Guest Blog: Deepfakes and the Expanding Enterprise Attack Surface

In the early days of digital media forensics (the field which gave rise to what’s now more generally referred to as deepfake detection), the concern was mostly cultural. That is, the Internet would buzz at celebrity impersonations, viral political hoaxes, and manipulated videos that would be created or altered synthetically and then circulated across social media. It was a problem, but most organizations viewed it outside their normal purview.

More recently, however, this problem has evolved into something far more insidious. In fact, it seems reasonable to view deepfakes now as a direct enterprise threat. The same AI that can fabricate a video of a public figure can now convincingly mimic a CEO’s voice, a colleague’s face, or a trusted supplier’s identity. What was once just a mischievous novelty has become a powerful new attack vector against enterprise identity systems.

At TAG Infosphere, we see this shift as part of a larger pattern (admittedly perhaps a more technical view). That is, we see deepfakes as an expansion of the enterprise attack surface from networks and devices to people and personas. These deepfakes are not just about manipulated content. Rather, they are about synthetic identity, trust exploitation, and infiltration at a human layer that our current defenses struggle to authenticate.

Deepfakes as a New Form of Identity Attack

It should be clear to anyone who has been active in the workforce over the past few years (both during- and post-Pandemic), that enterprises have become increasingly dependent on video, voice, and credential verification. Whether it’s a remote onboarding interview, a supplier negotiation, or an executive meeting, the assumption is generally that the person on the other end of a screen is who they claim to be. Deepfakes unfortunately exploit that assumption.

Specifically, modern generative AI systems can now easily synthesize realistic facial expressions, lip movements, and vocal tones in real time. You’ve no doubt seen examples on the Internet. Attackers can now use these capabilities to impersonate executives or employees during video calls, send voice commands that mimic legitimate leaders, or even construct entire synthetic personas that persist across platforms.

The result of this attack surface expansion is a brand new class of identity-based intrusion attacks, ones that operate outside the traditional bounds of credential theft or phishing. Instead of stealing your password, the adversary becomes you – and Chief Information Security Officers (CISOs) in organizations of all sizes and shapes must find ways to combat this new challenge, which include seemingly endless difficult implications.

Infiltrating the Remote Workforce

As an example, consider that remote work has also widened the aperture for deepfake-driven attacks. With fewer in-person interactions, trust decisions are often made via digital interfaces. This includes camera feeds, chat applications, and collaboration tools. Attackers exploit this modern virtual environment by crafting convincing synthetic profiles of job candidates, suppliers, or partners.

TAG analysts have watched instances of “imposter onboarding,” where deepfake applicants clear HR interviews and background checks, able to gain legitimate access credentials. Once inside, they can exfiltrate data, install malware, or manipulate internal communications. The same approach applies to supply chain impersonation, where fake vendor representatives use cloned voices or video personas to redirect payments or extract sensitive business information.

This evolution makes clear that enterprise identity and access management (IAM) systems are completely insufficient. That is, traditional IAM assumes static, document-based verification at a point in time. Deepfake threats demand continuous validation of who—or what—is behind each digital interaction. This demands innovation, and our observation is that this requires new ideas, approaches, and technologies.

Implications for Zero Trust

Surprisingly, zero trust architectures are also insufficient to address the problem. This modern concept operates on a foundational principle that devices and systems must never implicity trust, but rather, should always verify. Yet, most Zero Trust implementations emphasize devices, networks, and data flows, not the authenticity of the human identities initiating those actions. Deepfakes, again, expose that blind spot.

For example, if a synthetic persona should manage to enroll in an organization’s identity system or impersonate an authorized user convincingly enough, it can operate undetected within the perimeter of zero trust controls. The issue is not just access, but legitimacy. The architecture can enforce security policies perfectly, but if the entity being validated is synthetic and fake, then the policies are essentially meaningless.

This is why we explain to our TAG community members that zero trust strategies, which are obviously advised, must now also include some sort of “identity integrity” as a core pillar. Startups such as GetReal Security go a long way toward meeting this objective. With such advanced platforms, in addition to verifying what device is connecting or where the request originates, security teams can also confirm who and if a given persona is real.

Building Continuous Identity Protection

As suggested above, startups like GetReal Security offer a glimpse into how the next phase of defense will have to increasingly focus on more continuous identity protection. Just as enterprises monitor systems for anomalous network behavior, they must now monitor human interaction patterns for signs of synthetic or manipulated presence – and we would expect that new technologies will be needed to accomplish this objective.

Ultimately, achieving a high level of deepfake security must involve deploying strong new technologies that can analyze biometric signals, voice cadence, micro-expressions, and behavioral patterns across multiple contexts. This must not invade privacy, but rather, just ensure authenticity. AI-driven verification grounded in digital forensics, like from GetReal Security, can help distinguish between a real human interaction and a generated imitation.

But we should caveat: Simple detection alone is not enough. Organizations will need to define protocols for what to do when the system or a person detects an anomaly. That could include automatic isolation of suspicious sessions, escalation to human verification, or policy-based revocation of temporary credentials. The key is not just spotting the fake. It’s responding effectively when one appears.

The Enterprise Identity Attack Surface

We began this blog with mention of an expanded attack surface. And, in fact, for most enterprises, deepfake and impersonation risks really do create new surface area to cover. Identity attacks are expanding to include synthetic personas that operate through legitimate communication channels. These threats intersect with account takeover, supplier fraud, executive impersonation, and even nation-state espionage campaigns.

The call to action from your friends here at TAG is for security leaders to immediately evaluate their own organization’s identity attack surface. They must map where visual, audio, or behavioral trust assumptions exist in workflows. And they must identify which roles, transactions, or systems depend on unauthenticated digital presence. The final step is to assess how these could be manipulated by deepfake technology.

Closing Thought

As with many emerging threats, the response from the enterprise will require both innovation and awareness. Readers interested in more insight into how this can be done are encouraged to reach out to the TAG analysts – or, alternatively, if they are interested in a modern platform that can help with this risk, then they are encouraged to reach out directly to the GetReal Security team for more information.

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Copyright © 2026 TAG Infosphere, Inc. This report may not be reproduced, distributed, or shared without TAG Infosphere’s written permission. The material in this report is comprised of the opinions of the TAG Infosphere analysts and is not to be interpreted as consisting of factual assertions. All warranties regarding the correctness, usefulness, accuracy, or completeness of this report are disclaimed herein.

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