How Steadyline Keeps Your Mental Health Data Safe

Steadyline protects mental health data through encryption, on-device processing for AI features where possible, and strict data minimization. The app collects only what is necessary for mood tracking and pattern detection. No data is sold or shared with advertisers, and users can export or delete all their data at any time.

Mental health data is not ordinary data.

It’s the log entry you wrote at 2 AM when you couldn’t sleep. The mood score that tracked a depressive episode, the kind of data that a complete tracking system needs to handle with care. The medication log that shows what you’re taking and when. If any of that leaked, or got used in ways you didn’t agree to, the damage would be personal in a way that a compromised credit card simply isn’t.

I built Steadyline knowing this. And I’ve made architectural decisions at every layer of the stack to ensure that using AI features in the app doesn’t mean handing your most vulnerable moments to a third party. This is a transparent account of exactly how it works.

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The threat model I actually care about

Before getting into solutions, let me be specific about what I’m protecting against:

1. Your mood data or writing reaching AI training datasets. The most common concern, and a legitimate one.
2. Identifiable data leaving your device when it doesn’t have to. AI should only see what it needs to do the job.
3. The third-party AI provider seeing more than necessary. If Groq needs to process something, it should get the minimum needed to do the job.
4. Data breach exposure. If Steadyline’s backend were ever compromised, what’s there to steal?
5. Opaque data practices. You should always be able to know what’s happening with your data.

Most wellness apps fail on at least one of these, which is part of why building a premium app solo meant owning every privacy decision from day one. Here’s where Steadyline stands on each.

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Layer 1: AI receives limited context, not your identity

The most privacy-preserving thing you can do with AI is limit what it sees. Steadyline uses an InferenceOrchestrator and backend routing that control what data reaches Groq, the AI provider currently used for optional AI features.

Your Steadyline data
    ↓  (minimized for the feature you choose)
Groq receives only the needed AI context
    ↓
No name, email address, or account identifier in the prompt

When AI analyses your data for mood insights, pattern detection, or a weekly summary, it receives structured tracking context such as mood, sleep, energy, tags, and aggregate patterns. For Ask SAM chat, it may receive your current message, limited recent chat history, and relevant summarized log context so it can answer the question you asked.

Groq processes that limited context and returns a response or insight. Your name, email address, and account identifier are not included in AI prompts.

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Layer 2: Supabase Row Level Security. Your data is yours, period

All data synced to the cloud lives in Supabase (Postgres). The critical protection here isn’t encryption (though that’s present too). It’s Row Level Security (RLS).

Every table that holds user data has policies like this:

-- Users can only ever read their own rows
create policy "users_own_data"
  on mood_logs
  for all
  using (auth.uid() = user_id);

What this means in practice: even if there were a bug in the app’s API layer that somehow constructed a query to fetch another user’s logs, Postgres would refuse it at the database level. There is no admin backdoor that can accidentally return your data in a query targeting someone else.

This isn’t a checkbox. It’s the actual security boundary for multi-tenant data isolation.

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Layer 3: What actually gets sent to AI APIs, and how

For features that need cloud AI (like the weekly insights summary), Steadyline sends a structured, de-identified context object rather than a bulk export of your app database.

Here’s the shape of what an AI provider actually receives:

{
  "period": "2026-02-17 to 2026-02-23",
  "mood_average": 6.2,
  "mood_trend": "improving",
  "energy_average": 5.8,
  "log_count": 6,
  "notable_themes": ["work stress", "sleep disruption", "social connection"],
  "stability_score": 71,
  "medication_adherence_pct": 92
}

What is not in that payload:

• Your name or any account identifier
• Your email address
• Your full log history as a bulk export
• Any Health Connect data

For Ask SAM chat, the payload is different because the feature is conversational: it includes the message you type and limited relevant recent context, which may include short note snippets when they help answer the question. It still excludes your name, email address, and account identifier.

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Layer 4: Health Connect data never leaves the device

Steadyline integrates with Android Health Connect to optionally correlate health metrics (steps, sleep, heart rate) with your mood logs. This feature exists purely locally.

Health Connect data is:

• Read on-device only
• Used only for local correlation analysis
• Never synced to Supabase
• Never included in any AI API request

The correlation engine (“your mood scores are 1.4 points higher on days you walk more than 7,000 steps”) runs entirely in the app. That’s the kind of insight that helps you track bipolar patterns without compromising privacy, because it never needs to leave your phone.

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Layer 5: Transparent controls, not hidden toggles

I think app privacy settings are usually theatre. A buried “data sharing” toggle that most users never find, defaulted to “on.”

Steadyline takes a different approach. The Privacy & Transparency screen shows you:

• Which AI provider processed your last request and what context was sent
• What data was included in each AI request
• A complete log of data sync events to Supabase, with timestamps
• A one-tap “delete everything” that triggers deletion on both the local SQLDelight database and all Supabase tables associated with your account

This isn’t just for users who care about privacy. It’s for anyone who wants to understand what an AI-powered app is doing with their data. That should be anyone who uses one.

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What I don’t do (and why those decisions matter)

I don’t use your data to train models. Steadyline uses inference-only API calls to Groq. No fine-tuning, no feedback loops that improve a model on your data. Your usage makes the app better at understanding you locally; it doesn’t feed a global model.

I don’t sell data. There’s no analytics SDK, no advertising SDK, no data broker integration. The business model is a subscription. That’s it. The NAMI recommends evaluating mental health apps for data practices before trusting them with sensitive information. This is why I don’t gamify mental health either: Steadyline has no financial incentive to monetize your data because the product itself is the revenue stream.

I don’t collect what I don’t need. Steadyline doesn’t ask for your real name. It doesn’t need it. Account identity is handled via Supabase Auth: an email or OAuth token, no PII profile beyond that.

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The harder question: what about future AI features?

AI capability is moving fast. I’m planning features that will use more sophisticated AI: pattern recognition across months of data, a conversational AI that can genuinely discuss your mental state over time.

My commitment for those features:

1. Same minimization standard. The more powerful the AI task, the more careful I’ll be about what context it receives.
2. Explicit opt-in for new AI features. No “we updated our terms” email that silently enables new processing.
3. On-device where it genuinely adds value. I’m actively building local models for tasks like tone classification and theme extraction, where on-device inference means better privacy and faster results.
4. Open about what’s changing. Posts like this one, every time something meaningful shifts.

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The bottom line

Mental health apps have a trust problem because most of them are built for good days, and research confirms many apps share data with third parties without adequate disclosure. They collect broadly, process opaquely, and explain nothing.

Steadyline is built on the opposite assumptions: collect minimally, send only the context needed for the feature, be explicit about what AI sees, and give you real controls.

If you’re evaluating Steadyline and have specific questions about data handling that this post doesn’t answer, email me. I’ll answer directly and, if the question is good, add it here.

Your data is yours. Your data knows things before you do, and the AI is there to help you understand it, not to consume it.

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Related reading:

• What AI Should and Shouldn’t Do in Mental Health Apps
• I Built a Mood Tracker Because Nothing Else Took It Seriously

Steadyline is available on Android. The full Privacy Policy is accessible from the Settings screen within the app.