OutThink vs Adaptive Security: after the simulation runs, what changes?
If you have seen their demo, you already know why this comparison exists. Hearing a cloned version of your own voice, or watching a deepfake of your CEO ask for a transfer, is the most arresting thing in this category. It is not a trick, either. Those attacks are real and most organizations have never rehearsed them.
So this page is not going to argue that the simulation is not impressive. It is going to ask the question that comes after it: once the simulation has run, what happens?
Simulating an attack and managing human risk are two different products. One shows you what people would fall for. The other changes what they do and tells you whether it is working.
Compliance training, phishing simulations, campaigns and awareness months. Table stakes, and not where human risk falls.
All four jobs – Motivate, Educate, Activate, Correct – running autonomously across the whole workforce.
Human risk quantified from real behavioral signal in your security stack, and fed back into SOC, IAM and GRC decisions.
Risk-based access controls, user self-remediation, and behavioral governance extended to AI agents.
Adaptive Security straddles L1 and L2 on Activate – multi-channel deepfake, voice and text simulation is its strongest job and it is genuinely ahead there. The open questions are what happens to the person afterwards, and what triggers correction. Apply the four tests to both vendors.
Before you build the comparison grid
- A simulation is a diagnostic, not an intervention. The question to ask both vendors is what the platform does with the result: who gets trained on what, triggered by what, and what the risk position looks like a quarter later.
- The architectural difference is integrations. A risk score built on your EDR, DLP, web filter and IAM signal behaves differently from one built on simulation results, and only one of them can trigger a correction at the moment someone does something risky.
- 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 Adaptive Security does better: the deepfake and voice simulation demo is the best in the market, the UX is modern, product velocity is real, and their self-serve interactive tour is a standard we do not currently match.
- Who we are: founded in 2019 by CISOs who lived this problem, seven years of purpose-built HRM engineering, 100+ enterprise deployments.
First, what Adaptive Security does well
We compete with them directly. Discount our opinion accordingly, so here is the part we have no incentive to write.
- The deepfake and voice simulation is the best in the market and we are not going to pretend otherwise. Cloning an executive’s voice from seconds of audio, video avatars, unscripted two-way calls. It is technically impressive and, more importantly, it is a genuine gap in most organizations' practice.
- OSINT-personalized spear phishing per employee, rather than templates with a merge field. That is a meaningfully harder test than the industry standard.
- The UX is modern and the product velocity is real. Several product lines shipped inside eighteen months, including email security and AI-use governance.
- The self-serve interactive product tour is a standard we do not match. No gated call required to see the product. That is a better buying experience than ours and we should say so.
- Strong review sentiment on the platform relative to its age, and a founding team with a track record of building at scale.
If your objective this year is to show your organization what an AI-powered attack actually looks and sounds like, they will do that better than anyone. The question this page exists to answer is what you do with what you learn.
On what employees experience
Two things are worth separating: whether the experience is engaging, and whether it is defensible.
- On engaging: CyberQ gives every person a cyber-competence score they own, see monthly and can improve, with voluntary learning, content-style choice, WCAG 2.2 accessibility, a two-way channel where people tell us what is not working, and free family training as an employee benefit.
- On defensible: our AI-driven scenarios, including deepfake and voice, are rehearsed inside a Cyber Range rather than delivered as live contact. Vishing and smishing are practiced in-platform rather than by calling or texting employees' personal mobiles.
That distinction matters more than it sounds. If a simulation impersonates a named executive to their own colleagues, or reaches an employee on a personal device, there are consent, retention and misuse questions attached – and in European organizations there is often a works council with a view. Ask both vendors: whose likeness is used and with what consent, how the generated media is retained and destroyed, what stops it being reused, and whether the program has passed a works-council or privacy review.
We are not claiming they have no answer to this. We are saying the question belongs in the evaluation, and that where a scenario can be rehearsed safely in a range instead of delivered live, we think it should be.
Where the platforms diverge: the four jobs
Across more than 100 enterprise deployments we identified four critical jobs every successful human risk program shares: Motivate, Educate, Activate, Correct. All four, running continuously and autonomously across the entire workforce. They are a system rather than a menu, and each depends on the other three.
Both platforms are strong on parts of this. The gap is not modernity and it is not AI. It is what the platform is connected to.
ActivateThis is their strongest job and ours is different in kind
They simulate AI-powered attacks with more realism than anyone. We build practice environments.
Cyber Ranges are immersive, branching, self-adapting scenarios – deepfake CEO video, vishing, smishing, data sharing and handling, secure browsing, endpoint security, social media, physical security and clean desk – that adapt to each person’s role and motivational driver, and whose result feeds a risk portrait. First eight ranges deploying late 2026, and today the non-phishing behaviors are covered through adaptive training and stack-triggered nudges.
The honest framing: a one-off simulation of an executive deepfake is a powerful moment. A range someone can fail, retry and conquer builds muscle memory. Ask what happens the second and third time, and whether the result changes anything downstream.
EducateWhat happens to the person who fell for it
Ours diagnoses why each person clicked – URL literacy, authority bias, click speed, email fatigue – and routes them to remediation matched to that weakness, with training content generated per person from role, behavior and motivational driver.
Ask what remediation follows a failed simulation, and whether it differs between two people who failed for different reasons.
MotivateThe job neither simulation realism nor gamification covers
A short baseline assessment inside the first campaign tells us who responds to personal relevance, who to professional pride, who needs their confidence rebuilt, and who is already a champion. As people work through training they self-report ability and motivation per behavior, mapped to the Fogg behavior model.
Ask what either platform knows about why a person behaves the way they do, as distinct from what they did in a test.
CorrectThe architectural difference, and it is the whole page
Real-time correction needs three things working together: live behavioral data from your security systems, an engine that understands the context of each event, and a delivery mechanism that reaches the right person at the right moment.
Our nudges 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. That means a correction can be triggered by something a person actually did on a Tuesday afternoon, not only by a test we sent them.
Ask both vendors to name their security-stack integrations, and ask what can trigger an intervention other than a simulation result. This is the single most useful question on the page and it takes about a minute.
And then Level 3, which needs all four
At L3, human risk becomes quantified on real behavior from your security systems, the attitudes driving it, level of access, how targeted the person is, device security, and workplace factors such as email fatigue and collaboration networks.
Every vendor now claims a risk score, so the question that separates them is what data feeds it. Ask which inputs to their score originate outside their own product, and ask to see prioritized improvement actions across people, process and technology rather than a ranked list of who failed.
A closed-loop score
- Simulation clicks and reports
- Training completion and quiz results
- Time spent in the vendor’s own modules
- Engagement inside the awareness console
- Vendor-supplied threat intelligence
Measures how people behave inside the vendor’s product. Useful for running the program. Hard for a SOC, IAM or GRC team to act on.
A stack-fed score
- EDR and endpoint events
- DLP and data-handling events
- Web gateway and browsing decisions
- Email and authentication / IAM signal
- Attitudes, access level, how targeted the person is, device security
- Workplace factors: email fatigue, collaboration networks
Measures how people behave at work. Defensible enough to feed access approvals, policy exceptions and incident triage.
The four tests that settle it
Do not take our comparison table on faith, ours or anyone’s. These are the four questions to put to every vendor on your shortlist, including us.
If the answer to the L2, L3 and L4 questions is yes, you have found a genuine HRM platform. If not, you are looking at part of a program rather than a platform for one.
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 Adaptive Security, side by side
The Adaptive Security 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 | Adaptive Security (public information, August 2026) |
|---|---|---|
| Deepfake and voice simulation realism | Deepfake CEO video, vishing and smishing are rehearsed inside adaptive Cyber Ranges rather than delivered as live contact, and the first eight ranges deploy late 2026. For raw realism of a single set-piece moment, theirs is stronger. | Executive voice cloning from seconds of audio, video avatars, unscripted two-way calls. Best in market. |
| Product velocity | Seven years on one platform rather than several product lines in eighteen months. | if breadth of new surface matters to you. Ask what depth each line has today. |
| Buying experience | Our evaluation starts with a conversation. | A self-serve interactive product tour with no gated call. Better than ours. |
| Security-stack integrations | Entra ID, Okta, Microsoft Purview, Microsoft Defender, Microsoft Graph, Jamf, Zscaler, ServiceNow, plus threat enrichment via VirusTotal, IBM X-Force, CriminalIP and Spamhaus. Behavior from EDR, DLP, web filter and IAM feeds both the risk score and the nudge engine. | Ask them to name their security-stack integrations, and ask what can trigger an intervention other than a simulation result. This is the architectural question on this page. |
| What feeds the human risk view | Human Risk Intelligence: behavior from EDR, DLP, web, email and IAM, plus attitudes, level of access, targeting, device security and workplace factors such as email fatigue and collaboration networks. | Dynamic risk scoring. Ask which inputs originate outside their own product, and whether the model or its weighting is published. |
| From risk view to action | Prioritized improvement actions across people, process and technology, some executed automatically, some for approval, some for your team to action. | Ask to see the recommendation layer rather than the ranked list, and ask what is executed automatically. |
| What happens after a failed simulation | Automated root-cause analysis diagnoses why the person failed and auto-enrolls them in remediation matched to that specific weakness. | Ask whether remediation differs between two people who failed the same simulation for different reasons. |
| Coverage of the four L2 jobs | All four run autonomously across the workforce, with Activate outside the inbox extending from late 2026. | Educate and Activate are strong on the simulation side. Ask what covers Motivate, and what triggers Correct from your own security signal. |
| Motivation and the manager layer | CyberQ: a personal cyber-competence score people own and improve, with a manager view and an HR feed where governance allows. | Ask what motivates an employee to improve between simulations, and what a line manager can see and do. |
| Simulation ethics and consent | AI-driven scenarios including deepfake and voice are rehearsed in-platform rather than delivered to personal devices. | Ask whose likeness is used and with what consent, how generated media is retained and destroyed, what prevents reuse, and whether the program has passed a works-council or privacy review. Relevant in most European organizations. |
| Enterprise track record | Founded 2019. 100+ enterprise deployments, seven years of HRM engineering, named enterprise references across banking, construction, healthcare and industrials. | Founded 2024. Named enterprise logos and strong review sentiment for the age of the company. Ask how many customers have completed a second renewal cycle, and for references live for two or more years at your scale. Arithmetic, not a criticism. |
| Localization | 40+ languages across content, the full end-user experience, nudges, and Cyber Ranges from late 2026. | Broad language coverage. Ask whether the count covers the end-user interface and notifications or content only, on both sides. |
| Pricing transparency | 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 what tier the deepfake, voice and multichannel simulation capabilities sit in, since the headline offer and the full capability set may not be the same thing. |
| 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, who supports the program day to day, and what the integration work looks like given the architectural question above. |
| Independent ratings | See OutThink on Gartner Peer Insights. | See Adaptive Security on G2. Note that both of us have smaller review corpora than the largest platforms in this category, and read for the specifics rather than the score. |
Sources for the Adaptive Security column. Adaptive Security product and platform pages, their funding and launch announcements, 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.
If you are running both, or moving between them
Modern-to-modern moves are different from leaving a legacy platform, so this is shorter than it would be elsewhere.
- Keep your report button. You have spent years training people on the button they recognize, and OutThink integrates with it rather than asking you to retrain the reflex.
- Keep your LMS. OutThink works alongside major LMS platforms and returns behavioral telemetry rather than completion status.
- Run the deepfake moment, then keep going. If a set-piece simulation has already done its job internally, the useful next step is not repeating it. It is connecting the result to something that changes behavior between events.
- Start where you are. Packaging follows the maturity model, so you do not restart at L1 if you are already partway through L2.
- Know what the first month looks like. Directory and SSO in week one, first campaign and baseline inside week four. Stack integrations are scoped separately and sequenced after the program is running.
Frequently asked questions
Yes, and both organizations are competing for the same budget. The distinction worth holding is that they are strongest at simulating what an AI-powered attack looks like, and we are built to change behavior and quantify the risk that remains.
They position around securing people against AI-powered threats, which is a real and current problem. HRM is being adopted as a label across the market; the four tests above are how you tell what is behind it. The integration question is the fastest one.
It matters a great deal, and we would not argue otherwise. The question is what it is evidence of. A simulation demonstrates exposure. It does not by itself demonstrate that exposure is falling, and the second and third run of the same set-piece produce less than the first.
For a period, yes. It is not a destination, because running two programs splits the behavioral signal that makes adaptive training and risk quantification work.
By what feeds it. HRI ingests behavior from your EDR, DLP, web, email and IAM systems, plus attitudes, access level, targeting, device security and workplace factors, and produces prioritized actions across people, process and technology. A score computed from simulation results measures how tests went.
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.
Find your level, then decide
You do not need a position on OutThink versus Adaptive Security to make progress this quarter. You need to know which of the four jobs your program runs, and what your platform is connected to.
Run the simulation. Then ask what happens on the Tuesday afternoon when nobody is testing anyone.
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 Adaptive Security 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 Adaptive Security in human risk management and security awareness training. We have a commercial interest in your conclusion, which is why this page gives you an evaluation framework you can apply without us.
This page is informational and is not legal, financial or professional advice.