Ethical Implications and Cybersecurity Risks of Hyper-Personalized AI Feedback Systems for Mental Health Support in Home Environment

• Published in: Journal of Engineering Research and Reports Vol. 27; no. 9; pp. 310 - 328
• Main Author: Udechukwu, Lisa Mmesoma
• Format: Journal Article
• Language: English
• Published: 16.09.2025
• Subjects: Engineering Sciences
• ISSN: 2582-2926; 2582-2926
• DOI: 10.9734/jerr/2025/v27i91643

This study explores the ethical implications and cybersecurity risks of hyper-personalized AI feedback systems designed for mental health support in home settings, examining both their transformative potential and associated challenges. The introduction opens by emphasizing the growing role of AI-driven mental health interventions, while underscoring critical concerns related to privacy, autonomy, and security. It then integrates existing research on digital phenotyping, ethical frameworks, and cybersecurity vulnerabilities, highlighting gaps in research on long-term impacts and the absence of standardized protocols. To address these issues, the study adopts a convergent mixed-methods design, combining quantitative cybersecurity assessments (e.g., CVSS, STRIDE) with qualitative ethical evaluations (IEACP) to ensure methodological rigor. The results show that 45.3% of identified vulnerabilities fall within the high-to-critical range, with Cross-site Scripting appearing most frequently. Ethical compliance averages 6.48/10, revealing notable shortcomings in areas of autonomy and informed consent. Among the systems evaluated, System 5 achieves the highest integrated score of 0.863; however, trade-offs between security and ethical considerations remain evident. The study concludes that although the proposed integrated framework effectively merges ethical principles with cybersecurity best practices, inconsistent compliance and rapidly evolving threats demand the establishment of stronger standards. Importantly, this framework offers a foundation for shaping future development and informing policy by aligning technological innovation with ethical responsibility and security requirements. Key recommendations include the development of standardized ethical guidelines, the implementation of advanced privacy-preserving technologies such as federated learning, and the conduction of real-world trials to strengthen resilience and equity. Ultimately, the findings underscore the importance of adaptive, patient-centered AI systems to deliver safe and ethical mental health support in home environments.