Every day, organizations make high-stakes identity decisions based on human judgment. A helpdesk agent decides if a password reset request is legitimate. An IT admin approves contractor access. An HR coordinator verifies a new hire is who they claim to be.
These people are smart, trained, and following procedures. And they're being asked to do something humans are not particularly good at. Detecting deception under pressure with incomplete information.
Want proof? Watch The Traitors on Peacock. The contestants watch the same behavior, have the same conversations, and vote again and again on who to trust. Everyone in the castle knows deception is built into the game. The information is shared, the stakes are clear, and still, the wrong people keep getting banished.
This isn't bad judgment or reality-TV dramatics. It's how humans work. We trust confidence over evidence. We believe people who sound certain, who tell clean stories, who feel familiar. We miss the quieter signals that something doesn't add up. The Traitors just makes this failure visible.
The uncomfortable truth is that humans aren't particularly good at detecting deception, even when they know someone is lying. And most organizations have built identity and access systems that rely on this same flawed pattern-matching every single day.
Online impersonation is even harder to spot
Enterprise environments face harder conditions than a reality show roundtable. There's no eye contact, no body language, no shared room where everyone hears the same story. Just usernames, email addresses, login patterns, and someone on the other end of a ticket asking for their password to be reset.
The attackers targeting these systems aren't randomly assigned contestants. They've researched org charts, cloned voices, generated deepfake video, and practiced the exact phrasing employees use when they're locked out and frustrated. They're professionals working against people who are just trying to do their jobs.
Despite this asymmetry, most organizations still rely on human judgment at the exact moments where it matters most—password resets, access approvals, hiring, and sensitive requests.
These aren't trivial decisions. They're the keys to the kingdom. And organizations are asking people to make them based on the same flawed pattern-matching that fails so visibly on reality TV.
Risk scores just move the judgment call
Many organizations have evolved past "trust your gut." They've invested in risk-based authentication, behavioral analytics, and anomaly detection, tools that surface signals about whether something seems off. This feels more scientific and more defensible than relying on instinct alone.
But it doesn't actually solve the problem. Instead of asking "does this person seem legitimate?", the question becomes "is a risk score of 73 safe to approve?" or "should this login be trusted even though it's flagged as anomalous?" The human is still making the call. There are just more steps before they make it. Risk signals help organizations see the problem better, but they don't resolve it. They just shift responsibility from "trusting gut feel" to "interpreting the score."
What it looks like to actually solve this
Here's the thing. Broken decision-making processes can't be fixed by training people harder or giving them better signals to interpret. The problem itself (detecting sophisticated impersonation under time pressure with incomplete information) has outgrown what human judgment can reliably handle.
What works is designing systems that don't require human judgment in the first place. Not because people aren't smart or capable, but because the problem has outgrown what human pattern-matching can reliably handle.
That means building identity verification around technology that does three things.
1. Evaluates signals humans cannot perceive
Helpdesk agents can't tell if a selfie has spatial depth consistency or if it's a deepfake. IT teams can't verify that device signals are cryptographically attested rather than spoofed. HR teams can't detect pixel-level document manipulation in a government-issued ID. These aren't human-scale signals. They require technology to evaluate reliably.
This is where technology actually earns its place. Effective identity verification systems assess multiple dimensions simultaneously. Spatial integrity in live selfies to block deepfakes and replays, device attestation to confirm signals come from trusted and untampered hardware, document authenticity to detect forged or manipulated IDs, location verification to ensure GPS signals aren't spoofed, and identity correlation to confirm the person matches enterprise directory records.
No human can reliably process these signals, especially not in the 90 seconds they have to respond to a locked-out employee who needs access immediately.
2. Produces a decision, not a score
Consider the common output from risk-based systems: "This user has a risk score of 68. Proceed with caution."
That's not a decision. It's a liability transfer. Someone still has to decide whether 68 is acceptable, and that decision will vary by agent, by shift, by how busy the helpdesk is, and by how frustrated the employee sounds.
What works is a system that evaluates all available signals holistically and delivers a clear outcome. Verified or not verified. Not "probably fine" or "seems risky but decide for yourself," just a definitive answer that teams can act on immediately.
When cases are genuinely ambiguous, when signals conflict or the situation is unusual, the system shouldn't escalate to the helpdesk. It should route to identity experts who resolve the decision without pushing uncertainty back to internal teams.
3. Applies where human judgment fails most
This isn't about replacing every identity check in an organization. It's about protecting the specific moments where:
- Credentials don't exist yet (hiring and onboarding)
- Credentials have failed (account recovery and support)
- Credentials aren't enough (high-risk approvals and sensitive actions)
These are the moments attackers target because they know controls are weakest. They're also the moments where teams are under the most pressure to move fast, which makes judgment even less reliable.
Moving from identity judgment to identity verification
The Traitors is entertaining because the stakes are fake. The worst outcome is someone gets voted out of a game show.
Enterprise organizations are playing a similar game, but with customer data, financial systems, intellectual property, and employee access. The consequences of getting it wrong aren't just embarrassing. They're material. And unlike the show, there's no reveal at the end before real damage is done.
The question worth asking isn't "can we train people to be better at spotting lies?" It's "why are organizations still designing systems that require humans to be good at something that's genuinely hard for them?"
Identity verification doesn't have to be a judgment call. It can be a resolved outcome, backed by signals humans can't easily fake and delivered by systems that take responsibility for the decision. The alternative (hoping teams get it right under pressure, again and again, in moments that matter) isn't really security. It's optimism.
And that's exactly what attackers are counting on.
Moving Beyond Judgment-Based Identity Verification
If all of this sounds familiar, it's because most organizations didn't design their identity systems to fail this way. They inherited them. IAM and MFA were built to authenticate credentials, not verify humans. Helpdesks were built to resolve IT issues, not defend against social engineering. HR systems were built to manage records, not detect impersonation during hiring.
The gap between how these systems work and how attackers exploit them keeps growing. AI-generated deepfakes, voice cloning, and synthetic identities have made impersonation cheaper, faster, and more convincing than ever before.
Our 2026 Workforce Impersonation Report reveals how AI-enhanced hiring fraud is reshaping enterprise risk and what leading organizations are doing to stay ahead.
When identity verification strategies still rely on humans to interpret signals, make judgment calls, or decide when to trust, organizations aren't securing identity. They're distributing risk across people who were never equipped to carry it.
The tools to change this exist. The open question is whether organizations are ready to stop confusing judgment with security.
Nametag verifies the real human behind high-risk workforce actions, delivering clear identity decisions so organizations can act with confidence, without asking humans to detect what they can't see. Learn more at nametag.co.
