Publication
Revisiting Biometrics in Cybersecurity: Do AI Methods and Zero‑Trust Architectures Drive Innovation?
Siphesihle Sithungu; Christoph Lipps
In: International Conference on Cyber Warfare and Security. International Conference on Cyber Warfare and Security (ICCWS-2026), March 5-6, Wilmington, North Carolina, USA, Academic Conferences International, 3/2026.
Abstract
Biometric authentication has long been regarded as a foundational element of identity verification, leveraging unique physiological and behavioral traits to enhance security beyond traditional passwords. While it offers notable advantages such as convenience and resistance to identity theft, concerns are mounting regarding privacy, susceptibility to spoofing, and the irreversibility of compromised biometric identifiers. These weaknesses are becoming increasingly critical as digital infrastructures evolve into distributed, dynamic environments in which static trust models are no longer sufficient. Moreover, several traditional modalities
-such as fingerprints, iris scans, and voice recognition- have already been breached.
However, Artificial Intelligence (AI) methods are reshaping this landscape by introducing adaptive and context‑aware features into biometric systems. Machine Learning (ML) techniques enhance accuracy, enable continuous authentication, and support multimodal fusion, while anomaly‑detection mechanisms improve resilience against sophisticated attacks. Generative AI (GenAI) plays a particularly significant role, though it introduces a paradox: it empowers defenders through realistic attack simulations and robustness testing, yet simultaneously equips attackers with tools for producing deepfakes and synthetic identities, thereby expanding the attack surface.
Within this changing environment, Zero Trust Architectures (ZTA) have emerged as a prominent security paradigm built on continuous verification and the elimination of implicit trust. Integrating biometric data into ZTA can strengthen identity assurance but also amplifies existing challenges. Biometric information must be processed and stored in ways that protect privacy and comply with legal frameworks, and AI‑driven decision‑making raises concerns related to bias, explainability, and governance. In addition, the interplay between AI‑supported biometrics and Zero‑Trust principles prompts questions regarding scalability, interoperability, and the ethical implications of pervasive identity monitoring.
This work therefore examines the convergence of biometrics, AI, and Zero‑Trust principles from a critical perspective. It highlights the dual role of AI as both a source of innovation and a generator of new threats, while identifying opportunities for adaptive security, real‑time threat detection, and improved user experience. By analyzing technical and operational dimensions, the work proposes a roadmap for integrating biometrics into ZTA that balances innovation with accountability and supports trustworthy, resilient cybersecurity frameworks.