 5.4 points from 44.8% to 39.4%, but that move sits inside normal month-to-month variation and reversed direction from September, up 5.6 points month over month.

The category therefore turned upward in October after a September pullback that had compressed the leaders. Seven of ten tracked brands are classified as stable against the July baseline, two are significant risers, and one, Red Canary, is a watch item on three consecutive monthly declines that remain within normal variation cumulatively. Across the full July-to-October series, CrowdStrike Falcon and SentinelOne each lost ground in September and recovered it in October, while Arctic Wolf, Sophos Intercept X, and Red Canary remain below their July coverage levels.

Each monthly run begins with 800 prompt-surface observations across the benchmark's defined AI/search surface universe. In July 2026, those 800 observations covered 620 unique questions; all 800 mentioned a tracked brand or competitor; 366 were relevant and 434 irrelevant; and the public metrics used the 326 qualified observations that survived both qualification stages. In October 2026, the same 800 observations covered 615 unique questions; all 800 mentioned a tracked brand or competitor; 551 were relevant and 249 irrelevant; and the public metrics used the 447 qualified observations that survived both qualification stages. August 2026 produced 447 qualified observations and September 2026 produced 467.

Key Findings

AI Response Inconsistency Alerts

Questions This Section Answers

• What factual inconsistency did AI platforms produce about CrowdStrike Falcon?
• Which sources did ChatGPT and Google AI Mode rely on to answer the same EDR pricing question?

The benchmark detected one critical or high-severity factual inconsistency this month, spanning two AI platforms: ChatGPT and Google AI Mode.

CrowdStrike Falcon

A high-confidence pricing inconsistency appeared when the two platforms answered the same question. Asked "How much does EDR software cost?", Google AI Mode stated roughly $99.99 per device per year for the Pro package, while ChatGPT stated that CrowdStrike Falcon Go costs about $60 per endpoint per year. Both answers describe annual per-endpoint pricing for the same vendor, and the two figures cannot both be correct for the cited packages.

Each platform grounded its answer in different sources. Google AI Mode cited a managed EDR pricing page, an MDR versus EDR pricing comparison, and SentinelOne's endpoint security education page. ChatGPT cited an endpoint detection and response guide, a buyer's guide covering EDR and XDR, and a best-EDR roundup. A pricing page covering the vendor's tiers was flagged as supporting both sides, quoting Falcon Go at $59.99 per device annually and Falcon Pro at $99 per device annually. A procurement guide citing analyst pricing benchmarks was flagged against the ChatGPT figure, noting an expectation of $50 to $60 per endpoint annually for combined endpoint protection and EDR from top-tier vendors.

The practical consequence is that a buyer asking a straightforward cost question receives two different annual per-endpoint figures depending on which surface they use, with the discrepancy traceable to which pricing source each system treated as authoritative.

Benchmark Context

Questions This Section Answers

• How did the qualified observation count change between July and October 2026?
• What does the qualified benchmark set represent, and why does it differ from the raw collection universe?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The qualified observation count rose from 326 in July to a series high of 467 in September, then settled at 447 in October, matching August. Relevance filtering captured a steadily larger share of the raw collection across the period, which means the October percentages are calculated on a broader qualified base than July's.

Benchmark-Level Metrics

Recommendation-shaped answer share climbed from 31.3% in July to 48.8% in October, its highest reading of the series after dipping to 25.9% in September. Valid recommendation shortlist share followed the same path, from 64.7% in July to 75.4% in October, also a series high after a September reading of 58.0%. The September pullback in both metrics reversed decisively in October, meaning AI systems shaped a larger share of answers as direct recommendations in the current month than at any point since the series began.

AI Recommendation Trend

Questions This Section Answers

• Which brands gained or lost valid recommendation coverage between July and October 2026?
• How much did CrowdStrike Falcon and SentinelOne recover after their September pullback?

Valid Recommendation Coverage by Brand

CrowdStrike Falcon and SentinelOne both recovered the share they lost in September and now sit above their July baselines, while the mid-field and lower tier remained broadly stable. CrowdStrike Falcon exceeded normal month-to-month variation against the July baseline, and Secureworks Taegis did the same on its smaller base; every other brand's baseline-to-current change sits inside normal variation for its own series, meaning the category-level result came from the combination of those two moves plus the offsetting gains and losses across the mid-field.

What Changed This Month

Questions This Section Answers

• What drove CrowdStrike Falcon's largest single-month coverage gain, and did its placement move too?
• Why is SentinelOne's recovery described as a placement story rather than a rank-one story?
• What does it mean that Sophos Intercept X coverage held steady while presence, top-three, and rank-one rates all declined?

CrowdStrike Falcon

CrowdStrike Falcon rose 16.4 points from 56.3% in September to 72.7% in October, the largest single-month move of its series, and now sits 6.7 points above its July 2026 baseline of 66.0%, a significant gain. The month-over-month move is significant as well. The brand's series across July, August, September, and October reads 66.0%, 71.4%, 56.3%, and 72.7%.

Placement moved harder than coverage. The top-three rate rose 10.1 points from 57.7% in July to 67.8% in October, and the rank-one rate rose 11.7 points from 38.6% to 50.3%. Raw mention presence eased 1.8 points from 96.9% to 95.1%, which is not a significant change. In absolute terms, the brand was recommended in 325 of 447 qualified observations in October.

The distinction to notice is that presence and recommendation moved in opposite directions, and recommendation won. CrowdStrike Falcon was mentioned slightly less often than in July but was recommended, placed in the top three, and placed first substantially more often. The recovery also runs against the category's own answer-shaping trend, which shifted toward recommendation-shaped responses overall.

Highest-priority diagnostic: which surfaces and prompt categories drove the October placement gains, and which evidence sources those answers cited.

SentinelOne

SentinelOne rose 13.6 points from 48.8% in September to 62.4% in October, a significant month-over-month gain, and now sits 6.3 points above its July baseline of 56.1%, a move that remains inside normal variation for its series. Its series reads 56.1%, 60.9%, 48.8%, and 62.4%.

The top-three rate rose 9.0 points from 40.2% in July to 49.2% in October. The rank-one rate eased 0.3 points from 2.8% to 2.5%, which is not a meaningful change. Raw mention presence held nearly flat, up 0.9 points to 84.3%. In absolute terms, the brand was recommended in 279 of 447 qualified observations in October.

The distinction to notice is that SentinelOne's recovery is a placement story, not a rank-one story. The brand re-entered top-three slots at a significantly higher rate while its share of first-place outcomes stayed at the low single digits, the widest divergence between the two leaders in the category. With 279 valid recommendations in the month, this is a large-count brand and its movement should be read as a broad pattern rather than a small-sample effect.

Highest-priority diagnostic: which prompts place SentinelOne in the top three without placing it first, and which brand takes the first slot in those answers.

Secureworks Taegis

Secureworks Taegis rose 3.4 points from 1.5% in July to 4.9% in October, a significant gain against the baseline, driven by a 3.2 point month-over-month rise from 1.7% in September. Raw mention presence rose 4.0 points from 2.5% to 6.5%.

Placement followed presence. The top-three rate rose 1.2 points from 0.6% to 1.8%, and the rank-one rate rose 0.4 points from 0.3% to 0.7%, neither significant on its own. In absolute terms, the brand moved from 5 valid recommendations in July to 22 in October.

The distinction to notice is that this is a small-count brand whose movement carries weight precisely because the base is small. A rise from 5 to 22 valid recommendations across 326 and 447 qualified observations respectively is a real change in how often the brand is surfaced, but single recommendations drive large percentage swings at this scale.

Highest-priority diagnostic: which prompts and surfaces newly include Secureworks Taegis, and whether those answers place it inside a shortlist.

Sophos Intercept X

Sophos Intercept X declined 5.4 points from 44.8% in July to 39.4% in October, a move inside normal variation, after rising 5.6 points month over month from 33.8% in September. Its series reads 44.8%, 40.7%, 33.8%, and 39.4%, so the October reading partially recovers the September decline without returning to the July level.

The underlying signals are more negative than the coverage line suggests. Raw mention presence fell 12.4 points from 59.8% in July to 47.4% in October, and the top-three rate fell 8.2 points from 26.1% to 17.9%. The rank-one rate fell 4.2 points from 5.8% to 1.6%. In absolute terms, the brand was recommended in 176 of 447 qualified observations in October.

The distinction to notice is that Sophos Intercept X's coverage appears broadly stable while every supporting signal declined. The brand was recommended about as often as in July but was mentioned far less, placed in fewer top-three slots, and won far fewer first-place outcomes. Coverage holding steady against a shrinking presence base is a different position from coverage holding steady against a growing one.

Highest-priority diagnostic: whether the presence decline reflects a shift in the qualified prompt set or a genuine reduction in how often AI systems surface Sophos Intercept X.

Red Canary

Red Canary declined 3.3 points from 9.8% in July to 6.5% in October, a change inside normal variation, and 0.1 points month over month from 6.6% in September. Its series reads 9.8%, 8.1%, 6.6%, and 6.5%. The benchmark classifies the brand as a watch item on three consecutive monthly declines that remain within normal variation cumulatively.

Raw mention presence fell 2.8 points from 12.9% to 10.1%, and the top-three rate fell 0.8 points from 2.1% to 1.3%, neither significant. The rank-one rate eased 0.1 points from 0.3% to 0.2%. In absolute terms, the brand was recommended in 29 of 447 qualified observations in October, down from 32 in July.

The distinction to notice is the streak itself rather than any single month. Three consecutive declines that each sit inside normal variation have accumulated into a 3.3 point baseline gap. The brand's net sentiment score also eased, from 0.9 in July to 0.7 in October.

Highest-priority diagnostic: whether the three-month decline reflects a consistent prompt or surface pattern, and which brands now occupy the answers Red Canary has left.

Buyer-Intent Interpretation

Questions This Section Answers

• Which buyer-intent clusters did the October qualified observations actually capture?
• Why can't the public benchmark yet answer how brands fare on pricing or head-to-head comparisons?

All qualified observations fell into the Brand Recommendation cluster in every month of the July-to-October series. The October qualified set did include 5 pricing analysis responses and 17 comparison analysis responses by response type, yet the buyer-intent classification placed all observations in the brand recommendation cluster.

That means the public benchmark shows where brands are recommended and in what order, and cannot yet answer how any brand fares on price or in a direct head-to-head comparison. The pricing inconsistency reported above illustrates the gap from the other direction: AI systems are answering cost questions, and the benchmark's public metrics do not currently measure which brand those answers favor.

Brand Opportunity Summary

Questions This Section Answers

• Which brands are classified as significant risers or watch items this month?
• What diagnostic question does the benchmark flag for each tracked brand?

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations covering the query, surface, recommendation outcome, rank, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the LLM Authority Index AI Visibility Market Discovery research program.

• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Qualified observation counts moved from 326 in July to 447 in October, with a series high of 467 in September. Percentages are calculated within the qualified set, so month-over-month comparison reflects both brand movement and composition changes in the underlying prompts.
• The qualified denominator differs from the raw collection universe; relevance filtering accounts for the gap, and the share of the raw collection surviving that filter grew across the series.
• Small-count brands including Deepwatch, Secureworks Taegis, and eSentire should be interpreted with care given the low number of valid recommendations behind their percentages. Secureworks Taegis moved from 5 valid recommendations in July to 22 in October, a real change at a scale where individual observations carry visible weight.
• Month-over-month movement identifies changes worth investigating. It does not by itself establish the cause of those changes, and the benchmark does not attribute movement to any party's activity.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate coverage percentages sit the questions that matter for competitive position: which high-intent prompts a brand wins, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each option, and which external sources shape those answers. October's results show CrowdStrike Falcon and SentinelOne recovering their September losses and Secureworks Taegis posting a significant rise on a smaller base, but the benchmark does not explain what produced those shifts.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. It answers the "so what" behind each movement and identifies the specific levers that can influence AI recommendation outcomes.

Request an AI visibility audit