It is no longer science fiction. The deepfake era is here. It is already inside our inboxes, meetings, video calls and social media feeds. You get an urgent video call from your boss. As part of an important project, you need to authorize several financial transactions totaling several million dollars. This project has been confidential up until now, though other colleagues are also on the call to talk through the details.
Everything seems legitimate, even though you had not heard about this project before, so you authorize the transactions. Only later do you learn that everyone on the video call was generated by AI. It sounds like far-fetched, but this is exactly what happened to UK engineering firm Arup in 2024, when an AI deepfake video call was used to steal $25 million from the company.
As this incident shows, deepfakes are being used for much more than social media videos. All too often, they are being used to engineer highly sophisticated attacks. These innovative threats require an innovative response. The question is no longer if we need real-time defense against deepfakes but rather how fast we can build it and who is building it best.
How Serious Is The Risk?
Arup’s $25 million loss is just one example of the type of damage that can be caused by deepfakes. As profiled by IBM, in addition to other financial attacks, deepfakes have been used to fabricate clips to destroy individual reputations, create fabricated Zoom calls as a means of sending malware and in cyberbullying.
Deepfakes do not just represent a financial risk to individuals and companies. In many cases, they are being used to harass others and spread disinformation. As the tools used to create deepfakes become more advanced, it will become even harder to separate them from reality.
The old security paradigm of firewalls, passwords and multi-factor authentication is increasingly insufficient. Those tools protect access to a system, but they do not verify the authenticity of the human using it. The new security layer must be able to analyze reality itself.
This is where AI-powered defense comes in. These systems are not just looking for the glitchy, uncanny valley artifacts of early deepfakes. They are “digital bloodhounds,” trained to spot the microscopic impossibilities that human eyes and ears miss.
Getting Defensive: Fighting Fire With Fire
As deepfakes and synthetic IDs proliferate, continuous verification for agents and humans is becoming even more essential. There are several AI-powered solutions that are already being put into place to prevent deepfake attacks from succeeding. Especially for financial transactions, biometric verification tools offer an extra layer of security beyond typical multi-factor authentication.
Examples include:
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Liveness detection: AI systems analyze selfies or videos for actual signs of life, such as physical presence, behavioral cues and micro-expressions to identify whether a user is a real person or a deepfake. Companies like Intel have demonstrated this brilliantly with their FakeCatcher technology. It analyzes the pixels on the face to detect the subtle, involuntary flushing of skin that occurs with real human blood flow. An AI-generated avatar does not have a pulse.
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Real-time identity verification: This defensive mechanism often builds on liveness detection tech with additional verification steps. These may include having the user submit personal information in an online portal, document verification and cross-referencing other databases.
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Voice Analysis: For call centers and audio-only channels, this is paramount. A deepfaked voice can flawlessly mimic a CEO’s tone and cadence to authorize a fraudulent wire transfer. Companies like Pindrop use AI to analyze over 1,400 audio features. Pindrop analyzes not just what is said, but the acoustic “fingerprint” of the device, the network and the subtle “liveness” cues in a real human voice box that AI models fail to replicate.
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Behavioral analysis: AI can even detect fraud based on user behaviors, benchmarking activities like typing speed or using copy and paste actions against human interactions.
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Adaptive authentication: Layered security systems can assess a wide range of risk factors to require additional methods of authentication. Analyzing a user’s IP address and device type could signal whether they are legitimate or not, and additional security requirements would be layered on as needed.
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Platform Integration: The most critical development is embedding this detection inside the platforms we use. A company called Reality Defender, for example, provides an API that can integrate directly into platforms like Zoom or Microsoft Teams. It runs in the background of a video call, providing a real-time “veracity score” that can alert a participant — or automatically terminate a session — if it detects a synthetic participant. This is the direct antidote to the Arup attack.
Of course, ensuring that these verification systems do not disrupt the user experience is essential. As Vouched highlights, the company’s system identity verification solutions quickly scan IDs, conduct PII risk assessments during biometric verification and run liveness checks on customer selfies to streamline verification. In a Vouched case study for motorcycle-sharing service Riders Share, for example, switching from a manual to an automated system reduced verification time by 99%, while also reducing motorcycle theft by $1 million.
As the case study explains, “The elimination of manual verification tasks reduced the time spent per verification from up to six hours to less than one minute, allowing Riders Share to scale its operations efficiently. Streamlining the verification procedure also translated into cost savings by reducing labor expenses associated with manual verification and minimizing the administrative burden on staff.”
Act Now For The Best Defense
We are in an era where seeing is no longer believing. Real-time defenses against deepfakes are not only a technological imperative but also a strategic one. Similar to other innovations brought about by AI, any organization’s (or individual’s) ability to combat deepfakes with AI tools ultimately depends on their own knowledge. Human error has been tied to as many as 95% of cybersecurity breaches and deepfake attacks often seek to use many of the same weaknesses that contribute to other security problems.
Organizations that invest now in AI-powered defenses will not just avoid disaster. They will earn the trust required to lead in the next chapter of the digital economy — a chapter authored not just by what is possible with AI, but by what is provable. The deepfake era has begun. It is up to us to decide whether we meet it with confusion or with clarity and control.
Originally published on Forbes. For more details, visit the source.