• Lavanya S S

Conversational agents are increasingly being deployed in the mental health domain to provide accessible, scalable, and cost-effective support. However, ensuring user privacy, maintaining contextual continuity, and enabling safe escalation to human professionals remain significant challenges. This paper presents the design and evaluation of a privacy-first conversational mental health agent that integrates three core features: (1) privacy-first architecture, minimizing data retention and employing encryption to safeguard sensitive information; (2) context-aware memory, allowing the agent to recall and adapt to prior interactions while respecting user consent and ensuring data minimization; and (3) escalation protocols, enabling the timely referral of users to licensed mental health professionals in cases of crisis, risk, or clinical necessity. We describe the technical framework, ethical considerations, and user experience principles that guided the system’s development. Initial pilot evaluations suggest that the approach enhances user trust, fosters more meaningful engagement, and provides a safer pathway for digital mental health support. The work contributes to advancing responsible conversational AI in healthcare by balancing usability, safety, and privacy in sensitive domains.

"Privacy-First conversational mental health agent with context memory and escalation protocols", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.c542-c545, September-2025, Available :http://www.jetir.org/papers/JETIR2509263.pdf

"Privacy-First conversational mental health agent with context memory and escalation protocols", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppc542-c545, September-2025, Available at : http://www.jetir.org/papers/JETIR2509263.pdf