Synthetic Memory Injection: Enhancing Personalization Without Data Retention in Conversational AI

With the conversational delivering rapid AI expansion personalized applications, user experiences has become increasingly important. Many current systems rely on saving user details to personalize responses, but this can lead to privacy and security issues. This study presents a novel approach termed Synthetic Memory Injection (SMI), which enables conversational AI systems to simulate personalized interactions without permanently retaining user information. Instead of relying on stored data, the system dynamically constructs temporary contextual representations during active conversations. The proposed approach aims to achieve a balance between personalization and privacy by eliminating long-term data storage while preserving contextual relevance. The results show that SMI systems can maintain good personalization while reducing risks. This approach could be useful for creating AI systems that focus more on user privacy.