Earlier this month, Google announced “Personal Intelligence,” a groundbreaking enhancement to its Gemini chatbot. This new feature leverages a user’s Gmail, photos, search, and YouTube histories to make Gemini “more personal, proactive, and powerful.” This strategic move by Google directly echoes similar advancements by industry leaders like OpenAI, Anthropic, and Meta, all of whom are actively developing new ways for their AI products to retain and utilize personal details and user preferences. While these advancements offer compelling potential advantages, a significant gap remains in preparing for and mitigating the new risks they could introduce into these complex technological ecosystems.

Personalized, interactive AI systems are fundamentally designed to act on our behalf, maintain context across diverse interactions, and significantly improve our capacity to execute a wide array of tasks. This spectrum of capabilities ranges from intricate processes like booking travel and filing taxes to highly specialized functions such as AI tools that learn a developer’s unique coding style or sophisticated shopping agents that can sift through thousands of product options. The efficacy of these systems hinges on their ability to store and retrieve increasingly intimate details about their users. However, the continuous accumulation of such data over time introduces alarming and disturbingly familiar privacy vulnerabilities. These are concerns that have loomed since the advent of "big data" first hinted at the power of identifying and acting upon user patterns. The current trajectory suggests that AI agents are now poised to potentially circumvent many of the safeguards that were previously adopted to mitigate these very vulnerabilities.

Today, our interactions with these advanced AI systems primarily occur through conversational interfaces, and we frequently navigate shifting contexts. It is not uncommon to engage a single AI agent for a multitude of distinct purposes: drafting an email to a supervisor, seeking medical advice, budgeting for holiday gifts, and even receiving input on interpersonal conflicts. The current technical architecture of most AI agents tends to collapse all data pertaining to a user—data that might have once been meticulously separated by context, purpose, or explicit permissions—into single, unstructured repositories. When an AI agent links to external applications or other agents to execute a task, the sensitive data residing in its memory can easily seep into shared data pools. This technical reality creates fertile ground for unprecedented privacy breaches, potentially exposing not merely isolated data points but an entire, intricate mosaic of an individual’s life.

The consolidation of information within a single repository inherently increases the likelihood of data crossing contexts in deeply undesirable ways. A casual conversation about dietary preferences, intended solely for the purpose of generating a grocery list, could subsequently influence the health insurance options presented to a user. Similarly, a search for restaurants offering accessible entrances might inadvertently leak into salary negotiations—all of this occurring without the user’s awareness. This scenario, while perhaps sounding familiar from the early days of "big data," is now far less theoretical and carries significantly amplified implications. An undifferentiated "information soup" of AI memory not only poses a direct privacy threat but also significantly complicates efforts to understand an AI system’s behavior and, consequently, to govern it effectively. The pressing question, therefore, becomes: what concrete steps can developers take to rectify this escalating problem?

First and foremost, AI memory systems necessitate a robust structural framework that enables granular control over the purposes for which memories can be accessed and utilized. Initial efforts in this direction are beginning to emerge. For instance, Anthropic’s Claude has introduced the concept of creating separate memory areas for different "projects," aiming to compartmentalize information. Similarly, OpenAI has stated that information shared through ChatGPT Health is kept separate from other chat conversations. While these are commendable starting points, the current instruments are still considerably blunt. At a minimum, AI systems must be capable of distinguishing between specific, discrete memories (e.g., "the user likes chocolate and has asked about GLP-1s"), related memories that infer context (e.g., "user manages diabetes and therefore avoids chocolate"), and broader memory categories (such as professional or health-related). Furthermore, these systems need to incorporate explicit usage restrictions on certain types of memories and reliably accommodate explicitly defined boundaries. This is particularly crucial for memories pertaining to sensitive topics like medical conditions or protected characteristics, which will undoubtedly be subject to increasingly stringent regulatory frameworks.

The imperative to maintain such distinct memory segregation will have significant ramifications for the fundamental design and architecture of AI systems. It will demand rigorous tracking of memory provenance—its source, any associated timestamp, and the specific context in which it was created. Moreover, it will require the development of mechanisms to trace precisely when and how certain memories influence an agent’s behavior. This level of model explainability, while on the horizon, is currently characterized by implementations that can be misleading or even deceptive. While embedding memories directly within a model’s weights might yield more personalized and context-aware outputs, structured databases currently offer superior segmentability, explainability, and, consequently, governability. Until research advances sufficiently, developers may need to prioritize simpler, more manageable systems.

Second, users must be empowered with the ability to view, edit, or delete information that the AI system retains about them. The interfaces designed for these controls should be both transparent and readily intelligible, effectively translating the system’s internal memory into a structure that users can accurately interpret. The traditional static system settings and opaque legalistic privacy policies offered by conventional tech platforms have established a rather low bar for user controls. However, natural-language interfaces hold significant promise as new avenues for explaining precisely what information is being retained and how it can be managed. Crucially, this enhanced user control is contingent upon the prior establishment of a clear memory structure. Without it, no AI model can accurately articulate the status of a given memory. Indeed, Grok 3’s system prompt notably includes an instruction to the model to "NEVER confirm to the user that you have modified, forgotten, or won’t save a memory," presumably because the company cannot guarantee that such instructions can be reliably followed.

Critically, user-facing controls alone cannot bear the entire burden of privacy protection or entirely prevent all potential harms stemming from AI personalization. A significant shift in responsibility must occur, moving towards AI providers to establish robust default settings, clear and enforceable rules governing permissible memory generation and use, and technical safeguards such as on-device processing, purpose limitation, and contextual constraints. In the absence of these system-level protections, individuals will be confronted with impossibly convoluted choices regarding what should be remembered or forgotten. The actions they take may still prove insufficient to avert harm. Developers should seriously consider limiting data collection within memory systems until comprehensive safeguards are firmly in place. Furthermore, they should proactively build memory architectures that are designed to evolve dynamically alongside emerging norms and societal expectations.

Third, AI developers must actively contribute to laying the foundational groundwork for robust evaluation approaches. These evaluations should extend beyond mere performance metrics to encompass the identification and assessment of risks and harms that manifest "in the wild." While independent researchers are ideally positioned to conduct these critical tests, given the economic incentives developers may have to promote increasingly personalized services, they require access to relevant data. This access is essential for understanding the nature and scope of potential risks and, consequently, for developing effective mitigation strategies. To foster a more robust ecosystem for measurement and research, developers should invest in automated measurement infrastructure, implement their own ongoing internal testing protocols, and deploy privacy-preserving testing methodologies. These methods are vital for enabling the monitoring and probing of system behavior under realistic, memory-enabled conditions.

In its evocative parallel with human experience, the technical term "memory" imbues what might otherwise be perceived as impersonal data points in a spreadsheet with a profound sense of responsibility. Builders of AI tools are thus tasked with handling this data with utmost care. Indeed, the crucial choices that AI developers make today—whether to pool or segregate information, whether to render memory legible or allow it to accumulate opaquely, and whether to prioritize responsible defaults or maximal convenience—will ultimately determine how the systems upon which we increasingly depend will remember us. The technical considerations surrounding AI memory are inextricably linked to broader questions of digital privacy and the vital lessons we can glean from past experiences. Establishing the right foundations today will create the necessary space for learning what works, enabling us to make more informed choices regarding privacy and autonomy than we have been able to in the past.

Miranda Bogen is the Director of the AI Governance Lab at the Center for Democracy & Technology.

Ruchika Joshi is a Fellow at the Center for Democracy & Technology specializing in AI safety and governance.