OutThink vs CybSafe: how to tell two similar platforms apart
If you have sat through both demos, you already know the problem with this comparison. Both platforms are behavioral-science-led. Both talk about human risk rather than awareness. Both have a risk score, an explainability story and a research heritage. The decks rhyme.
That is not marketing convergence. CybSafe has a genuine research foundation – a real behavior database, academic collaboration, and a longer history of doing this properly than almost anyone in the category. We are not going to tell you they are a legacy vendor with better slides, because that would be false and you would know it.
So this page does something different from our other comparisons. It skips the category argument entirely and goes to the two things that actually separate two platforms making the same claim: what the mechanism is, and what evidence exists for it.
CybSafe
What the platform is built around
- Behavioral science model. SebDB, a published taxonomy of security behaviors, is the organizing spine.
- Measurement first. Behaviors are catalogued, scored and tracked over time against that taxonomy.
- Content and nudges are mapped to the behaviors the model names.
- Inspectable by design. The taxonomy is public, so you can audit what is being measured and why.
OutThink
What the platform is built around
- Per-person generation. Training and interventions are generated for the individual from role, behavior and motivational driver.
- Stack-fed triggers. Interventions fire from live signal in EDR, DLP, web and IAM rather than from platform events.
- Risk model fed from outside. Quantification ingests behavior from your security systems, plus attitudes, access and targeting.
- Intelligence leaves the platform. Human risk context flows into SOC, IAM and GRC decisions.
Both platforms are real, and both are more than a phishing simulator. They are built around different mechanisms, which is what actually decides which one fits your program. The differences below are mechanism differences, not scores.
Before you build the comparison grid
- Both platforms detect risky behavior and intervene. The mechanism differs. Theirs is oriented around nudges and guidance triggered by signal; ours also changes the training content itself per person, and the intervention can fire from your own security stack rather than from the platform’s own data.
- So ask three questions: what adapts, what can trigger an intervention, and what the risk model is built from and whether it is inspectable.
- Then ask for evidence with a method attached. Both of us publish outcome claims. Ask for cohort, timeframe and baseline definition on any headline number, ours included.
- What changing looks like: RelateCare’s click rate fell from 8% to 1%, reporting doubled from 17% to 34%, and credential submission halved.1
- What CybSafe does better: a genuine, published research foundation and a longer academic track record than ours, plus deep UK financial-services references.
- Who we are: founded in 2019 by CISOs who lived this problem, seven years of purpose-built HRM engineering, 100+ enterprise deployments.
First, what CybSafe does well
This is the heaviest concession section on any of our comparison pages, because the honest assessment requires it.
- The research foundation is real, not positioning. A behavior database built and maintained over years, academic collaboration, and published science behind the product. Plenty of vendors claim behavioral science. Theirs is documented and it predates the category’s fashion for it.
- They have been doing this longer than most. Founded 2015, London, with a heritage in UK financial services and regulatory work. In a category where “human risk management” is a 2023 rebrand for a lot of vendors, that matters.
- Named enterprise references in UK financial services, which is one of the most demanding buying environments there is.
- Their explainability narrative is well timed and legitimate. A risk indicator scored per user with published weighting logic, and a natural-language intelligence layer that answers questions with explainable answers. As AI procurement scrutiny increases, “we can show you why the model said that” is the right thing to be building.
- Their 2026 release closed most of the vocabulary gap between us. If the two products sound alike now, it is partly because they have built toward the same conclusions we did.
If your evaluation weights published research heritage and UK financial-services reference depth above everything else, they are a strong answer and we would rather say so.
The question this page exists to answer is what differs underneath the shared vocabulary.
What actually differs
Three differences, in the order they matter.
Difference 1What adapts
This is the sharpest distinction and it is easy to test in a demo.
Their model, as published, detects risky behavior and sentiment signals and then intervenes with nudges, guidance and automation. That is a real capability and it works.
Ours does that and changes the training itself. Content is generated per person from role, behavior, motivational driver and the choices they make as they go, with roles pre-mapped from your directory and validated by each user, and an allocation engine that sends fewer modules rather than more. The nudge and the curriculum are the same system.
Ask both vendors to show you the same module rendered for two different people, live, side by side. Not the campaign builder. The output. That request separates “we target training” from “we generate training” in about ninety seconds.
Difference 2What can trigger an intervention
Ask which of your own security systems can fire a correction, and ask to see it live rather than on a roadmap.
Ours fire from live signal in your EDR, DLP, web filter and IAM systems and land in Teams or Slack while the person is still active. Named integrations include Entra ID, Okta, Microsoft Purview, Microsoft Defender, Microsoft Graph, Jamf, Zscaler and ServiceNow, with threat enrichment via VirusTotal, IBM X-Force, CriminalIP and Spamhaus.
The question is not whether a platform has integrations. It is whether a real alert from your stack, about a named person, can trigger an intervention aimed at that person, today. Ask for the demo, not the connector list.
Difference 3What the risk model is built from, and whether you can inspect it
Both platforms publish a per-user risk score weighted by role and access. Both talk about explainability. So go one level down.
Ask what proportion of the score comes from signals originating outside the vendor’s own product. Ask whether the model specification and its weightings are published, or only described. Ask whether there is any validation or back-testing evidence behind the outputs. Ask whether your IAM and GRC functions have said they would act on it.
Ask us the same questions. Ours ingests behavior from EDR, DLP, web, email and IAM, plus attitudes, level of access, how targeted a person is, device security and workplace factors such as email fatigue and collaboration networks. Where our model documentation is not public, we will say so rather than describe it as transparent.
And the thing neither of us should be allowed to skip
Both companies publish headline outcome numbers. Ours are on this page. For any figure either of us shows you, ask for the cohort, the timeframe and the baseline definition. A percentage improvement with no stated baseline is not evidence, it is a shape. That standard applies symmetrically and it is the fastest way to compress two convincing decks into something comparable.
Where the maturity model does and does not help
Short section, and it exists to be honest about the limits of our own framework.
The HRM Maturity Model is useful for separating platforms built for compliance from platforms built for behavior change. It does not separate these two platforms, because both credibly operate at Level 2 and both are building toward Level 3. Anyone who tells you otherwise is using the model as a marketing device.
Where it still earns its place in this evaluation is the sequencing argument. The four L2 jobs – Motivate, Educate, Activate, Correct – are a system rather than a menu, so it is worth checking each one separately on both platforms rather than accepting a platform-level claim. And the Level 3 question, what feeds the risk score, is the one that decides whether your IAM and GRC functions will ever act on the output.
There isn’t one. If the program is built around phishing simulations, generic training campaigns, newsletters, posters and Cybersecurity Awareness Month activities, it is L1.
Can the platform execute all four L2 critical jobs – Motivate, Educate, Activate and Correct – autonomously, across the entire organization, and across the full spectrum of security behaviors?
Is the risk quantification built on actual behavioral data from security systems, or on simulation results and training completion?
Does it surface actionable intelligence from user interactions, generate prioritized improvement actions across people, process and technology, and feed human risk intelligence into SOC, GRC, ticketing and identity systems?
Can the platform adjust access controls from human risk scores, integrating directly with identity providers? Can it enable user self-remediation that shifts responsibility from the SOC to the individual? Is it architected to extend behavioral governance to AI agents, not just human users?
OutThink and CybSafe, side by side
The CybSafe column contains only statements traceable to their own public material or published third-party sources. Where we cannot verify something, the cell says so and tells you to ask them.
| What to ask | OutThink | CybSafe (public information, August 2026) |
|---|---|---|
| Research foundation | Behavioral science research before a line of code, and a Chief Scientific Adviser whose work underpins the psychographic layer. | A documented behavior database maintained over years, academic collaboration and published science. Longer track record than ours. |
| UK financial-services references | Enterprise references across banking, construction, healthcare, industrials and non-profit. | in that specific market, with named UK financial-services accounts and a regulatory heritage. |
| Category vocabulary | Effectively parity, and worth saying plainly. After their 2026 release the two products describe themselves in very similar terms. | Parity. This is why the rest of the table is about mechanism rather than positioning. |
| What adapts | The training content itself, generated per person from role, behavior, motivational driver and in-flow choices, plus the nudges. One system. | Detection of risky behavior and sentiment, with intervention through nudges, guidance and automation. Ask to see the same module rendered for two different people, side by side, live. |
| What can trigger an intervention | Live signal from EDR, DLP, web filter and IAM, delivered in Teams or Slack while the person is active. Named integrations across identity, endpoint, data and service management. | Ask which of your own security systems can trigger an intervention, and ask to see it live rather than described. |
| What feeds the risk model | Behavior from EDR, DLP, web, email and IAM, plus attitudes, level of access, targeting, device security and workplace factors. | A per-user risk indicator weighted by role and access. Ask what proportion of the score originates outside their own product. Put the same question to us. |
| Model inspectability | Where our model documentation is not public we will say so rather than call it transparent. Ask us what an IAM or GRC reviewer can inspect. | Explainability is a stated strength. Ask whether the specification and weightings are published or described, and whether any validation evidence exists. Same question to us. |
| Outcome evidence standard | Named customer outcomes below, each with the organization identified and the movement stated. | They publish headline improvement figures. Ask for cohort, timeframe and baseline definition on any headline number – theirs and ours. |
| Practice beyond training | Cyber Ranges across deepfake CEO video, vishing, smishing, data handling, secure browsing, endpoint, social media and physical security. First eight deploying late 2026. Non-phishing behaviors covered in adaptive training and stack-triggered nudges today. | Ask what is rehearsed as practice rather than delivered as guidance, and what is shipping versus roadmap on both sides. |
| Motivation and the manager layer | CyberQ: a personal cyber-competence score people own and improve, a manager view for line managers, and an HR feed where governance allows. | Ask what an individual employee sees about their own progress, and what a line manager can see and do. |
| From risk view to action | Prioritized improvement actions across people, process and technology, some executed automatically, some for approval, some for your team. | Ask what the platform recommends you do next, across all three levers, as distinct from what it surfaces. |
| Regulated-sector depth | Regulatory framing and audit evidence across sector regimes, with named references in banking and healthcare. | Genuine regulatory heritage. Ask both of us for the sector regimes you actually report against. |
| Localization | 40+ languages across content, the full end-user experience, nudges, and Cyber Ranges from late 2026. | Ask about coverage of the end-user interface and notifications as well as content, on both sides. |
| Pricing shape | No public list price. Packaging mirrors the maturity model, so expansion follows your own journey. L1 includes compliance training, delivery evidence and phishing simulation. See Plans. Human Risk Intelligence, the Real-Time Threats Engine and CyberQ are available from 2,001 licensed users. | No public list price. Ask for it priced at your seat count with the modules you would actually run. |
| Implementation and support | Directory and SSO in week one, first campaign and baseline inside week four. Stack integrations that feed L3 are scoped separately. Named CSM and HRM program expertise. | Ask how long to first campaign, how long until stack signal is flowing, and who supports the program day to day. |
| Independent ratings | See OutThink on Gartner Peer Insights. | See CybSafe on Gartner Peer Insights and on G2. Both of us have smaller review corpora than the largest platforms in the category; read for specifics rather than scores. |
Sources for the CybSafe column. CybSafe product and platform pages, their 2026 release notes, their published research, and Gartner Peer Insights and G2 listings. All accessed August 2026.
Building a Culture of Cyber Resilience Across a Global Workforce
VINCI partnered with OutThink to move beyond tick-box compliance, deploying adaptive, role-based security awareness across 270,000 employees in 120 countries - reducing human risk at enterprise scale.
How to design an evaluation that separates them
Five steps. They work on us as well as on them, which is the point.
- Ask both vendors for the same artifact, not the same story. One module rendered for two different people, live. One real alert from your own stack triggering an intervention on a named person. One risk score explained down to its inputs. Artifacts separate platforms; narratives do not.
- Run the four L2 jobs as four separate conversations. Motivate, Educate, Activate, Correct. Platform-level claims blur; job-level claims do not, and a system missing one job behaves differently from one missing none.
- Apply one evidence standard to both. Cohort, timeframe, baseline definition, on every headline figure. Ask what happens to the number if the baseline is defined differently.
- Involve the people who would have to consume the score. If IAM and GRC will not act on a per-person risk number, its methodology is academic. Get them in the room for that part.
- Pilot the mechanism, not the content. Both platforms will look good in a content review. Pilot the thing that differs: does the training change per person, and can your own stack trigger a correction.
If you run that process and choose them, you will have chosen well and for reasons you can defend. We would rather lose that evaluation than win a vaguer one.
Frequently asked questions
Yes, and this is one of the few genuinely close comparisons in the category. Both platforms are built for behavior change rather than compliance records. The differences are mechanism and evidence rather than category.
It is a real strength and we are not going to argue it away. What it establishes is that their model is grounded. What it does not establish is what the software does with the result, which is why this page is about mechanism.
Ask both vendors to show the same training module rendered for two different people, live, and ask both to trigger an intervention from a real alert in your own security stack. Those two requests take ten minutes and they separate the products more cleanly than any deck.
There is no good reason to. Both platforms want the same behavioral signal, and splitting it degrades both. This is a single-vendor decision.
Possibly less than either of us implies, which is why the useful questions are what proportion of the score comes from outside the vendor’s own product, whether the model can be inspected, and whether your IAM and GRC teams would act on it. HRI ingests behavior from your EDR, DLP, web, email and IAM systems plus attitudes, access level, targeting, device security and workplace factors.
Pricing follows the maturity model, L1 Reactive through L4 Predictive, and depends on seat count, level and term. See Plans, or talk to us for a quote at your seat count.
Run the evaluation
You do not need to take a position on OutThink versus CybSafe from a web page. You need a method that separates two platforms making the same claim, and section above gives you one you can run without us.
Two convincing demos is not a stalemate. It means the demo is the wrong instrument.
Footnotes
- Outcome figures are each organization’s own results, measured against their own starting point, and are not a benchmark to expect. Starting maturity, sector, workforce profile and program design change the result materially. ↩
Disclaimer
This comparison is an independent analysis by OutThink. Statements about CybSafe are drawn from their own public material and published third-party sources, current as of August 2026. Product capabilities change, and you should confirm anything decision-relevant with the vendor directly. Where we could not verify a capability from public sources, we have said so rather than guessed.
OutThink competes with CybSafe in human risk management. This is a close comparison and we have a commercial interest in your conclusion, which is why the page gives you an evaluation method you can apply to both of us equally.
This page is informational and is not legal, financial or professional advice.