Build the diary around a clear trust contract: what stays on the device, what reaches an AI service, what the app remembers, who can access each item, how long it lasts, and how the user can inspect or delete it. Keep raw diary entries separate from AI-generated memories, enforce access rules in application code rather than a prompt, and make the conversational style a user-tested product choice—not a claim of clinical benefit.
Start with a privacy promise the architecture can keep
Decide what “private” means for this specific app before choosing a model or writing a system prompt. The UK Government’s Data and AI Ethics Framework says, “You must design and build privacy into your project from the start.” It also recommends privacy by default and transparency about collection, purpose, use, and storage duration. Explain those facts in onboarding and beside relevant settings; avoid an unqualified “private” label that obscures what actually happens to a user’s words. UK Government Data and AI Ethics Framework
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Write the trust contract in plain language, then make product behavior match it. Answer these questions before launch:
- What is collected? Identify diary text, account details, conversation history, derived summaries, diagnostics, and any other stored information.
- What leaves the device? Specify whether an entry, a selected excerpt, a summary, or no diary content is sent for inference.
- What is remembered? Define which memory types may be retained, their purpose, and whether they are created automatically or only with user approval.
- Who can access it? State the roles and services that can access stored content, including any operator access that your design permits.
- How long does it last? Give retention periods where established, and explain how deletion behaves across primary storage, derived memory, logs, and backups.
- What can the user control? Provide ways to inspect, correct, disable, export, and delete entries and memories.
These are design questions, not labels to resolve with one encryption setting. On-device processing, hosted inference, storage encryption, key ownership, provider retention, operational logging, backups, and deletion are separate parts of the trust contract.
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Choose where inference runs by mapping data exposure
On-device and hosted inference are deployment alternatives, not a universal ranking. Compare the complete data path for the model and services you intend to use. A hosted design may send all or only selected diary context to a provider; document the actual request payload rather than assuming the whole journal is or is not transmitted.
| Decision point | On-device inference | Hosted inference |
|---|---|---|
| Data exposure | Can keep diary text on the user’s device if the app does not separately upload it. | Some content must reach the service for hosted inference; minimize and identify the content sent. |
| Keys and storage | Determine how local data and device-held keys are protected, including what happens when a device is lost or replaced. | Establish where data is stored, who controls encryption keys, and whether a provider or operator can access content. |
| Retention and deletion | Define local retention and how removal applies to backups or synced copies. | Verify provider retention for the exact feature and configuration, then account for your own storage, logs, and backups. |
| Training and operator access | Assess any separate telemetry or sync services; local inference alone does not describe those data flows. | Check whether submitted data is used for model training and what service personnel access is possible under the applicable terms. |
| Availability and performance | Assess offline operation, supported devices, latency, and response quality on the target hardware. | Assess connectivity requirements, service availability, latency, and response quality for the chosen provider. |
Google’s September 23, 2026 announcement describes a vendor-specific Private AI Compute design for persistent server-side memory: encrypted storage, keys held on personal devices, an authenticated encrypted channel, and processing in isolated secure enclaves. These are Google’s descriptions of its own approach, not an independent guarantee for another app or hosted service. Check the linked technical materials and current availability before relying on the details. Google DeepMind announcement
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Provider retention also depends on the feature and configuration. OpenAI’s documentation for Zero Data Retention with Private Safety Processing describes a particular automated safety-review workflow using customer-controlled storage and a protected runtime. It states a 30-day TTL for customer-stored safety records in that workflow. That is not a blanket retention promise for all API requests: verify endpoint eligibility, settings, and current contractual terms for the specific application. OpenAI Private Safety Processing documentation
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Treat a raw entry as the user’s record and a generated memory as secondary data. A memory might be a summary or a fact intended to help a later conversation, but it can be wrong, too broad, or more sensitive than expected. Do not let a model silently turn every conversation into a permanent profile.
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For each memory type, specify its purpose, eligible content, source, persistence conditions, and user controls. Keep enough provenance to explain why an item exists—for example, whether it came from a particular entry or was inferred by the system. Microsoft’s AI memory guidance recommends establishing user intent and provenance before persistence. Microsoft AI memory guidance
A useful product pattern is to present retained memories as objects a person can inspect and manipulate: show the memory in understandable language, let the user correct or remove it, and distinguish deleting a derived memory from deleting its source entry. The Memory Sandbox paper is an early research prototype exploring memory recording, summarization, inspection, and sharing across conversations; treat it as a design reference, not evidence of a universal usability result. Memory Sandbox paper
Enforce identity and memory boundaries in application code
A system prompt can shape responses, but it should not be the mechanism that decides which person’s diary can be read. Microsoft states, “Prompting alone is not a reliable security boundary.” Its guidance recommends isolating memory by user, agent, and tenant with controls such as access-control lists, scoped tokens, and encryption in transit and at rest. Microsoft Security Blog and Microsoft Learn memory safety guidance
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- Resolve identity first. Authenticate the user before any diary or memory lookup.
- Scope every operation. Apply that identity to reads, writes, search, exports, and deletion; do not rely on a model-generated user identifier.
- Constrain retrieval. Ensure a conversation can retrieve only memories the authenticated user is authorized to access, and avoid caches shared across users unless they are securely partitioned.
- Validate memory writes. Check the eligible memory type, purpose, source, and applicable user preference before persisting a generated item.
- Test the lifecycle. Verify that export and deletion cover both raw entries and derived memories, and test isolation between separate accounts.
- Limit operational exposure. Avoid putting diary text in diagnostic or operational logs unless it is necessary and disclosed; keep the logged data to the minimum needed for the stated purpose.
These are engineering implications of access-boundary and data-minimization guidance, not requirements for a particular database, programming language, or framework.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make memory controls understandable and complete
Users should not have to guess what the app has retained. Put memory controls where people can find them and make the result of each action explicit. The Memory Sandbox offers a precedent for treating conversational memories as inspectable, manipulable data, while Microsoft’s guidance supports setting intent and provenance before persistence.
- Inspect: Show saved summaries or facts in plain language, with enough context to explain their origin.
- Correct: Let the user edit a memory when it is inaccurate or too broad, and prevent a later generation from silently restoring the rejected version.
- Disable: Offer a clear way to stop creating or using persistent memories without implying that this also deletes the diary.
- Delete: Make removal of a derived memory distinct from removal of its source entry. Explain any relevant delay or limits imposed by backups or provider-side retention.
- Export: Provide an intelligible copy of the user’s records and retained memories if the product supports export, rather than exposing only an opaque internal format.
Before release, test these controls against real data paths: a deleted memory must no longer be retrievable, and deleting an entry must follow the deletion behavior you promised for related summaries, copies, and backups.
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Design a gentle tone without making clinical claims
“Gentle” is an interaction-design objective, not a scientifically established intervention or a validated prompt formula. Choose what it means in your product, then test the wording and controls with intended users. Useful design options include asking before offering advice, reflecting the user’s own words without diagnosing, avoiding shame or forced positivity, and letting the person choose whether they want listening, questions, or practical next steps.
Give the assistant a bounded role: it can respond conversationally to what the user wrote, but the app should not present its tone as treatment or promise a mental-health outcome. Keep any crisis-handling behavior separate from this tone design and research and review it specifically before including it; the cited material here does not establish a crisis-response policy.
Quick Recap
Build and review in a deliberate sequence
- Write the data map. List each kind of input, storage location, recipient, purpose, retention behavior, and deletion path.
- Set privacy defaults. Decide what is off by default, what requires consent, and whether persistent memory is optional.
- Choose the inference path. Decide what content, if any, leaves the device and assess keys, access, retention, training use, offline behavior, and performance.
- Define memory policy. Name eligible memory types, their purpose and provenance requirements, and the review, correction, disable, and deletion controls.
- Implement deterministic access controls. Resolve the user before retrieval, scope reads and writes, and keep authorization outside the prompt.
- Write and test the tone. Specify conversational behaviors as product choices and test them with intended users without claiming a proven therapeutic effect.
- Verify the trust contract end to end. Exercise inspection, export, deletion, account isolation, logging, and backup behavior, then ensure onboarding accurately describes the observed behavior.
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