Best Deepfake Simulation Platforms | Breacher.ai 2026
Best Deepfake Simulation Platforms: The Three Generations, Compared
Every product in this category sits in one of three architecture bands. What separates them is not the feature list, it is how much of a real sequence each one can run.
If your platform cannot simulate modern threats, what are you measuring?
Understanding the difference, and why it matters
Most platforms stop at the click. Modern social engineering does not: it is a voice, an action, and a consequence. The band a platform sits in decides how much of that it can even see.
The bands below describe typical products, not one named product. Individual vendors sit at different points inside a band and some straddle two, which is why the scorecard below scores them one at a time.
If what you need is a large compliance-based training library, we are not your vendor, and the rest of this guide will not change that.
We believe security is unique to every organization. Rather than shipping a canned catalogue, we give organizations the tools to curate training to their own environment, generated from their own policies, their own simulation results, or authored themselves.
If your obligation this quarter is a compliance checkbox, you can stop here.
Security is not a one-size-fits-all approach.
Legacy, email-first
Reproduces the first message, and nothing after it. Phishing tests and training content, built before deepfakes existed and designed to count clicks on templates. Everything else follows from that design decision.
Does well. Email at scale, large training libraries, LMS integration, and reports your governance team already knows how to read. If your obligation right now is documented training coverage, that is a real fit.
Stops at. Steps that do not talk to each other, nothing that answers when a target calls back, and a report that knows opens, clicks, and completions. Bolting a voice feature onto this architecture does not change what it can measure.
Multi-channel
Reproduces the individual stages, but never the sequence. Real synthetic voice, video, and SMS alongside email, with good cloning quality and research-driven pretexts. Genuinely broader than the first band.
Does well. Much wider coverage than email alone, and the better products run live adaptive voice with believable cloning. Some publish large benchmarks, though those usually measure clicks at volume rather than what people did under a full sequence.
Stops at. Each channel running as its own campaign, scored on its own, which does not reflect how modern social engineering actually unfolds. The numbers come back cleaner than real life. Video support is patchy across Teams, Meet, and Zoom, and the video is often a recording rather than a live face.
Orchestrated
Reproduces the sequence as one connected event. Every step reads what the target did in the step before, then decides what happens next. You get one result for the whole sequence instead of four separate channel scores. This is the band we build in, under OSES™ (Orchestrated Social Engineering Simulations), and it is what our deepfake simulation service runs on.
Does well. Simulations that build the way a real sequence does, something that answers when the target calls back, a report that names the process rather than the person, and metrics that look at action and process rather than just the user.
Stops at. It asks more of you up front. Someone has to decide which processes are in scope and what counts as a consequential action. It also does not flatten into a single dashboard number, so a program used to reporting click rate has to change what goes on the slide.
The third band tests the most, which does not automatically make it the right purchase.
Twelve criteria, six options, scored on public evidence
Start here if you are shortlisting. Each cell records what the vendor publicly documents as of September 2026, checked against their own product pages, pricing pages and published research. A blank cell means we could not find a public claim, which is not the same as the capability being absent. Several of these vendors gate their product documentation behind a login or a demo.
- ● Documented
- ◐ Partial, or documented with a material caveat
- ○ No public claim found
| Criterion | Breacher.ai | Adaptive | Hoxhunt | Doppel | Brightside | Generic awareness training |
|---|---|---|---|---|---|---|
| Original threat researchFirst-party findings, not curation of other outlets | ●YesFirst-party findings from live engagements | ○No claimResearch hub is external interviews and news aggregation; statistics posts cite third parties throughout. No first-party findings found | ●YesAnnual trends report drawn from its own simulation volume | ●YesNamed threat actor briefs from own telemetry | ○No claimBlog is buyer-guide content, not research | ◐Varies |
| Generic trainingStock library and compliance catalogue you can just assign | ○NoTraining is generated from your policies and results. We do not ship a stock compliance catalogue. We believe security is unique to every organization | ●Yes1,000+ resources, named framework tracks, 39 languages | ●Yes300+ modules, regulatory tracks, 40+ languages | ◐PartialHundreds of videos documented; no named frameworks or languages | ◐PartialCourse library documented; no compliance catalogue or count | ●YesThe category's core strength |
| OrchestrationLater stage branches on what the target did in the stage before | ●YesOSES™ conditional sequencing is the product | ○No claimDocuments coordinated multi-channel, not branch logic | ○No claimMulti-channel plus adaptive training difficulty | ◐PartialMulti-step campaigns documented; branch logic never stated | ○No claimHybrid voice plus email | ○No claim |
| Instantly create awareness training | ●YesGenerated from your policies, a prompt, or simulation results | ●YesPrompt-based and policy-fed content generation | ◐PartialPolicy to training documented; result-driven generation not | ●YesContent builder drawing on simulation results | ◐PartialAuto-assigns from a fixed library rather than generating | ○No claimFixed catalogue |
| Process testingDid a procedure hold, not did a person click | ●YesProcess hold rate against a named consequential action | ○No claimMeasurement is per-user risk scoring | ○No claimMeasurement is per-user click and report behaviour | ●YesHelpdesk mode tests password reset and MFA re-enrollment procedures | ○No claim | ○No claim |
| Human risk managementPer-person risk scoring and behaviour change tracked over time | ◐PartialWe focus on process invocation rather than only the user. Per-person risk scoring is not our primary output | ●YesPer-employee risk scores updating in real time, with just-in-time remediation | ●YesPositions itself as human risk management; per-user adaptive difficulty and cadence | ●YesHuman risk management is a named product line | ●YesPer-employee vulnerability score out of 100, plus risk by department | ◐VariesIncreasingly added, but historically completion tracking |
| Video conferencing simulationsSimulated attacks delivered inside Teams, Zoom or Meet | ●YesTeams, Zoom and Meet | ○NoTeams and Slack chat messages only; no meeting simulation documented | ◐PartialSimulated meeting window rather than a real call; Teams messaging documented separately | ●YesTeams and Zoom meetings | ○No claimEmail and voice only | ○No claim |
| Deepfakes | ●YesCloned voice and video | ●YesCloned voice and video | ●YesCloned voice and executive likeness | ◐PartialVoice on the attack side; video documented for training content | ◐PartialVoice self-serve; video only as a managed engagement | ○No claim |
| Interactive simulationsReal-time two-way conversation, not a recording | ●YesInteractive video avatars that join Teams, Zoom or Meet and hold a conversation, plus conversational voice agents | ●YesReal-time voice calls from deepfake personas; interactive conversations in training | ●YesReal-time AI voice agents that respond in conversation; video scenarios are pre-scripted | ●YesConversational attack simulation with real-time follow-ups during a live call | ●YesAI adapts and responds during live phone calls; voice only | ○No claim |
| Enterprise scaleLarge seat counts, directory provisioning, procurement evidence | ●YesSOC 2; deployed at enterprise scale, with Entra ID integration, full API and CLI | ●YesSOC 2; SCIM across Entra ID, Okta, Ping | ●YesMillions of users, 129+ countries; SSO and SCIM; multi-tenancy; SOC 2 | ●YesNamed enterprise customers; SOC 2, ISO 27001, 27701 and 42001 | ○No claimNo published customers, SOC 2, ISO 27001 or SCIM | ●YesThe category's core strength |
| Transparent pricingRates published on the vendor's own site | ●YesTiers and floors on the pricing page | ◐PartialQuote only on site; one tier listed on a cloud marketplace | ○NoPer-employee, quote on request | ○NoDemo request only | ●YesPer-seat monthly rates published | ◐Varies |
| Teaches users to spot deepfakesRead this row in reverse. We treat perceptual detection training as a decaying control | ○No, argues againstWe teach process invocation and out-of-band verification, a control that does not decay | ●Yes, leads with itPublishes an artifact curriculum covering blink symmetry, hairline smearing, lighting direction, lip-sync delay, prosody and timbre, with procedural controls framed as the complement | ○No claimSimulation exposure to reduce blind trust in voice and video channels, rather than artifact training | ○No, argues againstStates publicly that red flags fade with every model release, and that out-of-band verification is the control that holds regardless of media quality | ◐PartialTeaches visual and audio tells, then states that the most effective defense is not better detection but verification systems that work regardless of how convincing the fake appears | ●YesSpot-the-fake modules are the category norm |
Compiled from each vendor's public website on 19 September 2026. We are one of the six, so read our column with that in mind, and verify any row that decides your shortlist directly with the vendor. Capability behind a login is real capability, it is simply not something we can check. If you represent a vendor listed here and a cell is wrong or out of date, tell us and we will correct it.
Have us run this against your organization
Describe the sequence you actually worry about, the one that ends somewhere that matters. We will reproduce it end to end and show you where it terminates, what your process did, and how you sit against your sector.
No obligation, and nothing runs against your people without a signed scope. If you would rather just talk it through first, that is fine too.
Full write-up: agentic voice red team case study. For incidents that ran this way in the wild, see real deepfake attack examples.
A platform that only sends the first message cannot measure anything past the click, and past the click is where the consequence lives.
Compare the outcome, not the channel list
Channel counts stopped discriminating years ago. Every platform can claim voice, video, and SMS. The question that still separates them is what comes out at the end: what the report can contain, what the number describes, and who the finding is written about.
If you are only measuring users, you are measuring the smallest part of the failure. A click rate is a statement about one person's judgment on one message. It says nothing about whether the help desk asked for a ticket, whether accounts payable called the number in the directory, or whether the procedure that was supposed to catch this was ever invoked. That is where the loss actually happens, and it is a property of the organization, not of the person who answered the phone.
| What you get out | Gen 1, email-first | Gen 2, multi-channel | Gen 3, orchestrated |
|---|---|---|---|
| Subject of the measurement | The user, on one message | The user, once per channel | The organization, under a full sequence |
| What the report contains | Opens, clicks, completions | Per-channel engagement | Action, verification, process hold, report rates |
| Was a procedure tested | No, none was defined | No, no consequential action anchors the run | Yes, process hold rate against a named action |
| Is verification counted | No, there is nothing to verify against | No, engagement is scored, not the check | Yes, the share who independently checked |
| Can improvement be proven | Completion rates only | Per-channel movement, one leg at a time | Re-test delta on the same paths |
| Unit of result | One message | Four separate scores | One result for the whole sequence |
| Who the finding names | The person who clicked | The channel that performed worst | The process that permitted the action |
| Best fit | Coverage and compliance evidence | Breadth beyond the inbox | A defensible exposure figure |
Describes bands, not named products. Individual vendors sit at different points inside a band, and some straddle two.
The line between Generation 2 and Generation 3
Vendors use multi-channel and orchestrated as if they mean the same thing. They do not, and the difference is the whole gap between the second band and the third.
The platform can reach someone in more than one way. An email goes out. A call goes out. A video invite goes out. Each is scored separately.
Each stage reads what the target did in the previous stage and conditions on it. The call escalates only if the message landed. A target who verified independently at stage two never sees stage three, and that fact is itself a finding.
This matters for the number, not just for realism. Single-channel results look clean because they never look at the full chain. Measuring four channels separately does not reflect what happens when the stakes are real, and that gap is the distance between your training report and what an adversary would find.
Fidelity is a threshold, not a goal
All of the above is about structure. None of it matters if the synthetic media falls apart in front of someone paying attention, and this is the part most commonly misunderstood by the people writing the evaluation.
You are not measuring whether your people can spot it.
Breacher.ai engagement data, measured across tested participants and client organizations.
A control built on detection is a countdown.
Detection is a losing battle for humans, and the margin narrows with every model release. Procedural verification is the better control, because it does not depend on the fake being imperfect. If your awareness platform is still teaching people to spot the tells, it is not neutral, it is counter-productive: it builds confidence in a skill that is degrading, and a confident target verifies less.
- Gets weaker with every model release
- 63% of the people we test cannot tell, live, in the moment
- The only fix on offer is “be more careful next time”
- Puts the failure on a person, by name
- Cannot be re-tested into an improvement you can show
- Holds regardless of how good the fake is
- The same step works for voice, video, and a live call
- Has an owner, a trigger, and a documented step
- Names the process that permitted the action
- Fixable, and provable on the next re-test
Fidelity has one job: to remove detection from the experiment, so the procedure behind the person is the only variable left. For voice, the practical bar is a live agent that survives an interruption, an off-script question, and being placed on hold, rather than a clip. For video, a face that joins the call and responds in real time rather than a rendered playback link, working natively on all three of Teams, Meet, and Zoom rather than one of the three. We broke down what that looks like on a live call in our remote support simulation write-up.
There is a practical catch. Mediocre fidelity does not give you a cautious result, it gives you an unusable one, because afterward you cannot tell whether someone disengaged for the right reason or because the rendering looked wrong.
Measure the right thing
Whichever band you buy, the report is the product. Click rate is a delivery diagnostic for the email channel and little more, because a phone call contains nothing to click. Six numbers replace it, all anchored to a defined consequential action, and we published the dataset behind them in our 2026 deepfake benchmark.
What to do before you talk to any vendor
Three things, all free, all doable this week. They will tell you more about which band you need than any demo will.
- Write down the sequence, not the channel. One paragraph describing how someone would actually get to something that matters in your organization: who they would call, what they would ask for, and which system the loss lands in. That paragraph is your evaluation script. Hand it to every vendor and ask them to reproduce it end to end.
- Name the consequential action in advance. Not “engaged with the caller.” A specific privileged act: an MFA re-enrollment, a payment instruction change, a remote session accepted. If nobody can name it before the run, the report will have nothing to anchor to afterward.
- Ask each vendor where their sequence terminates. Then ask them to describe the branch logic between stages out loud. A vendor who can only describe the channels they support, and not what the second stage does differently when the first one succeeds, is describing multi-channel.
Where we sit, and the part that undercuts this guide
Now the uncomfortable part. Across our engagements, the platform an organization runs has not necessarily predicted how it performs. How the organization treats security matters more. Your awareness platform is a tool, and how you wield it is what matters. Run the same voicemail and callback technique across different organizations and the action rate ranges from zero to 34.5%.
The vendor is not necessarily the variable that moves the outcome. What correlates is whether a verification procedure exists and holds under pressure: whether the help desk can say no to an executive, whether people were told what to do rather than what to spot. You run this evaluation because you cannot fix a failure you have never observed, not because the tool is the fix.
“Kudos to your entire team. We haven't even seen the report and the whole company is talking about the risks of voice cloning. It's been a huge win for us already.”
Measure your risk.
Train for what you find.
Prove it changed.
Bring your own sequence. We will run it end to end and show you where it terminates.
Book Your DemoFrequently asked questions
Generation 1 is legacy email-first phishing simulation with awareness content attached, built to count clicks on templates. Generation 2 is multi-channel: voice, video, and SMS fired as parallel campaigns and scored channel by channel. Generation 3 is orchestrated: one conditional sequence where each step reads what the target did in the previous step. These are architecture bands rather than tiers of quality, and many programs correctly run a first-band platform alongside a third-band one.
Multi-channel means the platform can reach a target through more than one medium, each scored separately. Orchestrated means each stage conditions on what the target did in the previous stage, so the sequence itself is the unit of measurement. If a vendor cannot describe the branch logic between stages out loud, they are describing multi-channel.
Start from what your program is missing, not from which band sounds most advanced. Then describe the sequence an adversary would run against you and ask each vendor to reproduce it end to end, terminating at a named consequential action. Whatever a platform cannot reproduce is exposure its report will not describe.
Less than most buyers assume. Across our engagements the platform an organization runs has not necessarily predicted how it performs, with action rates on the same technique ranging from zero to 34.5% across organizations buying from the same short list. What correlates is whether a verification procedure exists and holds. You run the evaluation because you cannot fix a failure you have never observed.
Good enough that detection stops being the variable. Across our engagements, 63% of participants could not distinguish synthetic media from a real person in the moment. Fidelity exists to remove detection from the experiment, not to prove people can be fooled. Mediocre fidelity produces an uninterpretable result rather than a conservative one.
Only as a delivery diagnostic for email. In one engagement, 24.3% clicked, 21.6% called a spoofed number back and spoke to an autonomous voice agent, and 16.2% divulged credentials. A click-rate program reports the first figure and never records the other two.
The scorecard compares all five plus generic awareness training across twelve criteria, scored on what each vendor publicly documented as of September 2026. Two rows separate cleanly: no vendor other than Breacher.ai publicly documents conditional orchestration where a later stage branches on what the target did earlier, or a real-time video avatar that joins a live call. One row is scored in reverse: on teaching users to spot deepfakes, a documented capability is the weaker position, and Doppel and Hoxhunt score as favourably there as we do. Several rows do not separate at all. Doppel documents helpdesk process testing in detail, Hoxhunt publishes larger-sample threat research, Adaptive and Doppel both generate training content, and Brightside publishes per-seat pricing. A blank cell means no public claim was found, not that the capability is absent, since several of these vendors gate their documentation.
No. Encouraging users to identify synthetic media by its artifacts is a fundamentally flawed approach, and we consider it counterproductive rather than merely ineffective. Every tell taught today is a defect in the current generation of synthesis, and those defects are being engineered out on a release curve the buyer does not control, so the training is worth less the day after it is delivered. Real-time voice and video also leave no inspection window, because the decision is made during the conversation rather than afterwards. The dangerous part is the confidence it manufactures. Someone who has completed detection training and believes they can tell is more exposed than someone who knows they cannot, because only the second person reaches for the verification step. Training that improves self-assessment faster than it improves accuracy has made the organization worse, and nothing in a completion rate will show that. To be precise about what deserves scrutiny, publishing an explainer on what artifacts exist is fine, and several vendors we rate favourably do it. What deserves heavy scrutiny is a vendor that sells artifact recognition as the control, measures its program on whether people spotted something, or positions procedural verification as the complement to detection rather than the other way round. Ask any vendor making that claim a single question: what happens to your product when the next model release removes the tell you built it on?
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