Deepfake Detection Guide

A deepfake (a portmanteau of "deep learning" and "fake") is a video, image, or audio clip generated or manipulated using AI tools to make a person appear to say or do something they did not. The term combines "deep learning" and "fake." Modern deepfakes can be highly convincing, particularly in compressed video or low-bandwidth call environments where quality artifacts are harder to spot.

According to a Deloitte poll, nearly 26% of executives reported their company experienced at least one deepfake incident targeting financial or accounting data in a 12-month period. Incidents are underreported due to reputational concerns, so actual prevalence is likely higher. High-profile cases involving Arup, WPP, and LastPass demonstrate that no industry or company size is immune.

No. Detection rates depend on the quality of the deepfake, the viewing conditions, and the observer's experience. Low-resolution video calls, compressed recordings, and high-quality synthesis tools all make visual detection less reliable. Manual observation should be combined with automated tools and process controls rather than treated as a standalone defense.

Out-of-band verification means confirming a request through a separate, pre-established channel rather than continuing on the channel where the suspicious request arrived. If a video call asks you to initiate a wire transfer, out-of-band verification means hanging up and calling the requester back using a number from your internal directory, not one provided during the call.

Don't engage further with the suspicious request. Report it immediately through your company's incident response channel. If a financial transaction is already in motion, contact your financial institution as quickly as possible since speed is critical for reversing fraudulent transfers. Document what you observed, including the platform used, the nature of the request, and any anomalies you noticed.

Coverage varies significantly by policy and insurer. Some cyber policies insurance cover social engineering fraud, which can include deepfake-enabled wire transfer fraud, while others exclude it or require a separate endorsement. Review your policy with your broker and confirm whether social engineering coverage applies and what documentation requirements exist for a claim. A risk assessment can help identify gaps in your current coverage and controls.

Yes. Hackers can use AI-generated video and voice during live meetings to impersonate executives, vendors, coworkers, and clients. Some attacks use pre-rendered synthetic video, while more advanced attacks use real-time face and voice manipulation during active calls.

Video conferencing platforms themselves are not necessarily compromised. Instead, attackers abuse the trust people place in seeing and hearing familiar individuals during meetings. Businesses should treat video calls as one factor of identity verification, not proof of identity on their own.

Often, yes. Many modern BEC attacks now include AI-generated audio, video, or text impersonation as part of a broader fraud campaign. Attackers may begin with phishing emails, then escalate to voice calls, messaging apps, or video meetings to pressure employees into approving payments or sharing sensitive information.

Security teams increasingly view deepfake fraud as an evolution of social engineering rather than a completely separate threat category.

In some cases, yes. AI-generated voices have been used to target call centers, password recovery workflows, and voice authentication systems. While many biometric platforms include liveness detection and anti-spoofing controls, no system is perfect.

Companies should avoid relying solely on voice recognition for high-risk account recovery or financial authorization processes.

The most effective defense combines employee training, identity verification procedures, financial approval controls, and detection technology. Key protections include multi-person approval for wire transfers, out-of-band verification for sensitive requests, phishing-resistant MFA, and regular social engineering awareness training.

Businesses should also establish incident response procedures specifically for impersonation attacks and synthetic media fraud scenarios.

Legality depends on how the deepfake is used. Some uses of synthetic media are lawful, including entertainment, parody, accessibility, and creative applications. However, deepfakes used for fraud, harassment, election interference, identity theft, extortion, or non-consensual intimate imagery may violate federal or state laws.

Synthetic media refers to digital content generated or modified using artificial intelligence. This includes AI-generated images, video, audio, text, and avatars. Deepfakes are one category of synthetic media focused on realistic impersonation or manipulation of people.

Synthetic media can be used legitimately for entertainment, accessibility, marketing, and creative applications, but it can also be abused for fraud, misinformation, and impersonation attacks.

Yes. Many modern voice-cloning tools can generate convincing synthetic speech using only short audio samples taken from social media videos, podcasts, webinars, interviews, or voicemail greetings. Public-facing executives and sales staff are especially exposed because hackers can often find hours of recorded speech online.

Businesses should assume publicly available audio can potentially be used for impersonation attempts and build verification controls accordingly.

Regulatory requirements are evolving rapidly. Businesses should monitor state disclosure laws, privacy regulations, and platform liability requirements related to AI-generated content.

A voice cloning scam uses AI-generated speech to impersonate a trusted individual such as an executive, employee, family member, vendor, or financial institution representative. Hackers use cloned voices during phone calls, voicemails, or video meetings to pressure victims into transferring money, revealing credentials, or bypassing security procedures.

These scams often rely on urgency, secrecy, and emotional pressure rather than technical sophistication alone.

Yes. Businesses should establish formal procedures for verifying sensitive requests, reporting suspected impersonation attempts, preserving evidence, and escalating potential fraud incidents. Policies should also define who is authorized to approve payments, change banking information, or release sensitive data.

Yes. Small and midsize businesses are increasingly targeted because attackers often assume they have less formal approval processes and fewer dedicated security controls. A small finance team, limited segregation of duties, or informal communication culture can make impersonation attacks easier to execute successfully.

Deepfake fraud is no longer limited to large enterprises or public companies.

Some deepfake systems are capable of challenging basic identity verification workflows, particularly those relying only on static facial matching or low-friction selfie checks. Many modern verification platforms now include liveness detection, device intelligence, and behavioral analysis to reduce spoofing risk.

Companies handling regulated data or financial transactions should evaluate whether their identity verification providers include anti-deepfake protections.

Common warning signs include unusual urgency, requests for secrecy, pressure to bypass standard procedures, communication from unfamiliar numbers or accounts, requests to move conversations to personal platforms, and financial instructions that differ from normal workflows.

Technical anomalies such as unnatural speech rhythm, inconsistent lighting, lip-sync issues, or visual artifacts may also appear, but behavioral indicators are often more reliable than media quality alone.

Yes. Some advanced tools can manipulate facial appearance and voice output during live video or audio conversations in near real time. These systems are becoming more accessible as generative AI technology improves and hardware requirements decrease.

Businesses should assume that live communication channels can potentially be spoofed and should maintain verification procedures for high-risk requests.

Yes. Executives are common targets because their authority can override normal skepticism during financial or operational requests. Public appearances, conference presentations, interviews, earnings calls, podcasts, and social media videos also provide attackers with training material for voice and facial cloning systems.

CEO fraud and executive impersonation attacks increasingly incorporate AI-generated media to appear more convincing.

Yes. Some attackers use AI-generated video or manipulated identities during remote job interviews to conceal their real identity, location, or qualifications. Organizations have also reported cases involving synthetic resumes, AI-assisted interview responses, and impersonated candidates attempting to gain access to corporate systems.

Businesses should strengthen remote hiring verification procedures, especially for sensitive technical or privileged-access roles.

A documented response process helps reduce confusion during high-pressure incidents and improves consistency across departments.

Many deepfake attacks begin with traditional social engineering techniques such as phishing emails, credential theft, social media research, or business email compromise. Attackers gather information about organizational structure, communication habits, vendors, and executive behavior before launching impersonation attempts.

The AI-generated media is often only one part of a larger fraud campaign designed to build credibility and urgency.

Many deepfake attacks begin with traditional social engineering techniques such as phishing emails, credential theft, social media research, or business email compromise. Attackers gather information about organizational structure, communication habits, vendors, and executive behavior before launching impersonation attempts.

The AI-generated media is often only one part of a larger fraud campaign designed to build credibility and urgency.

Yes. Low-quality recordings, compression artifacts, poor lighting, unstable internet connections, and background noise can sometimes trigger false positives in automated detection systems. Human review and contextual verification remain important even when detection tools are deployed.

Organizations should treat detection scores as indicators of risk rather than definitive proof of manipulation.

Finance teams should pause the transaction and verify the request using a separate trusted communication channel before proceeding. Employees should never rely solely on email, voice calls, or video meetings for high-risk financial approvals.

Businesses should document escalation procedures in advance so employees know exactly who to contact and what verification steps are required during suspected fraud attempts.

Yes. While phishing-resistant MFA does not directly detect deepfakes, it helps reduce account compromise that often supports impersonation campaigns. Attackers frequently combine deepfake tactics with stolen credentials, session hijacking, or business email compromise.

Hardware security keys, passkeys, and FIDO2-based authentication are generally stronger protections than SMS-based MFA for high-risk accounts. Learn more about passwordless authentication.

Deepfake attacks are difficult to stop because they exploit human trust rather than only technical vulnerabilities. Attackers combine realistic AI-generated media with urgency, authority, emotional pressure, and compromised communication channels to influence employee behavior.

Detection technology continues to improve, but organizational controls and verification culture remain essential because no single technical solution can fully eliminate impersonation risk. Companies should regularly test executive impersonation and wire fraud scenarios through cybersecurity tabletop exercises.