Beyond credit cards, what personal details become risky once an AI agent is handling them for you?

This explores what else counts as sensitive when AI agents act on our behalf, beyond payment details. The corpus suggests that credit cards are actually the easier case. Card numbers are a known, bounded secret, and a fixed solution exists for them: lock them in a vault so the agent never sees the raw number Can credential vaulting alone solve merchant-facing agent identification?. Users are also handing cards over quickly. One company reports that 40% of users share a card with its agent within three weeks How fast do users trust AI agents with payment data?. The harder problems are the kinds of information that don't look like secrets at all.

Take your home address. In one reported case, Meta's Muse agent finished a purchase and gave out a user's address without checking back, because a single "allow always" setting had overridden the per-purchase approvals Do broad consent settings pose bigger risks than prompt injection attacks?. The lesson is that sensitivity depends on context. An address is fine to give a shipping company and not fine to hand to any merchant an agent happens to choose. A 300-person study makes the same point from the other direction. People picked AI-drafted replies that leaked private details 55% of the time, mostly because they didn't notice the leak, not because they had decided it was acceptable Do people notice when AI agents leak private information?.

The less obvious category is information you never actually shared. LLM memory features can infer things about you, such as health, relationships, or habits, from scattered conversations. In one study, most of the 40 participants never noticed this until a researcher showed them. Once they saw it, every one of them wanted controls to edit and delete it Do users notice when LLMs infer and retain private information?. Personalization makes this worse over time: the more a chatbot remembers, the more people trust it, and the more there is to expose Does chatbot personalization build trust or expose privacy risks?. Feeling safe is also a poor guide. People who cleaned up chatbot transcripts by hand felt just as confident as people who used AI suggestions, yet they left far more identifying details behind Why do people feel safe sharing when they haven't actually removed sensitive information?.

For organizations, the list goes further: an agent's internal communications, approval records, and reasoning traces can all be sensitive. One approach publishes a cryptographic fingerprint of these records instead of the content itself, so outsiders can verify the records weren't altered without seeing what they say Can commitments protect sensitive agent data while enabling verification?. On the practical side, some designers argue against trying to list everything sensitive. The iMy contract sorts data into just two tiers: information the agent can use by default, and information that needs explicit approval. That split is simple enough for an agent to follow and for an auditor to check Can a two-category privacy boundary actually be auditable?. A related design divides the work: users say what they want kept private, and the AI does the tedious cleanup Does splitting privacy work between users and AI reduce cognitive burden?.

The takeaway: "sensitive" is less a fixed list of data types than a mismatch between where information ends up and where you expected it to go. The biggest risks are things you didn't realize you'd revealed and permissions you didn't realize you'd granted.