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HomeCybersecurity 101
What Is a Deepfake?

What Is a Deepfake?

Written by: Lizzie Danielson

Published: 6/30/2025

Last updated: 5/7/2026

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Key Takeaways

  • Deepfakes are AI-generated videos, images, and audio clips that make people say and do things that never happened and they're convincing enough to fool most people.

  • Threat actors are already using deepfakes to commit fraud, steal credentials, and spread misinformation. This isn't a future threat, it's happening now.

  • Spotting a deepfake takes a critical eye. Unnatural movement, audio sync issues, and suspiciously provocative content are all red flags.

  • If you think you're being tricked, slow down. Verify through a separate channel before you act on anything.

  • 2026 is a turning point. Deepfake-as-a-service platforms have made this technology accessible to threat actors at every skill level and most organizations aren't ready.

What exactly is a deepfake?

A deepfake is a video, image, or audio clip created using artificial intelligence (AI) that makes a real person appear to say or do something they never did. These AI-generated fakes can be incredibly convincing and are increasingly used in scams, misinformation campaigns, and other forms of digital manipulation.

The word "deepfake" is a mashup of "deep learning" (a type of AI) and "fake" — which pretty much says it all.


Who created deepfake technology?

Deepfakes didn't appear overnight. They're the result of decades of academic research.

The roots go back to 1997 when researchers Christoph Bregler, Michele Covell, and Malcolm Slaney built a program called Video Rewrite that could sync new audio to existing facial footage. It was originally meant for movie dubbing, but it laid the groundwork for everything that came after.

The real turning point came in 2014 when machine learning researcher Ian Goodfellow — then a PhD student at the University of Montreal — introduced the Generative Adversarial Network (GAN). A GAN pits two AI systems against each other: one generates fake content and the other tries to detect it. The result is a system that keeps getting better at deception. Goodfellow's invention is widely credited as the foundation of modern deepfake technology.

The term "deepfake" itself entered public use in late 2017 when an anonymous Reddit user started sharing AI-manipulated videos and the open-source code behind them on GitHub. From there, apps and tools made the technology accessible to anyone — no technical background needed.

By 2025 "deepfake-as-a-service" platforms put this capability on-demand for threat actors worldwide. No expertise required.



How deepfakes work

At the core of deepfake technology is the GAN. Two AI systems run in a loop: one creates the fake content (the "generator") and the other evaluates whether it passes as real (the "discriminator"). The generator keeps improving until the output is convincing enough to fool the discriminator and ultimately a real person.

By processing large datasets of images, videos, and audio recordings of a target, the system learns to replicate their likeness, voice, and mannerisms with striking accuracy. Modern tools have simplified this dramatically. Today, someone with zero technical expertise can create a convincing deepfake using a smartphone app or a web-based service.


How to spot a deepfake

Detecting a deepfake isn't always easy, but there are signs something may be off.

Unnatural movements. Watch for stiff or robotic facial expressions, body language that doesn't feel quite right, and unusual blinking or eye movement. Real people blink regularly and naturally AI-generated faces may blink too much, too little, or not at all.

Visual artifacts. Look for blurry edges around the face, flickering, inconsistent lighting, or skin texture that looks oddly smooth especially near the hairline and ears. Pay close attention to accessories: reflections in glasses may not match the surrounding environment, and hats or jewelry may cast incorrect (or no) shadows.

Eye and facial detail inconsistencies. Deepfake eyes often lack depth or appear unusually still, without the subtle changes in pupil size or focus that occur naturally. Check whether eyebrow movement matches facial expressions. A smile with completely stationary eyebrows is a red flag. Also look for whether the apparent age of the skin matches the eyes and hair.

Audio irregularities. Pay attention to how well the voice syncs with lip movement. Mismatched timing, a robotic tone, or an unusual cadence are all red flags. Compare the voice to known recordings of the person if you can, altered voices often sound subdued or slightly off in tone.

The content seems designed to provoke. Deepfakes are often used to manufacture controversy or urgency. If a video shows someone saying something shocking, out of character, or demanding immediate action, treat it with skepticism and verify independently before sharing or acting.

Nobody else is reporting it. If a major news-worthy clip hasn't been picked up by credible outlets, that's a reason to question it.


See it in action: A real-world deepfake attack

Watch how convincing a deepfake can be — and what to look for.

https://youtube.com/shorts/ECDAlrVZO5s?si=yLFqX53JrAmvEZ6t




What to do if you think you're being tricked

Whether it's a voice call from your "CEO" demanding an urgent wire transfer, a video message from a colleague, or a viral clip of a public figure saying something alarming, here's what to do.

  1. Pause before you act. Deepfake attacks are built around urgency. If something feels rushed, that's your first warning sign.

  2. Verify through a separate channel. Call the person back on a known phone number. Send a separate email. Never verify using the same channel the suspicious message came from.

  3. Look for the telltale signs outlined above. Trust your instincts if something feels slightly off.

  4. Report it. If you suspect a deepfake is being used to commit fraud or impersonate someone, flag it to your IT or security team right away.

  5. Don't share it. Spreading an unverified deepfake can accelerate misinformation fast. Verify first.

Want to learn more? The Huntress _declassified webinar series tackles the most pressing cybersecurity threats including how deepfakes are being used against businesses right now.



Why are threat actors using deepfakes?

Deepfakes work because they exploit one of the most fundamental human instincts: we trust what we see and hear.

Social engineering and phishing. A fake video of a trusted contact asking you to click a link is far more convincing than a standard phishing email. Deepfakes make social engineering dramatically harder to catch.

Credential theft. Deepfakes can be used to bypass identity verification including voice authentication systems to gain unauthorized access to accounts and systems.

Reputation attacks. Organizations and public figures can be targeted with fabricated "evidence" of criminal behavior or controversial statements. It's costly and time-consuming to undo the damage even after the fake is exposed.

Misinformation and political manipulation. Deepfake-style technology has been used to spread false narratives during elections and conflicts, manufacturing fake speeches from world leaders and seeding confusion at critical moments.

As Ben Bernstein, technical account manager at Huntress, put it: "We can no longer trust voice authentication over the phone. I could effortlessly feed an interview or podcast into a deepfake AI solution, effectively training it to mimic a target's voice. Imagine that AI calling a helpdesk to reset a password or impersonating an executive to extract sensitive financial data. MSPs especially need to recognize this escalating threat and implement stronger verification methods."


Why deepfakes are a serious cybersecurity concern in 2026

Deepfakes aren't new, but 2026 marks a real turning point in how seriously every organization needs to take them.

Deepfake-as-a-service is mainstream. Ready-to-use platforms emerged widely in 2025 offering voice cloning, video impersonation, and persona simulation to anyone willing to pay. What once required a sophisticated attacker now requires only a credit card.

The attacks are working. Research shows nearly every other business globally reported a deepfake-related fraud incident in 2024. North America saw deepfake fraud grow by more than 1,700% between 2022 and 2023. The pace hasn't slowed.

Detection is still catching up. As detection tools improve, the technology generating deepfakes evolves to stay a step ahead. This arms race puts defenders at a disadvantage when verification isn't built into day-to-day operations.

Voice authentication can no longer be trusted. Many helpdesks, financial workflows, and identity verification systems were built on the assumption that a convincing voice is a trustworthy one. That assumption is now dangerously outdated.

The awareness gap is wide. Security experts are consistent on this point: most organizations aren't prepared for the sophistication of AI-powered deepfake attacks being actively deployed today.

How to combat deepfakes

We're publishing a full guide on detection and defense strategies soon. In the meantime, here's what to put in place right now.

  • Train your team. Security awareness training that covers deepfakes specifically, what they look like, how they're used in attacks, and how to respond is your most immediate line of defense.

  • Verify unusual requests through a second channel. Any instruction involving money, credentials, or sensitive data should always be confirmed independently before anyone acts on it.

  • Use multi-factor authentication (MFA). Even if threat actors impersonate someone convincingly, MFA creates a barrier they can't easily fake.

  • Build a culture of healthy skepticism. Teach your team to slow down, question urgency, and report suspicious communications without fear of embarrassment.

How Huntress helps protect against deepfakes

Deepfakes are a prime example of how emerging technology gets twisted into a cybersecurity problem. As these threats get more convincing and more accessible, organizations need more than awareness — they need the right tools, the right training, and expert eyes watching around the clock.

Huntress combines 24/7 SOC monitoring, Managed Security Awareness Training, and managed endpoint protection to help your team stay ahead of evolving threats — including the social engineering attacks that deepfakes power. From catching fraud attempts to preparing employees to recognize impersonation schemes, we help cut through the noise and protect what matters most.


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Security Awareness Training Episode
Security Awareness Training Episode

Deepfake

In this episode, the mayor of Sludge Springs, cooks up a deepfake to trick Curriculaville’s sanitation worker, Jacob, in hopes of sabotaging their clean town record.

This episode dives into how deepfakes are created, the risks they pose in daily life, and steps you can take to spot and protect against them. Will Jacob see through the scheme, or will AI win the day?

Huntress Managed SAT

Spear Phishing Simulation

Knowing what spear phishing is and actually spotting it in the moment are two different things. See how Huntress SAT closes that gap with a free simulation preview.

Watch the spear phishing simulation

FAQs about deepfakes

Deepfakes are AI-generated videos or audio clips that mimic a person’s likeness or voice to create fake yet convincing content. They’re a concern because they can be used for fraud, misinformation, and privacy violations, among other cybersecurity threats.

Cybercriminals use deepfakes for a variety of attacks, such as impersonating executives (CEO fraud) to approve fake wire transfers, spreading false information during critical events, or creating fake content for blackmail.

Look for signs like unnatural facial expressions or movements, audio and visual inconsistencies, mismatched lighting, or syncing issues between lip movement and sound. When content seems questionable, cross-check its credibility with reliable sources.

Yes, emerging AI detection tools analyze video and audio metadata to identify manipulation. Staying updated with these technologies can help you spot deepfake content more reliably.

Organizations should train employees on deepfake awareness, verify unusual requests through secondary channels, use authentication measures like MFA, and adopt endpoint protection tools to defend against threats associated with deepfakes.

Yes, deepfakes can enhance entertainment, provide cultural preservation, and streamline artistic or educational projects. The key is to use this technology ethically and responsibly to avoid harm.

High-value targets like corporations, political figures, and individuals with significant public profiles are at greater risk. However, deepfake scams can target anyone, so staying vigilant is critical for everyone.

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