Privacy-Aware Conversational AI Systems for Secure Interactions

Abstract

Conversational AI systems are a new norm of present-day technology, significantly changing how people and companies communicate with digital services. These systems, built on strong NLP and artificial intelligence tools like machine learning, address and facilitate conversations in customer support, healthcare, and shopping. This has made them increasingly efficient in their services and experiences. However, since these systems deal with personal and financial data, privacy and security have become significant issues of contention. It remains crucial to preserve such users' trust; therefore, there is a need to create privacy-conscious conversational AI models. Among the issues that raise concern are security breaches, unauthorized access, and data encryption that violates GDPR and HIPAA provisions. In response to these issues, privacy-by-design should integrate into AI creation, including encryption, data protection features, and land legislation. Conve should be followed by traditional AI systems that require the consideration of privacy concerns while demonstrating real-time simulations applied to actionable scenarios such as secure customer support in banks, anonymous medical advice, and e-commerce transactions. This essay illustrates forward-thinking methods like safe transmission, pseudonymization, and data tokenization. Based on the data given here, it looks plausible to design such systems to protect user identities while allowing web applications to interface with users. Safe conversational AI systems provide additional issues, which the essay addresses. Additionally, it suggests ways to avoid common AI system reliability issues and create a safe conversational processing platform.