Researchers are exploring enhanced security measures for biometric data as traditional databases become prime targets for cybercriminals.
Irreversible Biometric Data Leaks
Traditional databases storing biometric data of millions are valuable targets for cybercriminals, with stolen information like facial scans and fingerprints potentially misused without owners’ knowledge. Experts are investigating ways to mitigate these risks.
If security is breached and unauthorized access is gained, the consequences are often irreversible; compromised biometric patterns, if not further secured, are permanently compromised. The key defense isn’t stronger database walls, but changing data storage methods.
Local Storage and Quantum Computing Threats
Currently, when using phones, facial images or fingerprints are stored locally as mathematical codes (hashes). Researchers state that, for now, stealing this code format prevents recreating the original image on another device.
However, quantum computers pose a significant threat to current encryption algorithms, potentially rendering them ineffective.
Decentralized and End-to-End Encryption
Experts recommend institutions move away from centralized biometric data collection towards distributed, end-to-end encrypted models.
AI, Deepfakes, and Behavioral Biometrics
The rapid development of artificial intelligence and associated threats like deepfakes necessitate more resilient methods. Behavioral biometrics—analyzing *how* someone uses a device (typing speed, phone handling)—offer a less invasive and harder-to-spoof alternative to simply identifying *who* they are.
Cloud Computing and AI Regulation
A shift away from cloud storage is anticipated, particularly in critical systems, driven by regulations like the EU’s AI Act, leading companies to process biometrics locally, mirroring the security of some smartphones.
Multifactor Biometrics and Selective Use
The future will likely see increased use of multifactor biometrics—combining multiple data points like facial recognition and hand vein patterns—especially in high-security systems. A selective biometric model is expected, used where speed is crucial (payments, airports) with strict data deletion rules post-authentication.
Liveness Detection and Data Invalidation
“Liveness detection”—verifying a live person is present, not an AI-generated deepfake—is a critical challenge. Companies will invest in sensors detecting micro-movements of the eyes or pulse. Thermographic imaging is already used in critical systems.
The problem of “non-revocability” remains the biggest flaw in biometrics; while passwords can be changed, there’s no equivalent for a compromised retinal scan.