The people in this video were not arrested. But the video illustrates a problem that every security manager, HR manager and CEO must be aware of.
In the video above, the likenesses of Sam Altman, CEO of OpenAI, Dario Amodei, CEO of Anthropic, Mark Zuckerberg and Donald Trump, with Jensen Huang facing them, appear standing in a police lineup and singing together.
The situation is absurd, the result is amusing, and the video looks remarkably believable.
But no such lineup ever took place. The video was created with artificial intelligence as part of a satire of the AI industry.
The video was posted on X by Matthias Schmidt (@eurofounder) on 16 October 2026: https://x.com/eurofounder/status/2111122495667413424
I watched it and actually thought about something completely different: what happens when the ability to produce such a convincing reality meets the world of organizational trust and integrity?
Until recently, a photo, a voice recording and certainly a video call were considered significant evidence that a particular person had actually said or done something.
Today, that assumption is no longer safe.
We are entering a period in which it is possible not only to fake an identity, but also to generate for that identity behavior, a voice, facial expressions and interaction that look completely authentic.
And this is no longer just a problem of spreading false information. It is a real risk to organizational security.
From an external threat to an insider threat
Consider three scenarios:
A candidate for a sensitive position goes through a video interview using a false identity, with a face, voice and professional background that are not their own. The organization is convinced it has hired a specific person, but in practice it grants permissions and access to someone else.
A finance manager receives a video call from someone who looks and sounds exactly like the CEO, with an urgent instruction to transfer money or sensitive information. The employee carries out the instruction in good faith, and the organization is harmed from within.
And a video of an employee, circulating online, that purportedly documents criminal behavior, an extremist statement or an information leak. The organization rushes to conclusions, even though the event never took place.
In each of these cases, the technology allows an outside party to exploit the trust, processes and permissions of people inside the organization.
This is how a threat that originates outside the organization may turn into an insider threat incident.
Check the story, not just the video
And here the truly important question arises:
If we can no longer rely on what we see and hear, what can we rely on?
In my view, the answer lies in changing the method of checking.
Instead of asking only whether the video is real, we need to ask whether the story behind it is real.
This is where background checks and open-source intelligence (OSINT) become important.
A professional OSINT check does not settle for analyzing a single image or video. It examines the wider context and connects information from different sources, preferably independent ones, to build a more reliable picture.
For example:
1. Checking the source of the information: Who first published the video? When? Can the original source of the distribution be traced, and not just dozens of shares of the same content?
2. Verifying identities: Does the person appearing in the video match their digital identity over time? Is there a consistent professional history, social media activity, connections and external data that support their identity?
3. Cross-checking sources: Is there independent evidence of the alleged event? Do the dates, places, people and circumstances fit with other information that can be verified?
4. Examining activity patterns: Is there a gap between known digital behavior and an unusual event? Have new accounts, identity changes or deliberate attempts to create a false impression appeared recently?
5. Understanding the context and possible motives: Who is behind the distribution, what is their operating environment, and are there indications of an attempt to mislead, influence or cause harm?
It is important to stress: OSINT is not a magic solution. Open sources can also be fake, coordinated or mistaken. And the absence of findings does not prove that a person or a video is authentic.
But the more independent sources are cross-checked and the sequence of events is examined, the harder it becomes to base a fraud on a single fake piece of information.
And that is exactly the advantage of examining the big picture.
What to change in procedures
Alongside intelligence checks, organizations must also adapt their procedures: verify unusual instructions through an independent communication channel, apply dual control to sensitive actions, verify identities in recruitment processes, and avoid making decisions based on a single video.
This means that the responsibility for telling truth from forgery can no longer rest only with our own eyes, or with software that tries to detect Deepfakes.
It requires critical thinking, cross-checking of information and an understanding of the human, professional and organizational context.
The video above was meant to be funny. And it really is funny.
But it also illustrates how easy it will be to present us with a reality that never existed.
In an era when almost everything we see and hear can be faked, trust cannot rest on how convincing the evidence is. It must rest on the ability to verify it through additional evidence.
And this, in my view, is one of the most important changes that needs to happen today in the world of organizational integrity and security.
The professional basis for the recommendations
The recommendations in this article are based on three publications by US government bodies:
1. The joint publication by NSA, FBI and CISA (September 2023). The document Contextualizing Deepfake Threats to Organizations: Deepfake threats to organizations, and ways to prepare for, identify, defend against and respond to them. https://www.cisa.gov/news-events/alerts/2023/09/12/nsa-fbi-and-cisa-release-cybersecurity-information-sheet-deepfake-threats
2. The NIST AI 100-4 document (November 2024). Reducing Risks Posed by Synthetic Content: an overview of content provenance verification, labeling and detection of synthetic content, and the limitations of these technologies. https://www.nist.gov/publications/reducing-risks-posed-synthetic-content-overview-technical-approaches-digital-content
3. NIST's Guardians of Forensic Evidence program. A program for evaluating automated Deepfake detection tools. According to NIST, there is a gap between high accuracy in research and the difficulty of applying the tools in the real world. https://www.nist.gov/programs-projects/guardians-forensic-evidence