There is no denying how advances in artificial intelligence have made our world a better place. From enhancing code quality to providing real-time data, AI is helping us identify heart disease earlier and detect climate disasters more accurately and sooner.
Furthering AI’s impact, generative AI (genAI) has been instrumental in creating novel content, such as assisting in the creation of synthetic medical data that can augment real-world datasets, leading to improved training of diagnostic tools while preserving patient privacy.
That’s not to say the rapid adoption and scale of this new frontier of technology has been exclusively for good. GenAI can generate false information, amplify biases, and compromise data privacy. As this technology becomes more accessible, it can also be used for malicious purposes. People face a greater risk than just financial fraud or a data breach, but identity theft and, ultimately, the loss of their personhood.
Forrester found that creating deepfakes is very easy to do—it only takes about 10 minutes and $10-20 to utilize online deepfake generation services, many of which don’t require coding. The analyst group also reported that people are becoming increasingly susceptible to being tricked by deepfakes. A recent study conducted found that when evaluating identity matching and naturalness, the average person is poorly equipped to identify AI-generated voice clones. With both good and bad actors gaining access to genAI tools, the average person now struggles to distinguish what is real from what is fake.
While we must encourage the evolution of technologies like genAI and appreciate the strides it has made in moving our world forward, we cannot do so without acknowledging that, once in the wrong hands, it can lead to catastrophic consequences for the average person. As tools like text-to-image apps become more democratized, we have to protect ourselves from this looming threat.
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How we got to where we are today
For as long as photography has existed, so has fake visual content. That means since 1839, people have been manipulating images. Deepfake technology has technically been around for almost as long as digital photography has existed (since the 1990s), but its rapid escalation and integration into our threat landscape occurred over the last eight years.
In December 2017, Motherboard’s Sam Cole reported that an anonymous Reddit user, named “deepfakes,” used AI tools to superimpose celebrity faces onto pornographic materials. And thus, the term deepfake, a word we see every day, was born.
A few years later, in September 2022, genAI tool DALL-E 2 became widely available, a turning point as anyone online now had the capability of creating synthetic images at scale. Today, over 300 million people use text-to-image tools like Midjourney or Firefly weekly, making them a valuable tool for research, productivity, and creativity, and also a point of contention for copyright and misinformation. A recent example follows the release of OpenAI’s image generator in March 2025, when users began experimenting with different animation styles that the image tool could replicate, leading to the “Studio Ghibli photo” trend.
Although not necessarily dangerous, this surge in fake content also signifies the ease with which malicious actors can utilize genAI tools to commit identity theft, completely stripping victims of their personhood. Last year, we saw how detrimental it can be when you can’t differentiate between what’s real and what’s fake when rumors circulated that Kate Middleton disappeared and was replaced by a Photoshopped image. Shortly thereafter, in November 2024, the US Department of Treasury’s Financial Crimes Enforcement Network issued an alert on fraud involving deepfake media, emphasizing the need for federal oversight of the malicious threats it presents.
The threat enterprises face today
To understand how deepfakes can cause such long-lasting consequences, we have to recognize how easily synthetic media can blend in with what we presume to be authentic. Deepfakes are a slippery slope.
It doesn’t take much to add a beauty filter on Zoom to enhance your skin and potentially just remove under-eye bags. What if we took this a step further and added a makeup filter or a wig to cover your hair? If you’re working from home one morning, how about using an avatar on calls that you take from bed? Before you know it, none of your colleagues have any idea of what’s going on on your end of the screen. The same notion applies to other applications of genAI – while in some instances, it can seem harmless, this kind of technology can quickly escalate. Repercussions of attacks like identity theft can be particularly spirit-breaking, as they essentially remove autonomy over one’s own identity and control over one’s physical and digital persona.
For enterprises, the use of genAI goes beyond ethical concerns. This type of manipulation can significantly exacerbate the spread of misinformation and pose dangerous ramifications that can tarnish a brand and its C-suite through reputation damage. For example, a well-profiled tech CEO may have an active LinkedIn profile, various headshots, and/or other personal data that is easily accessible online. Given the democratization of genAI tools, it wouldn’t take much for a disgruntled employee, customer, or competitor to falsify media that paints the CEO in a bad light, thereby discrediting their work and damaging their reputation.
Additionally, recently, organizations have experienced a massive surge in fake job seekers – the result of weaponized genAI to exploit enterprises. Gartner predicts that by 2028, one out of four job candidates globally will be synthetically curated from AI-generated profiles. These attackers have replicated identification, used synthetic audio and video during interviews, and submitted fake resumes. Welcoming a fake employee into an organization poses an immediate insider threat, as sensitive financial and employee data are at risk of compromise.
Organizations should no longer consider if they will be targeted by threat actors using synthetic or manipulated media, but rather when. And it’s time for every organization to consider what safety regulations need to be implemented to protect its brand and everyone associated with it.
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What enterprises need to understand about today’s threat landscape
As attackers begin to get personal, often stealing credentials and mimicking faces or voices, enterprises face a much more real threat than ever before, and it’s leading us to lose trust in what we see and hear. A Gartner report predicted that by 2026, 30% of enterprises won’t consider ID verification solutions reliable as a result of AI-generated deepfakes. It’s no longer enough to just focus on the who behind authentication; enterprises should ensure that they verify what digital content is being used.
We need to shift the way that we approach developing and adopting proper security practices. It’s time to rethink enterprise security posture with synthetic AI content in mind. This begins with raising awareness about today’s threats and how to combat deepfakes, both in our personal lives and the workplace.
All employees, regardless of age, department, or education, must be trained on deepfake awareness and the organization’s policies to prevent these types of attacks. Ensuring that employees are aware of this hazard is crucial to ensuring that all parties play their role in taking precautions.
Security leaders should expect to continually update protocols as threats evolve, rather than remain stagnant. Executives must continually ask questions about the evolving threat landscape and assess how their current security practices hold up against emerging vulnerabilities, updating their security posture with the latest technology and threat intelligence to stay ahead. Crafting a strategy that is adaptable enough to be tweaked, torn down, and rebuilt will be key.
To prevent widespread mistrust and compromise of business integrity, organizations must take the necessary steps to reconfigure their current security framework with AI in mind.
How enterprises can combat malicious genAI
Although the stakes are higher than ever before, it’s all the more reason to implement measures to protect yourself and your business. Ensuring security does not necessarily require a costly overhaul.
Consider implementing features like watermarks—a company logo, a signature, or a piece of text superimposed onto some form of media—into company multimedia. This can be your saving grace in accurately identifying the authenticity behind a piece of media. While transparent, they protect copyright and prevent others from using the media without permission.
Let’s also make sure everyone in your organization is on the same page. Educate your leadership on the dangers of where the threat landscape is headed, with threat actors using genAI and synthetic content at the forefront. Implement measures to mitigate your business from falling victim to these threats, including employee training and new security measures that provide analysis on what is real and what is not.
Finally, don’t be afraid to focus on analyzing what we do not know. Consider the world of criminal forensics: Investigators aren’t assessing the death of every human who passes – there aren’t enough resources, time, or need to do that. Instead, they hone in on cases that prove to be complicated or mysterious, suggesting foul play. We should be doing the same thing in the digital forensics world.
The evolution of AI is not only necessary but inevitable. The advances we see today in genAI have so much potential to make our world a better place. Unfortunately, bad people are just taking advantage of it. As a society, we must ensure that we are taking prevention and combating the consequences of deepfakes seriously to best protect our loved ones, colleagues, and ourselves from this looming threat.
