Cyber risk quantification · October 2026 Edition
SAFE Security vs RiskLens
Two of fourteen models named SAFE Security first on the direct prompt; zero named RiskLens. SAFE Security was named by twelve of the fourteen models and RiskLens by thirteen and SAFE Security carries 40 labels and RiskLens 27, so the shares are not directly comparable.
Named in one category this edition.
RiskLens
criticized challengerNamed in one category this edition.
First-choice share8%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate32%37%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#5A position in a field of 6; printed, not drawn.
Labels4027A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, SAFE Security reading right to left. Rank and label count are printed, not drawn.CyberSaint CyberStrong was named alongside these two in twelve of the fourteen direct answers. CyberSaint CyberStrong vs SAFE Security · CyberSaint CyberStrong vs RiskLens · Kovrr vs SAFE Security
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 cyber risk quantification page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
SAFE SecurityFirst choices, of fourteen modelsRiskLensDirect203 against SAFE Security · 1 against RiskLens
Paraphrase202 against SAFE Security · 1 against RiskLens
Comparative91
Budget-constrained014 against SAFE Security · 5 against RiskLens
Scale-constrained00
Negative104 against SAFE Security · 3 against RiskLens
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Across every category in the October 2026 Edition, SAFE Security and RiskLens were named in the same answer fifty-seven times, of the 116 answers naming SAFE Security and the 91 naming RiskLens. In those answers RiskLens took the first choice one time and SAFE Security nineteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where SAFE Security and RiskLens stood in it.
| Model | DirectRI | ParaphraseRI | ComparativeRI | Budget-constrainedRI | Scale-constrainedRI | NegativeRI |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | RI | | ||||
| GPT-5.4 mini | | RI | RI | |||
| Gemini 3.5 Flash | | | | | RI | |
| Perplexity Sonar | RI | RI | | |||
| Grok 4.1 Fast | | | RI | RI | ||
| Mistral Small | RI | | | |||
| DeepSeek V4 Flash | | | | RI | | |
| Llama 4 Maverick | RI | RI | ||||
| Qwen 3.7 Flash | RI | RI | ||||
| Kimi K2 | RI | | | | RI | |
| GLM 4.7 FlashX | RI | |||||
| MiniMax M2.5 | | RI | ||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | RI | RI | RI | RI | |
SAFE SecurityRI RiskLens first choice named as an alternative argued againstblank: not namedEach cell is one answer, SAFE Security on the left and RiskLens on the right.The direct prompt
The plain question, one answer per model, grouped by where SAFE Security and RiskLens stood in it.
SAFE Security first, RiskLens an alternative
2 of 14 modelsRiskLens was named in the answer but not as the choice, or not at all.GPT-5.4 miniSAFE Security alternatives: Kovrr, ThreatConnect Risk Quantifier
GLM 4.7 FlashXCyberSaint CyberStrong, SAFE Security alternatives: Axio, Balbix, LogicGate Risk Cloud, RiskLens, UpGuard
Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Perplexity SonarCyberSaint CyberStrong alternatives: RiskLens, SAFE One
Mistral SmallCyberSaint CyberStrong, Maxxsure alternatives: RiskLens
DeepSeek V4 FlashCyberSaint CyberStrong alternatives: Axio, SAFE Security, vCISO Lite
Llama 4 MaverickCyberSaint CyberStrong alternatives: Axio, Maxxsure, RiskLens
Qwen 3.7 FlashCyberSaint CyberStrong, Maxxsure alternatives: Black Kite, RiskLens
GPT-6 LunaAxio360 alternatives: CyberSaint CyberStrong, SAFE Security
Muse Glimmer 30BCyberSaint CyberStrong alternatives: Maxxsure, RiskLens, SAFE Security
Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Cyrisma alternatives: Black Kite, Maxxsure, SAI360
Gemini 3.5 FlashKovrr alternatives: Axio, CyberSaint CyberStrong
Grok 4.1 FastCyberSaint CyberStrong alternatives: Cyber Quant, Maxxsure, vCISO Lite
Kimi K2CyberSaint CyberStrong alternatives: Axio, Maxxsure
MiniMax M2.5CyberSaint CyberStrong alternatives: Bitsight, Black Kite, Kovrr, LogicGate Risk Cloud, UpGuard
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
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Level: the same share of first choices.
SAFE Security4%#6 of 7RiskLens4%#5 of 7
The full small business standing →Mid-marketThe figures above
SAFE Security leads by six points.
SAFE Security8%#3 of 6RiskLens2%#5 of 6
The full mid-market standing →Enterprise
SAFE Security leads by forty-seven points.
SAFE Security56%#1 of 7RiskLens9%#2 of 7
The full enterprise standing →What the models said about SAFE Security
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“such as Kovrr, Safe Security, and Axio) are generally out of reach, as their annual licensing fees frequently scale into the tens or hundreds of thousands of dollars” Gemini 3.5 Flash · budget prompt · soft negative
“Safe Security (post-RiskLens acquisition) | ⚠️ Moderate | Partially black-box methodology; auditability of underlying assumptions varies” DeepSeek V4 Flash · negative prompt · soft negative
“Caveat: It can be expensive and may require more administrative overhead to configure properly compared to Kovrr or Axio.” Gemini 3.5 Flash · direct prompt · soft negative
“The only enterprise platform purpose-built on the FAIR (Factor Analysis of Information Risk) framework... Ranked leader in the 2025 Forrester CRQ Wave” Kimi K2 · comparative prompt · first choice
“Safe Security leads the 2025 Forrester CRQ Wave after acquiring RiskLens, consolidating FAIR-native quantification with real-world data integration.” Mistral Small · comparative prompt · first choice
“Cloud-first enterprises that want CRQ wired into daily security operations lean Safe Security; its integration depth is the widest in the category.” Claude Haiku 4.5 · comparative prompt · first choice
What the models said about RiskLens
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“transparent pricing compared to most enterprise CRQ tools (like RiskLens or SAFE Security) that typically start at $25K–$200K+ annually” DeepSeek V4 Flash · budget prompt · soft negative
“RiskLens (traditional FAIR implementation), manual FAIR assessments ... Heavy reliance on manual FAIR; resource-intensive” Kimi K2 · negative prompt · soft negative
“Enterprise tools like RiskLens, LogicGate, or Balbix are powerful but start at $10K+/year, so skip unless scaling up.” Grok 4.1 Fast · budget prompt · soft negative
“One of the most established FAIR-native CRQ platforms... references enterprise-grade RiskLens as a benchmark tool” GPT-5.4 mini · comparative prompt · first choice
“RiskLens - a pioneer in FAIR methodology, best for FAIR-based quantification.” Llama 4 Maverick · budget prompt · first choice
“RiskLens is the gold standard for those who want rigorous, scientifically backed quantification... Caveat: Ensure you get the right pricing tier” Qwen 3.7 Flash · direct prompt · alternative
Also compared
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.