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Cybersecurity and Privacy Risks of Generative AI Mental-Health Chatbots: A Systematic Review and Regulatory

Suhila Sawesi1, Hemalatha Sabbineni1, Rajashekhar Reddy Shagamreddy1

  • 1Department of Information Sciences and Technologies, College of Computing, Grand Valley State University, Grand Rapids, MI, USA.

Journal of Multidisciplinary Healthcare
|May 29, 2026
PubMed
Summary

Large language models in mental healthcare pose cybersecurity and privacy risks. Current regulations inadequately address these risks, necessitating stronger safeguards and updated governance for AI in healthcare.

Keywords:
clinical safetyconversational systemsdata protectiondigital therapeuticsregulatory frameworksrisk management

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Area of Science:

  • Artificial Intelligence in Mental Health
  • Cybersecurity and Privacy in Healthcare
  • Software as a Medical Device (SaMD) Regulation

Background:

  • Generative AI, including large language models (LLMs), is increasingly adopted in mental health services for applications like psychoeducation, emotional support, and crisis intervention.
  • Existing regulatory frameworks for Software as a Medical Device (SaMD) may not fully encompass the unique cybersecurity and privacy challenges posed by LLM-based systems.

Purpose of the Study:

  • To systematically map LLM-specific cybersecurity and privacy risks against SaMD regulatory frameworks.
  • To characterize deployment patterns of LLMs in mental health.
  • To evaluate the alignment of reported safeguards with healthcare governance standards.

Main Methods:

  • A PRISMA-guided systematic review of studies from PubMed, APA PsycNet, and Google Scholar.
  • Inclusion criteria screening of eligible records.
  • Independent data extraction by two reviewers on application domains, deployment settings, risk categories, attack surfaces, data sensitivity, and controls.

Main Results:

  • Generative AI chatbots were predominantly used for therapy/emotional support (39.4%) and suicide prevention/crisis detection (30.3%).
  • Most systems utilized general-purpose LLMs (63.6%) deployed on consumer-facing platforms (48.5%).
  • Key risks identified include harmful outputs, crisis response failures, sensitive data exposure, and lack of transparency, with 78.8% of studies showing low cybersecurity rigor.

Conclusions:

  • Current governance frameworks require adaptation to address generative conversational AI in mental health.
  • Single-layer security evaluations are insufficient due to the therapeutic interface acting as a primary attack surface.
  • Comprehensive approaches are needed, integrating robust cybersecurity, privacy-preserving practices, and alignment with FDA SaMD, HIPAA, ISO 14971, and NIST AI Risk Management Framework.