How to Protect Against Deepfakes: A Step-by-Step Defense Guide for Organizations

Protecting against AI deepfakes requires a layered defense: personal exposure management, organizational verification protocols, employee readiness, and a tested incident response plan. Deepfakes exploit human perception rather than technical vulnerabilities, which is why no firewall blocks a convincing synthetic video call.

A deepfake is synthetic media generated by AI to fabricate or replace a person's likeness, voice, or both, and the cyberattacks built on this technology are no longer theoretical. In 2024, engineering firm Arup lost $25 million after a finance employee was deceived by a deepfake video call impersonating the company's CFO. The case set the benchmark for corporate exposure.

This guide covers every layer of that defense: how to reduce the OSINT footprint attackers use to build deepfakes, how to establish verification protocols and phishing-resistant MFA that hold under social engineering pressure, and how to train employees to recognize and resist synthetic voice and video attacks. It also addresses what organizations must do the moment an incident is discovered.

See how Adaptive Security's deepfake awareness training prepares employees for synthetic voice and video attacks before a real attacker tests the team.

What Is a Deepfake and Why AI Deepfake Attacks Are Accelerating

A deepfake is synthetic media generated by artificial intelligence, specifically generative adversarial networks (GANs), diffusion models, and voice cloning systems, that fabricates or replaces a real person's likeness, voice, or both with enough fidelity to deceive human perception.

Unlike a cheapfake, which uses low-tech editing tools like speed manipulation or cropping to mislead viewers, a deepfake is produced by AI models trained on real source material and capable of generating entirely new, never-recorded speech or video. That distinction matters for defense: cheapfakes are detectable through basic forensic inspection, while deepfakes defeat both untrained observers and, increasingly, automated detection systems.

How Little Data Does an Attacker Need to Build a Convincing AI Deepfake?

Modern voice cloning tools synthesize a convincing audio replica from as little as three seconds of recorded speech, sourced from earnings calls, LinkedIn video posts, or conference recordings.

Video deepfakes require only dozens of clean images, material routinely available in public profiles and social media accounts. Any executive, employee, or public-facing team member who has ever appeared in a recording or posted online is a potential target, and their digital presence is the raw material cybercriminals need.

Why Deepfake Cyber Threats Have Outpaced Conventional Security Controls

Deepfakes exploit human perception, not technical vulnerabilities, which is exactly why conventional cybersecurity controls are structurally blind to them. Reducing the attack surface starts with controlling how much deepfake-ready source material attackers can harvest about employees before any call is ever scheduled.

Step 1: Reduce the Digital Footprint Attackers Use to Build Deepfakes

Reducing publicly available photos, videos, and audio limits what attackers can harvest, raises the effort required to clone a credible likeness, and forces threat actors toward easier targets.

Audit and restrict social media profiles, remove tagged content, scrub personal details from data broker sites, and apply platform privacy controls wherever public-facing content exists.

Executives and high-profile employees carry the greatest risk because their content is abundant, easily indexed, and persistently accessible, making digital footprint reduction a board-level priority.

1. Audit Social Media Profiles to Cut Off Deepfake Source Material

Every platform an employee uses publicly is a potential data source for a deepfake attacker. LinkedIn profile photos, TikTok videos, Instagram reels, and Facebook posts all contribute audio and visual material that off-the-shelf AI tools can process in minutes. Set profiles to friends only, or equivalent private modes, disable content downloads where available, and audit tagged posts and videos.

2. Scrub OSINT Exposure by Removing Data from Broker Sites

Open-source intelligence (OSINT), the practice of aggregating publicly available data to profile a target, is the first step attackers take before building a deepfake.

Home addresses, phone numbers, workplace history, and family connections scraped from aggregator sites such as Spokeo, Whitepages, and BeenVerified provide attackers with the personalization layer that makes a deepfake pretext believable.

Data broker removal services such as DeleteMe or Privacy Bee automate opt-out requests across hundreds of sites. Google also accepts requests to blur home addresses in Street View as an additional precaution.

3. Control Public Audio and Video to Limit Voice Cloning Risk

The same OSINT exposure that feeds deepfake creation directly fuels spear phishing personalization. A tighter digital footprint degrades both attack vectors simultaneously.

Avoid posting unscripted video or audio content publicly; watermark any audio or video that must remain public; and establish organizational policies that restrict executives from sharing candid recordings on public channels.

A controlled digital footprint removes the raw material attackers depend on, but it does not eliminate the need to verify that the person on the other end of a call or video is who they claim to be.

Step 2: Establish Verification Protocols and Phishing-Resistant MFA

Defending against deepfake-enabled fraud requires two reinforcing layers: out-of-band identity verification for high-stakes communications and phishing-resistant multi-factor authentication (MFA) for system access.

Both layers target the same gap: the moment an attacker impersonates a trusted person to trigger a financial transfer, credential reset, or system access event. The highest-risk workflows, financial authorization, executive communication, and HR onboarding each require specific protocols, not just general awareness.

1. Use Out-of-Band Verification to Stop Deepfake-Enabled Wire Fraud

When a CFO calls to approve a wire transfer or a CISO requests emergency system access, the channel used to carry the request cannot be the same channel used to verify it.

Out-of-band verification means hanging up and redialing the requester's known, independently verified number. Never use a callback number provided during the inbound call itself.

Organizations should also establish a code word system: a pre-agreed, private phrase shared between trusted colleagues, exchanged verbally before any urgent financial or credential-related request proceeds.

The phrase must never appear in writing or in any system the attacker may have already compromised. A synthetic voice can replicate tone and cadence, but it cannot produce a secret phrase it was never trained on.

2. Deploy FIDO2 Hardware Security Keys to Block Deepfake Authentication Bypass

Deepfakes do not just impersonate colleagues on calls. They bypass authentication systems built around knowledge-based questions and certain biometric checks. A synthetic face can defeat facial recognition that lacks depth sensing; a cloned voice can fool voice authentication that relies on acoustic pattern matching alone.

Liveness detection closes part of this gap. It is a biometric security mechanism that requires real-time proof of physical presence, random head movements, blinking sequences, or depth-map analysis to distinguish a live person from a deepfake replay or a photo-injection attack. Liveness detection still operates on a single channel, which limits its protection.

The most phishing- and deepfake-resistant authentication method available is a hardware security key using FIDO2/WebAuthn. These keys perform a cryptographic handshake bound to the specific device and registered domain. CISA's phishing-resistant MFA guidance classifies FIDO2/WebAuthn as the highest-assurance authenticator class because the private key never leaves the hardware token and cannot be intercepted, replayed, or socially engineered. No deepfake can produce the physical key.

3. Apply Deepfake Verification Protocols to HR and Remote Onboarding

The verification gap extends well beyond finance. HR teams conducting remote video interviews now face a documented threat: candidates submitting AI-generated deepfake videos to pass screening calls with fabricated identities.

HR teams need the same out-of-band verification logic applied to hiring: government ID verification cross-referenced against liveness-checked video, with code-word confirmation steps before any system access is provisioned to a new hire.

Organizations that treat onboarding as a lower-risk workflow than financial authorization leave an unguarded entry point into their human layer. Building phishing simulation scenarios into employee training, including HR-specific deepfake interview drills, ensures the team most likely to face this threat has practiced the response before it arrives.

Step 3: How to Spot a Deepfake: Visual, Audio, and Behavioral Detection Signals

Knowing how to protect against AI deepfakes starts with training employees' eyes, ears, and judgment to catch what attackers want missed. Scan for visual artifacts at the face boundary and in the hair, listen for robotic cadence or unnatural pauses in voice, and treat any sudden pressure to act as a signal to pause rather than comply.

Behavioral verification, confirming high-stakes requests through a separate, trusted channel, closes the gap that technical detection tools alone cannot cover.

1. Identify Visual Artifacts That Expose an AI-Generated Deepfake

Current deepfake generation technology produces consistent failure points. Unnatural blinking patterns, either too infrequent or mechanically timed, remain one of the most reliable giveaways in video deepfakes. Rendering engines also struggle with hair edges, ears, and the boundary where a synthesized face meets an unchanged background, producing subtle warping or pixelation that shifts during head movement.

Watch for lip-sync errors that appear specifically on hard consonants; where mouth movement visibly lags or misaligns with the audio. Skin texture inconsistencies, patches of unusual smoothness against realistic background detail, and frozen or glitchy frames during rapid movement signal synthetic generation rather than a live recording.

2. Recognize Voice Cloning Tells in AI-Generated Audio

AI-cloned voice often loses the micro-variations that define natural human speech. Robotic cadence, unnaturally flat pitch, and pauses inserted at grammatically correct but contextually odd moments are the clearest warning signs. Background audio inconsistencies, a sudden shift in ambient noise or an abrupt silence mid-sentence, indicate a spliced or synthesized clip rather than a continuous recording.

3. Use Behavioral Context to Catch What Detection Tools Miss

Visual and audio signals matter less than behavioral context in high-pressure scenarios. Deepfakes exploit two simultaneous pressures: authority bias (the psychological tendency to comply with perceived authority figures) and artificial urgency, which compresses the window for critical thinking.

A finance employee who receives an executive voice call demanding a same-day wire transfer faces two simultaneous psychological pressures: fear of disappointing a superior and fear of causing a deal to collapse.

The practical countermeasure is to treat urgency itself as a red flag. When a message creates immediate pressure, that pressure is the signal to slow down, not comply. Standard verification through a confirmed secondary channel, a phone number stored in the company directory and not one provided in the message, takes under two minutes and defeats the majority of deepfake phishing simulations that rely entirely on bypassing that one step.

4. Why AI Deepfake Detection Tools Cannot Be the Only Defense

Automated deepfake detection tools are improving, but their real-world accuracy remains inconsistent.

A Reuters Institute for the Study of Journalism study from early 2024 first documented these inconsistencies; a 2025 benchmark evaluation ("Fit for Purpose? Deepfake Detection in the Real World," arXiv, October 2025) confirmed that both academic and commercial detectors continue to produce meaningful false positive and false negative rates when tested on real-world content, including compressed or re-encoded media.

Organizations that rely solely on detection software, without trained employees applying deliberate cognitive checks, remain exposed whenever an attacker's generation method outpaces the detector's training data.

The most durable defense combines visual literacy, audio awareness, and behavioral verification protocols. These skills hold regardless of how technology evolves, and they feed directly into the organizational controls that limit the digital footprint attackers rely on to build convincing deepfakes in the first place.

Step 4: Build Organizational Defenses Against AI Deepfake Attacks

Protecting against AI deepfakes at the organizational level requires a structured sequence of interconnected actions: threat landscape assessment, incident response planning, brand protection, content provenance tooling, and compliance alignment. Each layer addresses a distinct attack surface. Technology controls reduce exposure but cannot eliminate it. The organizations that close the gap invest in human readiness alongside technical measures.

1. Run a Deepfake Threat Assessment Mapped to High-Risk Roles

Deepfake risk concentrates in specific roles, workflows, and data assets. CFOs and finance teams face wire fraud via fake executive video calls. Healthcare organizations must account for patient-identifiable deepfakes that could expose patients to HIPAA violations.

Technology companies face executive impersonation during negotiations, where a synthetic video of a CEO can manipulate deal terms or leak confidential strategy.

Map the highest-risk roles first: CEO, CFO, HR, legal, and anyone with wire transfer authority. Cross-reference those roles with realistic attack scenarios. That inventory becomes the foundation for every subsequent defensive decision.

2. Define a Deepfake Incident Response Plan Before an Attack Lands

Assign ownership before an incident occurs. Designate a response lead with authority to pause financial transactions, a communications lead for reputational deepfakes (which often require a public relations firm with crisis-management experience), and a legal lead to manage evidence preservation and coordinate with law enforcement.

When a deepfake surfaces, immediate containment steps include preserving the original content as forensic evidence, notifying affected parties, issuing takedown requests to the hosting platform's trust-and-safety team, and filing a report with the FBI IC3 if financial fraud is involved.

Escalation paths matter as much as containment steps. Platform trust-and-safety teams at LinkedIn, YouTube, and Meta each maintain formal channels for reporting synthetic media abuse. Engage them in parallel with the internal response. Delays in platform takedowns extend reputational damage.

3. Protect Executive Brand Integrity Against Synthetic Media Fraud

Deepfakes targeting brands typically appear as fraudulent advertising, synthetic videos of executives endorsing cryptocurrency schemes, fake product announcements, or counterfeit partnership claims.

Monitoring services that scan social platforms and ad networks for unauthorized executive likenesses provide early detection. When fraudulent content is confirmed, DMCA takedown procedures apply to infringing use of copyrighted footage, and platform reporting mechanisms handle synthetic media violations separately.

Designate a brand protection owner who receives escalations from monitoring tools and has authority to initiate takedown actions without waiting for committee approval.

4. Use C2PA Content Provenance Standards to Authenticate Media

The Coalition for Content Provenance and Authenticity (C2PA) has developed an open technical standard for embedding cryptographically signed metadata, called Content Credentials, directly into digital media at the point of creation.

A January 2025 joint advisory from the NSA, NCSC-UK, ACSC, and the Canadian Centre for Cyber Security endorsed Content Credentials as the most viable current standard for verifying media provenance and recommended that organizations begin implementation now. The advisory notes that the C2PA specification is being fast-tracked toward ISO 22144 recognition. Adopters already include Adobe, OpenAI, Microsoft, LinkedIn, Google, and the BBC.

Content Credentials are an emerging standard; adoption is growing but not yet universal. Organizations cannot rely on their absence alone to flag synthetic content as fraudulent. Use provenance tools as one signal in a multi-layered verification process, not as a binary trust indicator.

5. Map Deepfake Incidents to GDPR, HIPAA, and SOX Compliance Obligations

Deepfake incidents trigger obligations across multiple regulatory frameworks. GDPR requires notification when personal data is used to create synthetic media without the subject's consent, a requirement that applies to virtually every executive impersonation cyberattack.

HIPAA obligations activate when deepfake content involves patient-identifiable information. SOX applies when synthetic media is used to manipulate financial communications, including earnings calls, board presentations, or investor-facing video content.

These are risk management requirements, not compliance checkboxes. An organization that experiences a deepfake-enabled wire transfer fraud and cannot demonstrate reasonable controls will face regulatory scrutiny that compounds the financial loss.

Build incident documentation and response workflows to meet these frameworks before testing. No technology can fully close this gap. Deepfake awareness training is the variable that determines whether employees apply verification protocols when the controls matter most.

Step 5: Train Employees to Recognize and Resist Deepfake Social Engineering Attacks

Deepfake awareness training starts with building employees who can identify synthetic audio and video before they act on it. Effective programs combine artifact recognition, attack-context education, verification protocol drills, and psychological pressure resistance, skills traditional phishing awareness programs were never designed to develop.

Deepfake phishing simulations, not explainer videos, produce measurable improvements in detection. Executives represent the sharpest vulnerability: they are the most impersonated personas in deepfake fraud and consistently the least likely to receive targeted training.

1. Why Deepfake Awareness Training Requires a Different Approach Than Phishing

Traditional phishing awareness teaches employees to scrutinize written text, sender addresses, grammar anomalies, and mismatched URLs. Deepfake attacks bypass that entire skill set by replacing a suspicious email with a convincing voice call or a video message that appears to show a known, trusted person speaking directly to the target. The written cues employees have been trained to catch simply don't exist in a synthetic video.

2. Build Deepfake Security Awareness Training Around Four Core Skills

Deepfake awareness training covers four specific skill areas that traditional programs omit entirely.

• Artifact recognition: Employees learn to identify visual and audio anomalies, unnatural blinking patterns, lip-sync lag, audio compression artifacts, inconsistent lighting on a face, or the subtle flattening of voice emotion that AI cloning still produces.
• Attack context awareness: Training covers scenarios in which deepfakes appear: wire fraud authorization calls, vishing requests from impersonated executives, urgent video messages demanding credential resets, and business email compromise (BEC) sequences in which an email is followed by a confirming voice call.
• Verification under pressure: Employees practice interrupting high-urgency requests to confirm through a second trusted channel, even when the voice or face on screen belongs to their CEO.
• Pressure resistance: Urgency and authority are the two psychological triggers deepfakes exploit. Employees rehearse recognizing when those triggers are activated and pausing rather than complying.

3. Run Deepfake Simulations to Build Positive Behavior

Static training modules that explain deepfakes produce familiarity with the concept, not the behavioral muscle memory to apply that knowledge under pressure.

Deepfake simulations place employees inside a controlled attack scenario: a vishing call using an AI-cloned executive voice, or a video message from a synthetic version of a known leader requesting urgent action. Employees who experience that scenario in a safe environment build the pattern recognition needed to catch the real thing.

Simulations that include deepfake voice and video components are the training format most closely mirroring the actual psychological conditions of a cyberattack, the authority cue, the time pressure, and the sensory credibility of a familiar face or voice.

Red team deepfake exercises extend this further: security teams run simulated deepfake campaigns targeting finance, IT, and executive assistants to identify which roles and individuals comply before a real attacker does.

4. Prioritize Executives: The Most Impersonated Targets Who Receive the Least Training

Executives are simultaneously deepfake attackers' most valuable targets for impersonation and the employees most likely to be excluded from security awareness training rollouts.

Their public-facing audio and video, earnings calls, conference keynotes, and media interviews provide attackers with ample source material to build convincing voice and video clones. Yet most programs treat executives as sponsors rather than participants.

Executive deepfake training must cover both sides of the threat: not just being impersonated, but also being targeted by impersonation attempts from attackers posing as board members, regulators, or counterparts at partner organizations. Dedicated sessions walk executives through the verification protocols they must apply when receiving urgent requests by phone or video, regardless of how legitimate the caller appears.

That exposure grows in direct proportion to how much of an executive's voice and image is publicly accessible, underscoring why digital footprint management and employee training must be treated as two sides of the same defensive posture.

Step 6: How to Respond to a Deepfake Attack in the First 24 Hours

When a deepfake cyberattack lands, the first 24 hours determine how much damage compounds. Individuals must document before acting, then escalate through platform reporting channels, law enforcement, and legal counsel in parallel.

Organizations need a parallel track: activate the incident response plan, lock down the narrative internally, and pursue platform takedown simultaneously. The legal landscape is evolving quickly; deepfakes are now criminalized in many contexts, but determining which laws apply requires expert guidance.

1. Preserve Forensic Evidence Before Reporting or Removing Deepfake Content

Before reporting to any platform or authority, preserve the evidence. Take a screenshot of the deepfake content, record the URLs, download the metadata, and note the time and date of discovery. Taking removal action before documentation destroys the chain of custody needed for legal proceedings and forensic analysis.

2. Report to Platform Trust-and-Safety Teams to Accelerate Deepfake Takedown

Meta, TikTok, and YouTube all maintain specific policies against non-consensual synthetic media and provide dedicated reporting tools. Platform trust-and-safety teams hold the fastest lever for content removal. Use it in parallel with every other step, not after.

3. File an FBI IC3 Report for Deepfake-Enabled Financial Fraud

For financially motivated deepfakes, file a complaint at ic3.gov, the FBI's Internet Crime Complaint Center. The FBI IC3 Annual Report, 2025, documented losses exceeding $17 billion from cyber-enabled crimes.

4. Consult Legal Counsel on State and Federal Deepfake Laws

The National Conference of State Legislatures tracked active deepfake legislation across multiple U.S. states covering non-consensual intimate imagery, election interference, and financial fraud. The legal framework is evolving rapidly. An attorney familiar with state-specific statutes can identify criminal charges, civil remedies, and preservation obligations that apply to the case.

5. Activate the Incident Response Plan and Notify Stakeholders

Organizations must treat a deepfake incident as a formal security event. Activate the incident response plan, notify affected internal stakeholders, brief the board and communications team on the scope, and engage outside counsel to assess any regulatory notification obligations for affected external parties.

How Deepfake Simulation Fits Into a Human Risk Management Program

Protecting against AI deepfakes is not a standalone initiative. It is one behavioral risk vector within a broader human risk management (HRM) framework, the practice of measuring, monitoring, and reducing the probability that an employee will take an unsafe action, including falling for a deepfake social engineering cyberattack.

Why Simulation-Based Deepfake Training Outperforms Completion-Based Programs

Multi-channel phishing simulation programs, covering email, voice, SMS, and deepfake video, generate behavioral data that completion-based training cannot capture: who responded to a simulated deepfake vishing call, who verified before complying, and who escalated the request.

These behavioral signals allow security teams to identify which employees and departments carry the highest residual exposure to deepfake-style attacks and direct targeted intervention accordingly.

How to Integrate Deepfake Awareness Into a Continuous Security Training Program

Deepfake awareness training reaches its ceiling when deployed as a one-time annual module. Integrated programs map deepfake scenarios to phishing simulations across all channels, automatically trigger microlearning when an employee fails a simulation, and deliver role-specific content to finance and executive teams, who face the highest impersonation risk.

Organizations subject to SOC 2, HIPAA, GDPR, and PCI DSS must demonstrate that employee training addresses AI-era threat vectors; training content mapped to these frameworks must cover deepfake and voice-cloning scenarios, not just traditional phishing. Continuous risk scoring, rather than static completion logs, is what separates a program that satisfies an auditor from one that actually changes behavior.

Frequently Asked Questions: How to Protect Against Deepfakes

How Much Data Does an Attacker Need to Create a Convincing AI Deepfake?

Modern voice-cloning tools can produce a convincing AI deepfake of a person's voice from as little as three seconds of audio. Video deepfakes require more material but are achievable with a few dozen publicly available photos or a short video clip.

The barrier dropped sharply with diffusion-model and GAN-based synthesis tools, many of which are free or low-cost. According to "Deepfakes and the Crisis of Knowing" (UNESCO, 2025), voice-cloning AI enables attackers to mimic a target's voice using seconds of audio. That means any executive with a recorded earnings call, public interview, or LinkedIn video has already provided enough raw material for a credible impersonation attack.

Can AI Deepfake Detection Tools Reliably Identify Synthetic Media?

AI deepfake detection tools are improving but are not reliable enough to serve as a standalone defense. Researchers at the Reuters Institute for the Study of Journalism first documented significant accuracy gaps in early 2024.

The U.S. GAO similarly concluded that detection technology requires continued development and should not be the sole control. Procedural verification, such as call-back confirmation, code words, and dual-approval workflows, remains the more reliable countermeasure in high-stakes situations.

What to Do Immediately After Discovering a Deepfake Attack Targeting the Organization

Stop all financial transactions or access grants tied to the suspicious request before taking any other action. Document and preserve evidence, including screenshots, call recordings, and message logs, before attempting to remove any content, as deletion can compromise forensic and legal options.

For individuals:

• Report to the platform hosting the content (Meta, YouTube, and TikTok each maintain deepfake-specific takedown policies)
• File a report with the FBI Internet Crime Complaint Center (IC3) for financially motivated deepfake attacks
• Consult legal counsel regarding applicable state deepfake statutes

For organizations:

• Activate the incident response plan and notify affected internal stakeholders immediately
• Engage platform trust-and-safety teams for priority content removal
• Preserve the chain of custody for all evidence before any remediation steps
• Brief legal, communications, and board leadership in parallel

Speed matters most in the first hour. Contain the financial exposure, preserve evidence, and escalate to the right channels simultaneously.

Are AI Deepfakes Illegal? Laws That Apply to Deepfake Fraud and Impersonation

AI deepfakes used for fraud, financial impersonation, or non-consensual intimate imagery are illegal under existing federal and state laws in the United States, even without deepfake-specific statutes.

Wire fraud, identity theft, and impersonation laws all apply when synthetic media is used to obtain money or assets under false pretenses.

The legal landscape is evolving rapidly: the National Conference of State Legislatures tracks active deepfake legislation across states, with dozens of laws enacted targeting non-consensual intimate imagery, election interference, and financial fraud.

At the federal level, the FTC has proposed rules specifically addressing AI-enabled impersonation of individuals. No single comprehensive federal deepfake law exists as of this writing, meaning jurisdiction-specific legal advice is essential when a deepfake incident occurs.

Voice Deepfake vs. Video Deepfake: Risk Comparison and Defenses for Each

Voice-only deepfakes carry higher immediate financial risk for most organizations because they are faster to produce, harder to detect in real time, and the primary vector in vishing attacks that have driven large-scale wire fraud losses. Video deepfakes carry higher reputational and brand risk and require greater production effort, but tools are lowering that barrier. The defenses differ accordingly.

Against voice deepfakes (vishing): Implement callback verification to an independently verified number, establish pre-agreed code words for high-stakes voice requests, and require dual approval for any financial authorization initiated by phone. Train employees to treat urgency on a voice call as a trigger to slow down, not comply.

Against video deepfakes: Apply content provenance standards, such as C2PA Content Credentials, where available; use liveness detection in video-based authentication workflows; and verify video identities through a separate out-of-band channel before acting on any request.

Both attack types exploit the same psychological levers of authority, urgency, and fear of failure. Simulation-based training that exposes employees to realistic deepfake vishing calls and executive impersonation scenarios is the most direct way to build the recognition and verification habits that procedural policies alone cannot instill.

See How Adaptive Security Prepares the Team for Deepfake Vishing and Executive Impersonation

Knowing what a deepfake cyberattack looks like in theory is not the same as knowing how a team will respond under real pressure. Adaptive Security's Phishing Simulations deliver multichannel deepfake vishing and executive impersonation exercises built from the organization's actual OSINT profile. That means the workforce can encounter the cyberattack before a real attacker sends it. Walk through the platform to see how it works. Book a demo to understand more.