Errors in AI-assisted recruitment and the role of cybersecurity in mitigating risks

Abstract

The primary objective of this research is to demonstrate the necessity of integrating cybersecurity mechanisms as a fundamental technical foundation for mitigating processing errors in AI-assisted recruitment. The paper aims to bridge the identified gap between theoretical ethical discourse and the practical implementation of technical control solutions within organizational environments. Specialized literature extensively documents algorithmic bias and discrimination, particularly regarding gender and ethnicity, as well as the risks of ”black-box” opacity in automated hiring. Previous research emphasizes the transition from human interaction to complex digital filters, while highlighting applicants' preferences for "augmented" hiring approaches rather than purely automated ones. Our quantitative bibliometric analysis of 1338 scientific articles (2020–2026) reveals a significant ”security deficit” in Human Resources (HR) workflows. The data shows a fracture between high technological performance and limited risk control, where cybersecurity is often perceived as an external administrative challenge rather than an internal control instrument. Consequently, the paper proposes a five-layer operational framework, covering data ingestion, preprocessing, training, decision and monitoring, to ensure data integrity and decisional equity. This study redefines cybersecurity from a defensive function into an active mechanism for guaranteeing social equity and mitigating algorithmic bias. By ”pulling” the cybersecurity node into the decisional center, the research provides a pragmatic, security-by-design solution for organizations. This approach transforms abstract ethical principles into quantifiable technical safety procedures, offering a structural pillar for transparent and resilient digital recruitment.