| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Data loss prevention | Security operations | 7% | 3 of 89 | 26% | 43 | criticized challenger | ▲+5Since September 2026: 4% → 9%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Insider risk management | Security operations | 0% | 31 of 84 | 17% | 6 | under 10 labels · led by Microsoft Purview Insider Risk Management at 22% | |
| Data security posture management | Security operations | 0% | 67 of 105 | 0% | 1 | under 10 labels · led by Microsoft Purview at 18% |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| DLP | 4% | 9% | ▲+5Since September 2026: 4% → 9%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 6 → 3 of 89 |
| Insider risk | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 31 of 84, unchanged |
| DSPM | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 67 of 105, 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 | 0 | 0 | 2 | 0 | 2 |
| GPT-5.4 mini | 0 | 2 | 0 | 1 | 3 |
| Gemini 3.5 Flash | 0 | 1 | 0 | 3 | 4 |
| Perplexity Sonar | 2 | 1 | 0 | 0 | 3 |
| Grok 4.20 | 0 | 3 | 1 | 2 | 6 |
| Mistral Small | 2 | 0 | 2 | 0 | 4 |
| DeepSeek V4 Flash | 0 | 2 | 1 | 2 | 5 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 3 | 0 | 2 | 5 |
| Kimi K2 | 0 | 3 | 3 | 0 | 6 |
| GLM 4.7 FlashX | 1 | 3 | 0 | 1 | 5 |
| MiniMax M2.5 | 1 | 1 | 0 | 0 | 2 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 1 | 2 | 1 | 4 |
Verbatim evidence the judge attached to positive labels.
“or Forcepoint DLP if you need a unified policy engine that works across cloud, email, endpoints and on-premises systems” GLM 4.7 FlashX · DLP · paraphrase prompt · first choice
“Proofpoint and Forcepoint are top-tier choices due to their comprehensive features and scalability” Mistral Small · DLP · paraphrase prompt · first choice
“Forcepoint if you need stronger classic DLP controls across endpoints and web traffic” Perplexity Sonar · DLP · paraphrase prompt · first choice
“Best for broad traditional enterprise DLP: Forcepoint or Symantec/Broadcom” Perplexity Sonar · DLP · comparative 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.
“rarely the heavy, legacy enterprise suite (like Broadcom/Symantec or traditional Forcepoint). Those legacy tools are notorious for requiring multi-month deployments” Gemini 3.5 Flash · DLP · direct prompt · hard negative
“When to avoid: Large-scale hybrid environments or if you need low-maintenance AI-driven detection.” Grok 4.20 · DLP · negative prompt · hard negative
“What to Avoid for Tight Budgets - Enterprise suites (Forcepoint, Symantec, Proofpoint)” DeepSeek V4 Flash · DLP · budget prompt · hard negative
“Recent peer feedback includes issues with agent stability and application control, especially with default Microsoft apps.” GPT-5.4 mini · DLP · 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 160 of the 170 answers that named Forcepoint DLP and are not a share of its labels.
510 of the 615 domain citations in answers naming Forcepoint DLP 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 Forcepoint DLP's own pages state, read September 28, 2026: forcepoint.com/forcepoint-dlp-pricing, forcepoint.com/data-loss-prevention-lp, forcepoint.com/forcepoint-dlp-vs-symantec-dlp, forcepoint.com/forcepoint-dspm-and-cloud-dlp-rtg, forcepoint.com/cyber-edu/network-dlp. 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 Forcepoint DLP'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 Forcepoint DLP, 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 Forcepoint DLP by email, built from the raw record of the edition. It shows:
Forcepoint DLP is among the products AI models recommend first in Data loss prevention 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.