Deepfake Phishing Simulation & Red Team | Breacher.ai

OSES™ · Orchestrated Social Engineering Simulation

Your training stopsat the click.The attack doesn't.

OSES™ Platform: training beyond the click, secure behavior management. Measure your risk, train for what you find, and prove it changed, on one platform.

Deepfake voice, live avatar, and video, delivered through Teams, Zoom, Meet, telephony, email, and SMS.

“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.”
CISO, Large Financial Enterprise
“I was expecting a demo, not an episode of Black Mirror. This is really good. I’m surprised at how advanced it’s gotten.”
CEO, Cybersecurity (North America)
“The training was well-structured, clear, and provided valuable insights into the growing threat landscape associated with deepfakes.”
GRC Manager, Manufacturing (EMEA)
Watch Demo
92% of orgs tested were vulnerable to deepfake social engineering
63% could not tell synthetic from real

Trusted by security teams in

Fortune 500/ Financial Services/ Global Law/ Private Equity/ Regional Banking/ Technology/ Community Banking/ Enterprise SaaS/ Fintech Infrastructure/ Managed IT Services/ Mining & Metals/ HR Technology/ Energy/ Tax & Compliance Software/ Manufacturing/ Transportation & Logistics/ Public Sector/

Click Rate Cannot See
What Is Actually Happening

Attacks now arrive inside the platforms your people are told to trust. A cloned voice on a call. A deepfake face in a Teams meeting. Every security tool in the path works exactly as designed, and none of them owns the space between them. The process breaks before the person does.

0
Tools In Your Stack That Measure This
Your gateway scores mail. Your EDR scores endpoints. Nothing in the stack produces a number for whether a person or a process can be talked into the wrong action. That gap is the one we close.
The Measurement Gap
78%
Were Highly Vulnerable
Not "someone clicked." Highly vulnerable means the process itself failed. A callback was trusted, an approval was granted, a control was bypassed on the strength of a synthetic voice.
Breacher.ai Benchmark
63%
Couldn't Tell Synthetic From Real
Nearly two thirds of the people we tested could not identify an AI impersonation while it was happening to them. Detection is not a control you can train your way into.
Breacher.ai Benchmark
What we actually run

One orchestrated scenario, every channel an attacker uses

Voice, video, and messaging coordinated into a single engagement, run against your people, your procedures, and the controls behind them.

OSES™ · Orchestrated Social Engineering Simulation

What goes in.
What actually gets tested.

What we orchestrate
AI voice cloning Cloned exec calls the target back
Deepfake video Live impersonation on Teams or Zoom
Agentic messaging AI email, SMS and chat lures
OSES™ Orchestration
engine
What we actually test
People Was procedure followed and reported?
Process Callback, approval and reset procedures
Technology Do your controls detect any of it?
Coverage of a real attack chain
Training Reporting Lure sent User clicks Voice callback Helpdesk reset Process tested Objective hit Controls tested Fix guidance Peer benchmark
Awareness trainingCovers 4 of 11
Breacher.ai OSES™Covers 11 of 11
Stops at the click Where the loss actually happens

See What a Real Simulation Looks Like

The platform

Measure your risk. Train for what you find. Prove it changed.

Three stages, one dataset running through all of them. Measurement without training is an audit finding. Training without a re-test is a completion rate. The third stage is the one almost no program runs, and it is the only one that turns any of this into a control.

01Measure
One orchestrated scenario across the channels that carry your risk, run against the procedures meant to catch it.
02Train
Training generated from your own engagement data, aimed at the procedure that failed rather than a generic module.
03Prove
The same paths run again, with the delta reported against your baseline and your position against your sector.
The OSES™ Platform

Secure behavior management, on one dataset

Most programs stitch this together from three vendors: a simulation tool, a training catalogue, and a survey that claims to score the result. Nothing connects them, so nothing can prove the training worked. OSES™ runs all three stages against the same engagement data, which is what makes the re-test a real measurement rather than a second opinion. Three editions, one platform. What you start with follows the stage you are accountable for, not the size of the organization.

Measure

OSES™ Simulate

The simulation edition. Establishes the baseline, runs orchestrated scenarios across the channels that carry your risk, and produces the Social Engineering Risk Index.

Organization level exposure reporting
AI spear vishing at up to 150 concurrent sessions
Evidence written for a board and an auditor
Train

OSES™ Behave

The secure behavior management platform edition. Risk-based security training aimed at the procedure that failed, then behavior change tracked through re-test.

Training built from your own results
Procedure first, not detection first
Re-test delta as the completion metric
Peer benchmark

OSES™ Measure

See how you rank in comparison to your vertical. Your measured exposure is reported next to the position of your sector, so the number arrives with the context that makes it actionable.

Ranked against your own vertical
Sector median with published methodology
Benchmark gets denser with every run
Inside the platform

One console, all three stages

Scenario design, training, and reporting are the same product reading the same engagement data.

Stage 01, Measure

Design the scenario in plain language

Describe what you want to test, or paste a real-world attack to replicate. The playbook drafts itself with the persona, the goal, and the channel, and you edit it before anything runs.

Breacher.ai AI Concierge building an AI vishing simulation playbook for a CEO help desk impersonation call.
Stage 02, Train

Build the module from what failed

Start from scratch, from a policy document, or from the simulation a learner just fell for. The remediation module is generated against the procedure that broke, not a generic catalogue entry.

Breacher.ai training builder start screen for creating a risk-based secure behavior training module.
Stage 03, Prove

Report the movement, per channel

Every vector measured against your industry baseline, with the delta on each metric and the trend across re-tests. This is the artifact that goes to the board and the auditor.

Breacher.ai risk reporting dashboard showing social engineering risk scores and peer benchmarks per vector.
Stage 01, Measure

How do you measure your risk?

Almost every number a security team currently holds on this describes intent. None of it observes behavior in a live channel.

How exposure is usually established
Assumed
·A questionnaire or maturity self assessment
·Click rate from an email test that never leaves the inbox
·A vendor benchmark built from somebody else's population
·Confidence that the finance team would verify a payment instruction
·Coverage reported as course completion
Every line here is a statement about what people believe would happen.
How we establish it
Measured
Action rate: did a consequential action actually occur
Process hold rate: did a verification procedure stop it in time
Report rate: did anyone flag it, and how fast
Channel breadth: how many channels produce a failure
Re-test delta: what moved after training
Five organization level inputs, computed from observed simulation outcomes rather than a survey. Together they produce the Social Engineering Risk Index.
Stage 02, Train

What do you train, if not detection?

You train the procedure the simulation just broke, using the simulation itself as the source material. This is behavior-based security training rather than awareness content, and it is what a security behavior and culture program (SBCP) is supposed to deliver. Secure behavior management is the category Gartner named in 2026 as the successor to human risk management, and its founding argument is one we already held: labelling your workforce a risk does not change what they do.

Built from your own results

Training is generated from the engagement data, not selected from a catalogue, with the exact point of failure as the teaching moment.

Procedure, not tells

The lesson is what a person must do before acting on an instruction, regardless of how convincing the deepfake is. That holds as generation quality improves. Teaching people to spot a fake does not.

Written to be re-tested

Every module maps to a path that can be run again, so training is scoped to something measurable. A course completion is not a control, and it never was.

Stage 03, Prove

How do you prove it changed?

You re-test the same paths and report the movement, and the number that moves is the Social Engineering Risk Index. An index rather than a score, because a score grades a subject and an index positions a population.

The reading, illustrative
64
Organization index, reported with the sector position, never on its own
Your organization64
Sector median51
Re-test, same paths78
Illustrative figures. The absolute number and the peer position always appear together, because a percentile on its own hides the case where the sector median is itself unacceptable.
Five inputs, all observed
01
Action rate
Did a consequential action occur. A payment, a reset, an approval. Not a click.
02
Process hold rate
Did a verification procedure stop it before the action completed. This is the number nobody else in the category can report.
03
Report rate
Did anyone flag it, through which route, and how quickly relative to the action.
04
Channel breadth
How many channels produce a failure. Coverage on four of eleven is a different exposure from eleven of eleven.
05
Re-test delta
Movement after training on the same paths. This is what makes prove it changed a literal claim.
Every other risk assessment in this market is a survey. Read the index methodology.

Case Studies & Research

Real engagement findings, threat research, and platform analysis from the team running the simulations.

Case Study A Cloned CEO Voicemail. 16% Gave Up Credentials. A cloned CEO voicemail dropped silently onto company phones at a North American financial services firm. The full engagement, step by step. Read it Case Study Agentic AI and a Cloned COO A quarterly engagement against a multinational financial services firm. Cloned executive voice, agentic AI behind it, run against corporate mobile. Read it Engagement The Helpdesk Mini Red Team A short, focused assault on internal support workflows using deepfake audio and agentic AI. Findings and remediation in days, not quarters. Read it Buyer's Guide Best Deepfake Simulation Platforms 2026 Orchestrated kill chain versus multi-channel versus legacy. How the field actually ranks against the threat as it runs today. Read it Red Team The Deepfake Candidate Epidemic Synthetic candidates are passing video interviews and getting hired. Awareness training cannot test a hiring pipeline. A red team can. Read it Threat Intelligence Gartner's 2026 Deepfake Data 41% of organizations hit on audio calls, 35% on video. Gartner prescribes second-channel verification. Teaching that policy is not the same as testing it. Read it Analysis What the Mercor Breach Changes A practitioner's read on what the breach actually shifts for enterprise deepfake defense, and what it leaves exactly where it was. Read it Kill Chain Black Basta, UNC1069, and the Gap Two attributed campaigns mapped against three categories of simulation platform. What the chain requires, and where each category stops short. Read it

Questions Every CISO
Asks Us First

What is a deepfake red team engagement?
A deepfake red team engagement is an authorized security assessment in which AI-generated voice, video, and messaging are used to impersonate trusted people inside an organization, in order to test whether that organization's people and processes can be manipulated. Unlike a phishing simulation, which ends when a user clicks a link, a deepfake red team engagement continues into the process that follows: the callback to a spoofed number, the helpdesk approving a credential reset, the finance approval that releases a payment.
What is OSES™?
OSES™ stands for Orchestrated Social Engineering Simulation, a methodology developed by Breacher.ai. An OSES™ engagement coordinates multiple synthetic channels at once, including AI voice calls, deepfake video, email, and messaging platforms, so the simulation mirrors how a real attacker builds trust across channels rather than testing a single email in isolation.
How is deepfake red teaming different from security awareness training?
Security awareness training and phishing simulation measure whether an individual clicks a link and whether they report it. A deepfake red team engagement measures what happens after that point. It tests whether the helpdesk verifies identity before resetting a password, whether finance follows callback procedure before releasing funds, whether the escalation path holds under pressure, and whether existing defensive controls detect the activity at all. Click rate measures individual detection. A red team engagement measures organizational process resilience.
Can employees be trained to detect deepfakes?
Detection is not a reliable control. Across Breacher.ai engagements spanning voice, video, email, SMS, and collaboration platforms, 63% of the people tested could not distinguish synthetic voice or video from a real person while the interaction was happening. Because individual detection cannot be relied upon, resilience has to be built into process controls: verification procedures, callback rules, and approval workflows. Testing those controls is what an OSES™ engagement is designed to do.
What is a deepfake phishing simulation?
A deepfake phishing simulation is an authorized test in which AI-generated voice, video, or messaging impersonates a trusted person in order to measure how an organization responds. Unlike traditional phishing simulation software, which sends an email and records who clicked, deepfake phishing simulation reproduces the synthetic voice call or video meeting that real attackers use to establish credibility before any request is made, then follows the interaction into the process that acts on it.
How do you run a deepfake simulation exercise?
In four stages. Open-source intelligence establishes who would be impersonated and which business processes an attacker would target. Synthetic voice and video assets are generated for those specific people. The simulation is delivered across coordinated channels, typically an AI voice call, then video inside Teams, Meet or Zoom, then email or SMS. Finally the engagement continues past the point of engagement into the helpdesk approval, callback procedure, or payment authorization, and findings are reported at the organizational level. The first cycle runs fully managed and external, with no software installed and no integration into your environment.
Do you run vishing simulations and CEO fraud testing?
Yes. Vishing simulation using cloned executive voices is the most requested single component, because AI voice is where most orchestrated attacks begin. Campaigns place outbound calls, hold a live conversation if answered, leave a callback voicemail if not, and handle the inbound callback autonomously. For CEO fraud prevention specifically, the test targets the approval chain rather than the individual: whether the wire threshold triggers a callback, whether that callback goes to a verified number, and whether an urgent request from a familiar voice can bypass either.
Can deepfake phishing simulation be used in regulated industries?
Yes, and it is a large share of the work. Engagements run fully external with no software installed and no changes to your security stack, which removes most of the change-control burden that blocks testing in regulated environments. Deliverables include third-party assessment documentation and attestation suitable for auditors, cyber insurance underwriters, and frameworks requiring evidence of awareness and control testing. Clients include financial services, banking, global law, energy, and public sector organizations.
Who is Breacher.ai for?
Breacher.ai works with CISOs, VPs of Security, and Security Directors running red team or assessment programs, and with Security Awareness Managers and training leads who need realistic AI impersonation content. Clients include Fortune 500 transportation, manufacturing, and energy companies, financial services firms, global law firms, and public sector organizations.

Get a real number
for your own organization.

Thirty minutes on the platform: how a baseline is scoped, what the training is built from, and what the re-test actually proves. You decide whether your process would have held.

No IT integration required Runs fully external Audit and insurer-ready reporting
92% of organizations we test are vulnerable G2 Gartner Peer Insights

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How organizations are successfully defending against deepfake threats