Cyberhaven vs Proofpoint Enterprise DLP
Zero of fourteen models named Cyberhaven first on the direct prompt; zero named Proofpoint Enterprise DLP. Cyberhaven was named by nine of the fourteen models and Proofpoint Enterprise DLP by ten and Cyberhaven carries 16 labels and Proofpoint Enterprise DLP 17, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the data loss prevention page.
By framing
CyberhavenFirst choices, of fourteen modelsProofpoint Enterprise DLP
Across every category in the November 2026 Edition, Cyberhaven and Proofpoint Enterprise DLP were named in the same answer twenty-eight times, of the 92 answers naming Cyberhaven and the 65 naming Proofpoint Enterprise DLP. In those answers Proofpoint Enterprise DLP took the first choice zero times and Cyberhaven zero.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | |||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | |||
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | |||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | ||||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | |||||
| Kimi K2 | | | ||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | | |||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | |
Cyberhaven Proofpoint Enterprise DLP first choice named as an alternative argued againstblank: not namedEach cell is one answer, Cyberhaven on the left and Proofpoint Enterprise DLP on the right.The direct prompt
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Cyberhaven0%#9 of 14Proofpoint Enterprise DLP0%#12 of 14Cyberhaven2%#6 of 15Proofpoint Enterprise DLP2%#8 of 15Cyberhaven0%#9 of 13Proofpoint Enterprise DLP0%#8 of 13What the models said about Cyberhaven
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Look at modern vendors built for mid-market and cloud-first environments (e.g., *Cyberhaven, Nightfall AI, Next DLP, or Code42/Incydr*).” Gemini 3.5 Flash · scale prompt · first choice
“If you need more advanced features or have significant SaaS usage, Cyberhaven or Nightfall would be stronger choices.” MiniMax M2.5 · direct prompt · alternative
What the models said about Proofpoint Enterprise DLP
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid treating it as a universal DLP solution if your principal risks involve USB, engineering workstations, network shares, or non-email channels” Perplexity Sonar · negative prompt · soft negative
“Multiple administration consoles, limited predefined templates, false positive management burden” Kimi K2 · negative prompt · soft negative
“can afford higher costs/setup ... can require more expertise.” Grok 4.20 · paraphrase prompt · soft negative
“Mid-market DLP solutions from Microsoft Purview, Proofpoint, Netskope, or Forcepoint often hit the sweet spot” Kimi K2 · scale prompt · first choice
“Consider Proofpoint Enterprise DLP, which is particularly strong for email and collaboration protection.” Perplexity Sonar · paraphrase prompt · alternative
“Other alternatives include Safetica, Forcepoint DLP, and Proofpoint Enterprise DLP.” Llama 4 Maverick · paraphrase prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.