Detecting Deepfakes and Proxy Test Takers: A Tactical Guide for Engineering Leaders

## The $50K Hallucination Your AI model just hallucinated in production, costing your company $50K in customer refunds. Why? A proxy candidate slipped through the cracks during the hiring process, using a deepfake to impersonate a qualified applicant. This scenario isn't merely hypothetical; it’s a growing reality in a

## Why This Matters For engineering leaders, the stakes of identity fraud are higher than ever. The potential financial losses, reputational damage, and operational disruptions can be catastrophic. Deepfakes and proxy candidates can undermine the integrity of your hiring process, leading to poor hires that impact team

## How to Implement It 1. **Set Up Liveness Detection**: Implement real-time checks during interviews to assess the candidate's liveliness. Use multiple signals, such as eye movement and facial expressions, to differentiate between real candidates and deepfakes. 2. **Integrate Device Fingerprinting**: Utilize device ID

## Key Takeaways - Always validate AI outputs by integrating multiple layers of verification. - Continuous liveness checks can significantly reduce the risk of fraud. - Create a response protocol that enables quick action when anomalies are detected.

## Response Runbook When a potential deepfake or proxy candidate is flagged: 1. **Assess the Evidence**: Gather all relevant data, including liveness scores, device fingerprints, and behavioral telemetry. 2. **Review the Case**: Have a designated reviewer analyze the evidence and make a decision. 3. **Document Findings

## FAQs - **What is liveness detection?** Liveness detection is a technique used to verify that a candidate is physically present during a verification check, preventing the use of static images or deepfakes. - **How can device fingerprints help in fraud detection?** Device fingerprints track unique identifiers and can