Hiring has always involved some uncertainty. A resume only gives you part of the picture. An interview can tell you more, but it still happens in a limited amount of time, and references do not always reveal what a candidate is really like to work with.
In 2026, employers face a new concern: is the person behind an application really who they say they are?
AI has made it easier to write resumes, tailor applications, and prepare for interviews. In most cases, that is not a problem. Candidates can reasonably use technology to improve the way they present their experience.
The problem starts when technology is used to create experience that does not exist, impersonate another person, or hide who is actually applying for a job.
Recent research suggests this is no longer a theoretical concern.
A September 2026 iHire survey of 318 U.S. employers found that 23.6% had received at least one fake or fraudulent applicant during the previous year. Nearly one in five employers, 19.2%, said fake or fraudulent applicants were among their biggest online recruiting challenges. The survey also found that 39% of employers want job boards to help verify that candidates are real people rather than AI-generated identities.
A separate 2026 survey by iProspectCheck of 1,500 U.S. business managers and company owners found that 22.9% said they had knowingly interviewed a proxy or suspected deepfake candidate. The same survey found that 29.3% said they had hired someone who later did not appear to be the same person they had interviewed.
That does not mean employers should assume every polished resume is suspicious. It means employers need to look beyond how a candidate presents themselves and focus on the evidence behind their claims.
Start with the work, not the resume
The simplest way to test whether a candidate is genuine is to move the conversation away from general claims.
A resume might say that someone is an experienced project manager. A better question is what they actually managed.
Ask about a real project:
- What was the goal?
- What went wrong?
- What did you personally handle?
- What changed because of your work?
- What would you do differently now?
Specific experience tends to produce specific answers.
This is especially useful for remote positions, where an employer may have less opportunity to observe how someone works day to day.
Ask for evidence, not perfection
A candidate does not need to have a perfect portfolio to be credible.
Depending on the role, useful evidence could include a project they completed, a campaign they worked on, a piece of writing, a technical project, a process they improved, or a measurable business result.
For confidential work, candidates may not be able to share the actual material. That is fine. They can still explain the problem, their role, and the outcome without revealing private information.
The goal is not to force candidates to prove themselves through unpaid work. It is to understand whether their claimed experience has substance behind it.
Use practical questions during interviews
Generic interview questions are easy to prepare for, especially when candidates have access to AI tools.
Practical questions are harder to answer convincingly without relevant experience.
Instead of asking:
“Are you comfortable working independently?”
ask:
“Tell me about a time you had to complete an important project without regular supervision. How did you organize the work?”
Instead of:
“How do you handle difficult clients?”
ask:
“Tell me about the last difficult client situation you handled. What happened, and what did you do?”
The difference is small, but the second version asks the candidate to describe something that actually happened.
Don’t confuse AI use with dishonesty
This distinction matters.
Using AI to correct grammar, improve a resume, or prepare for an interview does not automatically make a candidate dishonest. In fact, the 2026 Robert Half research found that AI-enhanced applications are already affecting how employers evaluate candidates. In its survey of more than 2,000 U.S. hiring managers, 65% said AI-enhanced resumes had made it harder to verify candidates’ skills, while 67% of HR leaders said reviewing AI-generated applications had slowed hiring.
This is part of a wider problem employers are facing as AI changes the hiring process. AI has made applications easier to create, but evaluating what is behind them has become harder.
The issue is not whether a candidate touched an AI tool.
The issue is whether the experience, skills, and identity presented to the employer are genuine.
That is an important distinction because an overly aggressive response to AI could create a new problem: rejecting legitimate candidates simply because their application was professionally written.
Build verification into the process
Employers do not need to turn every hiring process into an investigation.
A sensible process can include several ordinary checks:
Verify the basics.
Check employment history, professional profiles, qualifications, and references where appropriate.
Check employment history, professional profiles, qualifications, and references where appropriate.
Test relevant skills.
Use a short, job-related assessment or practical discussion rather than relying entirely on a resume.
Use a short, job-related assessment or practical discussion rather than relying entirely on a resume.
Meet the candidate live.
For remote positions, a live video interview can help confirm that the person you are speaking with is the same person who submitted the application. But it should not be the only way you verify a candidate’s identity.
For remote positions, a live video interview can help confirm that the person you are speaking with is the same person who submitted the application. But it should not be the only way you verify a candidate’s identity.
Ask consistent questions.
A structured interview makes it easier to compare candidates and notice inconsistencies.
A structured interview makes it easier to compare candidates and notice inconsistencies.
Verify before access is granted.
For roles involving sensitive systems, customer information, money, or confidential data, identity and background checks may need to be more formal.
For roles involving sensitive systems, customer information, money, or confidential data, identity and background checks may need to be more formal.
The right level of verification depends on the role. A freelance writer and someone with access to financial systems do not present the same risks.
Trust should work both ways
There is another side to the problem.
Job seekers are also dealing with fake employers and fraudulent job postings. The 2026 iHire survey found that 41.2% of U.S. job seekers had encountered a fake or scam job during the previous year, while 42.8% said they had difficulty determining whether a job or employer on a recruiting platform was legitimate.
That means trust is becoming a two-sided issue.
Employers need ways to establish that candidates are genuine. Candidates need confidence that the company contacting them is genuine.
For remote hiring, where the entire relationship may begin online, that basic layer of trust matters even more.
The goal is not to catch people. It is to make better hiring decisions.
A hiring process should not become an interrogation because AI exists.
The better approach is simpler: rely less on what a resume says about a candidate and more on evidence that the person can do the work they are being hired to do.
Look for consistency between the application, interview, work history, and practical skills. Verify what needs to be verified. Keep the process proportionate to the role.
As AI becomes more common in hiring, the question is no longer, “Does this application look good?”
It is:
“What evidence do I have that this person can actually do the job?”
Hiring remotely? Start with the right candidates.
Online.jobs helps employers connect with remote talent from around the world. Post your job, review relevant candidates, and build a hiring process that looks beyond the resume.