| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| SIEM platforms | Security operations | 38% | 1 of 61 | 13% | 62 | endorsed leader | ▼−2Since September 2026: 41% → 39%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| AI SOC agents | Security operations | 4% | 5 of 104 | 0% | 7 | under 10 labels · led by Dropzone AI at 24% | |
| AI security platforms | Security operations | 2% | 15 of 233 | 17% | 6 | under 10 labels · led by SentinelOne Singularity at 11% | |
| SOAR platforms | Security operations | 2% | 7 of 67 | 8% | 39 | accepted challenger | =heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Log management | IT operations and endpoint | 0% | 53 of 73 | 40% | 5 | under 10 labels · led by Datadog Log Management at 28% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Extended detection and response | Security operations | 0% | 25 of 58 | 25% | 4 | under 10 labels · led by Microsoft Defender XDR at 40% | newNew since September 2026: not ranked then, 0% now. |
| Threat intelligence platforms | Security operations | 0% | 46 of 119 | 0% | 2 | under 10 labels · led by MISP at 12% | newNew since September 2026: not ranked then, 0% now. |
| Data security posture management | Security operations | 0% | 96 of 105 | 100% | 1 | under 10 labels · led by Microsoft Purview at 18% | |
| Identity threat detection and response | Identity and access | 0% | 85 of 105 | 0% | 1 | under 10 labels · led by Huntress Managed ITDR at 51% | |
| Network detection and response | Security operations | 0% | 75 of 102 | 0% | 1 | under 10 labels · led by ManageEngine NetFlow Analyzer at 21% |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| AI SOC agents | 0% | 4% | newNew since September 2026: not ranked then, 4% now. | New this edition | Rank 5 of 104, unchanged |
| SIEM | 41% | 39% | ▼−2Since September 2026: 41% → 39%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 1 of 61, unchanged |
| AI security | 0% | 2% | newNew since September 2026: not ranked then, 2% now. | New this edition | Rank 15 of 233, unchanged |
| SOAR | 2% | 2% | =heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 6 → 7 of 67 |
| Logs | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 36 → 53 of 73 |
| XDR | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 25 of 58, unchanged |
| Threat intel | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 46 of 119, unchanged |
| DSPM | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 96 of 105, unchanged |
| ITDR | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 85 of 105, unchanged |
| NDR | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 75 of 102, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 2 | 3 | 0 | 0 | 5 |
| GPT-5.4 mini | 4 | 5 | 0 | 3 | 12 |
| Gemini 3.5 Flash | 1 | 7 | 0 | 2 | 10 |
| Perplexity Sonar | 1 | 4 | 1 | 1 | 7 |
| Grok 4.20 | 4 | 6 | 2 | 0 | 12 |
| Mistral Small | 3 | 2 | 3 | 0 | 8 |
| DeepSeek V4 Flash | 2 | 5 | 1 | 3 | 11 |
| Llama 4 Maverick | 0 | 1 | 2 | 0 | 3 |
| Qwen 3.7 Flash | 2 | 9 | 1 | 3 | 15 |
| Kimi K2 | 2 | 9 | 1 | 0 | 12 |
| GLM 4.7 FlashX | 2 | 5 | 1 | 1 | 9 |
| MiniMax M2.5 | 4 | 2 | 2 | 0 | 8 |
| GPT-6 Luna | 2 | 3 | 1 | 2 | 8 |
| Muse Glimmer 30B | 1 | 5 | 1 | 1 | 8 |
Verbatim evidence the judge attached to positive labels.
“I'd suggest starting with either Microsoft Sentinel (if already in the Microsoft ecosystem) or Sumo Logic (if you want vendor neutrality)” Claude Haiku 4.5 · SIEM · paraphrase prompt · first choice
“Is primarily a Microsoft/Azure shop | Microsoft Sentinel | Seamless out-of-the-box ingestion, KQL query language, and fast deployment.” Gemini 3.5 Flash · SIEM · comparative prompt · first choice
“Microsoft Sentinel — unbeatable value if you're a Microsoft shop; pay-as-you-go pricing; strong detection content; built-in UEBA and SOAR.” DeepSeek V4 Flash · SIEM · scale prompt · first choice
“If you're in Microsoft world (common for B2B): Go with Sentinel—cost-effective, quick value, scales well without huge teams.” Grok 4.20 · SIEM · direct prompt · first choice
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Why to be cautious: Vendor Ecosystem Dependence... difficult, confusing, and often suboptimal if your infrastructure is primarily AWS, Google Cloud, or Linux-based” Qwen 3.7 Flash · SIEM · negative prompt · soft negative
“Sentinel is incredibly powerful. However, do not buy it to manage alone unless you have dedicated KQL (Kusto Query Language) expertise.” Gemini 3.5 Flash · SIEM · scale prompt · soft negative
“Microsoft Sentinel's billing, for example, depends on ingestion tiers, retention, and separate infrastructure charges in some cases” GPT-5.4 mini · SIEM · negative prompt · soft negative
“Microsoft Sentinel, if you're not invested in Azure or haven't modeled the full bill: Pricing depends on data volume and tier” GPT-6 Luna · SIEM · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 359 of the 389 answers that named Microsoft Sentinel and are not a share of its labels.
835 of the 835 domain citations in answers naming Microsoft Sentinel came from somebody else's page.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
What Microsoft Sentinel's own pages state, read October 5, 2026: microsoft.com/en-us/security/business/siem-and-xdr/microsoft-sentinel-siem. A claimed page can correct any of them.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Microsoft Sentinel's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Microsoft Sentinel, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for Microsoft Sentinel by email, built from the raw record of the edition. It shows:
AI models recommend Microsoft Sentinel first in SIEM platforms this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.