Imagine the scenario: an urgent phone call from a CFO requesting an immediate custom transfer to close a key confidential transaction. The voice sounds authentic, the context is believable, and the time pressure is immense. The transfer is executed. A few hours later, it turns out that the director never made the call, and the money is gone irretrievably. The voice that authorized the transaction was perfectly cloned by artificial intelligence based on a few seconds of footage from a publicly available interview. This is not the plot of a movie, it’s a real threat that companies are already facing today.
Generative artificial intelligence technologies, including deepfake, are democratizing access to tools that not long ago were reserved for intelligence agencies and major movie studios. Today, they are becoming weapons in the arsenal of cybercriminals, opening a new chapter in the history of social engineering. Attacks are becoming hyper-realistic, personalized and extremely difficult for an unprepared human to detect. This article explains how these technologies work, analyzes specific attack vectors against organizations, and presents a comprehensive defense strategy that will allow IT and security leaders to prepare their companies for this inevitable challenge.
Shortcuts
- What exactly is deepfake technology and how does it work?
- What are the most common uses of deepfake in cyberattacks on companies?
- How is deepfake audio revolutionizing vishing and BEC scams?
- What threats does deepfake video pose to a company’s reputation and its leaders?
- What are the technical limitations and weaknesses of deepfake technology that can be used for defense?
- What internal processes and procedures should be implemented to minimize the risk of AI-based fraud?
- How can nFlo help defend against AI and deepfake-based threats?
- What is the future of AI-based threats and how can organizations build long-term resilience?
What exactly is deepfake technology and how does it work?
Deepfake is a term formed from a combination of the words “deep learning” and “fake.” It refers to synthetic media - images, video or audio - in which a person on existing material is replaced or altered to look and sound like someone else. The technology is based on advanced artificial intelligence models, and is most often underpinned by so-called Generative Adversarial Networks (GANs).
The GAN model consists of two competing neural networks: Generator and Discriminator. The Generator is tasked with creating false data (e.g., a facial image) that is as similar as possible to real data. The Discriminator, in turn, learns to distinguish real data from that generated by the Generator. The two networks train each other in a loop: The Generator gets better and better at creating fakes to fool the Discriminator, and the Discriminator improves its detection skills to avoid being fooled. This “duel” leads to extremely realistic, synthetic content.
To create a deepfake video, a criminal needs source material (the face he wants to superimpose) and target material (the video on which the face will be superimposed). In the case of voice cloning, modern algorithms need only a few seconds of a sample of an authentic voice to generate any speech that sounds natural and convincing. It is this low barrier to entry and increasing quality that makes deepfake technology such a powerful and dangerous tool in the hands of cybercriminals.
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What are the most common uses of deepfake in cyberattacks on companies?
Cybercriminals are using deepfake technology to launch attacks that are far more sophisticated than traditional social engineering. One of the most common and dangerous uses is Business Email Compromise (BEC) scam version 2.0. Instead of relying on a fake email, criminals use a cloned voice (deepfake audio) to call a finance department employee to authorize an urgent transfer. The supervisor’s realistic voice, tone and manner of speaking can bypass any human verification mechanism based on trust.
The second key use is blackmail and reputational damage. Criminals can create compromising video or audio footage of key executives, threatening to publish it in exchange for ransom. Imagine a fake video in which the CEO admits to illegal practices or insults business partners. Even if the company eventually proves that the material is fake, the image damage and loss of investor confidence could be irreparable.
The third area is market manipulation and misinformation. A fake video statement by the CEO of a major listed company announcing alleged financial problems can cause market panic and a sharp drop in stock prices, which criminals playing the stock market can profit from. Deepfakes can also be used to impersonate business partners during videoconferences to phish for confidential business information or strategic data, opening up a whole new dimension of industrial espionage.
How is deepfake audio revolutionizing vishing and BEC scams?
Vishing (voice phishing) is a form of attack in which criminals use the telephone to extort information or solicit certain actions. For years, its effectiveness has been limited by the fact that the human ear can pick up attempts to imitate a voice. Voice cloning technology, or deepfake audio, completely eliminates this barrier. AI algorithms can analyze a person’s intonation, speech rate, accent and unique vocal characteristics, creating a model that can “speak” any sentence in their voice.
In the context of Business Email Compromise (BEC) scams, deepfake audio is the missing link that lends credibility to the fake email. The attack scenario often goes as follows: an employee in the accounting department receives an email from the supposed CEO requesting an urgent wire transfer. The employee, having learned the procedures, decides to call the CEO for verification. Criminals, who have previously taken control of the phone line (e.g., through SIM swapping) or simply call from another number, answer the call and respond with the cloned voice of the CEO, confirming the command. In this way, the verification mechanism is turned against the company.
The revolution lies in breaking down the psychological barrier. While it is possible to doubt the content of an e-mail, hearing the familiar voice of a superior on the phone builds almost absolute trust. This makes employees much more vulnerable to manipulation, especially under time pressure and in the atmosphere of confidentiality that attackers often create. Defending against such attacks requires moving away from simple verification methods to more formalized, multi-step procedures.
What threats does deepfake video pose to a company’s reputation and its leaders?
Deepfake video poses one of the most serious reputational threats, as it strikes at any organization’s most valuable asset - trust. Visual material is perceived as much more credible than text or audio. A realistic-looking video of a board member can go viral on social media within hours, causing a crisis from which it will be extremely difficult for a company to recover, even after the forgery is proven.
One key scenario is the fabrication of scandals. Criminals can create a video in which a CEO or other key manager makes racist comments, admits to bullying, or reveals confidential, negative information about a company’s products. Such material, published just before an important event (such as a product launch or the release of financial results), can devastate the image of the leader and the entire organization, scare off customers and discourage investors.
Another threat is corporate espionage and manipulation of business partners. Imagine a video conference with a potential partner, during which criminals impersonate our team using real-time deepfakes. They can thus sabotage negotiations or phish for sensitive business data. The threat also applies to internal processes. A fake video with a supposed order from a supervisor can be used to get an employee to share sensitive data or access passwords.
What are the technical limitations and weaknesses of deepfake technology that can be used for defense?
Although deepfake technology is advancing at an alarming rate, it still has some imperfections, knowledge of which can help identify counterfeits. Detecting these anomalies, however, requires great perceptiveness and awareness of what to look out for. In the case of video footage, one common artifact is unnatural eye movements and infrequent blinking. AI models often fail to perfectly simulate natural, random eye movements and blink frequency, which can be a warning sign.
Another weakness is inconsistent lighting and shadows. A face superimposed on video footage may have lighting that does not match the rest of the scene. Shadows on a face may fall at a different angle than on surrounding objects. It’s also worth paying attention to the edges of faces and hair - subtle blurring, flickering or unnatural transitions often appear in these areas, where the false image blends with the original background.
In the case of deepfakes generated in real time, such as during video conferencing, an unusual angle or partial covering of the face can sometimes be a problem for the algorithms. When a person turns sideways or obscures the face with the hand, the algorithm may have trouble rendering the image correctly, leading to visible distortion. It is also worth paying attention to the synchronization of lip movement with sound and the overall smoothness of facial expressions. Although the best models are getting better at this, cheaper and faster tools can still generate subtle delays or unnatural movements.
What internal processes and procedures should be implemented to minimize the risk of AI-based fraud?
Technology for detecting deepfakes is important, but a much more reliable line of defense is robust, formalized internal processes that minimize risks based on human error. The most important procedure is the introduction of mandatory “out-of-band verification” for any sensitive or unusual requests, especially those involving financial transfers, changing counterparty bank details or sharing confidential information.
“Off-channel” verification means confirming the request using a completely different, pre-established and trusted communication medium. If the request came by e-mail, verification should be done by phone - but not to the number provided in the e-mail, but to a known, internal number from the address book. If the request came during a phone call, it should be confirmed by sending a message on the company’s internal messenger or simply walking up to the person’s desk. Such a simple procedure thwarts most fraud attempts, as the criminal rarely has control over multiple channels of communication simultaneously.
Another key element is to implement the principle of multi-person approval for all financial transactions above a certain threshold. No single employee, regardless of position, should be able to approve a significant transfer alone. Requiring approval by at least two people from different departments makes it significantly more difficult to carry out fraud, even if one person is successfully manipulated. These processes must be clearly defined, communicated and ruthlessly enforced throughout the organization.
Pillar of DefenseKey ActionsMaturity Index (KPI)TechnologyImplement deepfake detection tools, strong multi-factor authentication (MFA), advanced email protection.Percentage of attack attempts detected and blocked, incident response time.Verification ProcessesMandatory “off-channel” verification for sensitive operations, multi-person authorization rule for transfers, regular process audits.Number of transactions retained for additional verification, compliance with procedures in internal audits.Employee AwarenessContinuous training on new social engineering techniques, regular advanced simulations (vishing, deepfake), clear incident reporting path.Percentage of employees reporting simulated attacks, drop in “click-through” rate in test campaigns.
How can nFlo help defend against AI and deepfake-based threats?
At nFlo, we understand that defending against next-generation threats such as deepfake requires an approach that goes beyond standard technology implementations. That’s why we base our strategy on three pillars: advanced testing, building human resilience, and creating robust processes. We help organizations prepare for these challenges by acting as a partner in building a comprehensive defense shield.
Our teams conduct advanced simulations of social engineering attacks that incorporate the latest techniques. Instead of standard phishing campaigns, we execute controlled vishing scenarios using voice cloning technology. Such a test in a secure environment identifies the weakest points in verification processes and makes employees aware of the reality of the threat in a way that no theoretical training can. Red Teaming services offered by nFlo allow comprehensive verification of an entire organization’s resilience to multi-stage, complex attacks.
We place great emphasis on building awareness and practical skills for employees. Our training programs are constantly updated with modules on disinformation and deepfake. We teach not only how to recognize technical artifacts of fake materials, but more importantly, how to apply the principle of limited trust and verification procedures. As part of our consulting services, such as virtual CISO (vCISO), we help managements develop and implement robust policies and procedures, such as “off-channel” verification and the principle of multi-person authorization, which are the most effective barriers to fraud.
We also support organizations in selecting and implementing technologies that support defense. We help implement modern identity protection solutions, including phishing-resistant multi-factor authentication (MFA) methods. Our security audits and architecture analysis identify vulnerabilities that could be exploited by criminals after a successful social engineering attack, strengthening the overall resilience of the infrastructure.
What is the future of AI-based threats and how can organizations build long-term resilience?
The future of artificial intelligence-based threats is a constant arms race. Generative models will become increasingly perfect, cheaper and more accessible, and counterfeit materials will become virtually impossible to distinguish with the “naked eye.” At the same time, detection technologies will develop, which also use AI to analyze subtle patterns and inconsistencies in digital data that are invisible to humans. Those organizations that understand that one side of this competition cannot be relied upon alone will win.
Long-term resilience will not be based on a magic tool that will detect every deepfake. It will be the result of a Zero Trust strategy (Never Trust, Always Verify), but extended from the network and device level to the level of human interactions. This means building an organizational culture in which asking for additional verification of an unusual command is not seen as a lack of trust in a superior, but as a sign of professionalism and concern for company security.
Organizations must invest in three key areas. First, in continuous education and adaptive training programs that evolve with threats. Second, in automating and strengthening verification processes so that they are least disruptive to employees, but still as effective as possible. Third, in building operational resilience, that is, preparing incident response plans that assume that a social engineering attack will eventually succeed, and focusing on minimizing its impact. In a world where one can no longer fully trust one’s own eyes and ears, trust must be shifted from perception to process.
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