AI is hacking your interviews: 4 defenses every engineering manager should deploy
Your next interviewee might not just be faking skills. They might be faking their entire identity.
AI has infiltrated the hiring pipeline.
On one end, we’re seeing remote candidates using AI to generate answers in live coding interviews. On the other, we’re seeing deepfakes complete with synthetic voices and fabricated identities. Some are malicious actors, while others are real developers looking for shortcuts.
Gartner has estimated that by 2028, 25% of job candidates will be AI-generated fakes.
So how do you protect your hiring pipeline from manipulation?
Here are 4 proven strategies you can use to stay one step ahead of AI-powered cheating and deepfakes.
4 Strategies to Detect AI Cheating and Deepfakes
1. Adapt the Format to Reduce Predictability
Scripted interviews are easy to game. Adaptive interviews? Not so much.
What to do: Vary your questions. Change the framing. Add curveballs.
Why it works: Real candidates adapt. AI struggles with nuance.
Tip: Rotate formats across interviewers to reduce over-optimization.
2. Use Signal-Tracking Tools (as Context, Not Gospel)
Modern platforms offer tools to detect tab-switching, keystroke dynamics, or suspicious code patterns.
What to do: Leverage confidence scores from your chosen platform.
Why it works: These behavioral cues help flag inconsistencies.
Tip: Use these signals to guide deeper probing—not disqualify on their own.
3. Trust (and Train) Your Interviewers
A veteran interviewer knows when something feels off. Overly polished answers, missing nuance, or a weird drop-off on follow-up questions? All red flags.
What to do: Train interviewers to recognize patterns that indicate inauthentic responses.
Why it works: Human judgment is your first line of defense.
Tip: Always debrief with multiple interviewers to surface concerns.
4. Reinforce with Real-World Tasks
Introduce ambiguity, shifting requirements, and subtle errors.
What to do: Assign take-home projects, pair programming, or real bugs from your codebase.
Why it works: AI can fake clean demos, but struggles when things get messy.
Tip: Ask candidates to explain trade-offs or adjust to unexpected changes.
Protecting your hiring pipeline
Interviews have always been gameable. From memorizing LeetCode to trading insider docs, people have long optimized for the test instead of the job. AI just raised the stakes and scale.
Just like in software security, every new exploit demands a new patch. You don’t scrap the whole system. You shore it up, harden the edge cases, and evolve your defenses.
The same goes for hiring.
You don’t need to throw away your remote pipeline. You just need to design it for resilience. AI can fake performance, but it can’t fake deep understanding, adaptability, or team instinct.
What strategies are you using to detect AI in interviews? Leave a comment and share your advice.
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