"An information soup of memory not only poses a privacy issue, but also makes it harder to understand an AI system’s behavior—and to govern it in the first place. So what can developers do to fix this problem? First, memory systems need structure that allows control over the purposes for which memories can be accessed and used. Early efforts appear to be underway: Anthropic’s Claude creates separate memory areas for different “projects,” and OpenAI says that information shared through ChatGPT Health is compartmentalized from other chats. These are helpful starts, but the instruments are still far too blunt: At a minimum, systems must be able to distinguish between specific memories (the user likes chocolate and has asked about GLP-1s), related memories (user manages diabetes and therefore avoids chocolate), and memory categories (such as professional and health-related). Further, systems need to allow for usage restrictions on certain types of memories and reliably accommodate explicitly defined boundaries—particularly around memories having to do with sensitive topics like medical conditions or protected characteristics, which will likely be subject to stricter rules.Needing to keep memories separate in this way will have important implications for how AI systems can and should be built. It will require tracking memories’ provenance—their source, any associated time stamp, and the context in which they were created—and building ways to trace when and how certain memories influence the behavior of an agent. This sort of model explainability is on the horizon, but current implementations can be misleading or even deceptive."technologyreview.com/2026/01/2 #AI #GenerativeAI #DataProtection #Privacy #LLMs #Memory #AIExplainability