A privacy policy explains what personal data your app collects, why you collect it, who you share it with, how long you keep it, and what choices people have. For AI and no-code products, the hard part is not the wording. It’s understanding where data actually goes.

Not legal advice. This is a plain-language, minimum-viable overview to help you draft responsibly and know when you should talk to a lawyer.

Definition (quick)

Privacy policy: a public notice that describes your data practices (collection, use, sharing, retention, security, and rights).

It is different from your terms of service (rules and liability) and any DPA you may sign with business customers.

Why AI/no‑code apps get privacy policies wrong

AI-assisted and no-code tools make it easy to ship an MVP quickly. They also make it easy to lose track of data flows:

Analytics, support chat, and error monitoring start collecting identifiers by default.
Automations (Zapier/Make) copy data into places you forget (Sheets, CRMs, email tools).
Prompts sent to an LLM include more personal data than you intended.
Logs capture request bodies, attachments, or “helpful” debugging context.

The risk is a mismatch: your policy says one thing, your stack does another. That’s where user trust breaks—and where compliance problems begin.

Minimum viable privacy policy for AI/no‑code apps

You don’t need a 30-page document for an MVP, but you do need coverage of the basics. A minimum viable policy is specific about categories and honest about vendors.

1) What data you collect

Describe categories of data you collect and how you collect them. Typical categories:

Account data: name, email, sign-in method
User content: text, files, prompts, outputs, workspace content
Payment and billing: billing details and payment status (often via Stripe)
Usage data: IP address, device/browser info, pages/events
Support data: messages and attachments

If you collect sensitive data (health, biometrics, precise location, children’s data), say so. That’s often the point where an MVP template stops being enough.

2) Why you collect it (purposes)

Tie data to purposes people can understand:

Provide and operate the app (accounts, core features)
Improve reliability (debugging, performance)
Communicate (support, important notices)
Secure the service (fraud prevention, abuse detection)
Billing and accounting

For AI features, be explicit about what happens: you process user input to generate outputs, and you may review examples to improve quality and prevent abuse.

3) Sharing, subprocessors, and integrations

Most apps share data with service providers that help run the product. Explain (at a high level) which types of providers you use:

Hosting and infrastructure
Analytics
Customer support
Payments
Email/SMS delivery
Error monitoring
AI/LLM providers

Also cover user-enabled integrations (e.g., Slack, Google Drive) and legal/safety disclosures (lawful requests, enforcing terms).

4) Retention and deletion

“We keep data as long as necessary” is common, but it’s vague. Minimum viable retention language should answer:

What happens when a user deletes an account
Whether backups persist for a period and are overwritten
How long logs/analytics are kept (or what factors determine it)

For AI apps, include where prompts/outputs are stored (in the user workspace, in logs, or both).

5) Security (without overpromising)

You can reassure users without claiming perfection. Mention practical controls (access controls, encryption in transit, monitoring). Avoid “100% secure” language.

6) Cookies and tracking

If you use cookies or similar tech, explain what they do (login, preferences, analytics). If you run marketing pixels, say so, and point users to controls (browser settings and, where applicable, a consent tool).

7) Rights and choices

Rights vary by jurisdiction, but you can still explain the basics:

Access/update account info
Delete the account
Opt out of marketing emails
Contact you for privacy requests

If you have users in stricter regimes (e.g., EU/UK), you may need additional sections (legal bases, transfers, regional rights). If you’re unsure, treat it as a “get legal help” flag.

8) AI-specific disclosures that build trust

Even when not strictly required, these answers reduce surprises:

What AI does in your product (summarizes, drafts, classifies, searches)
What data you send to AI providers (and what you try not to send)
Whether you or vendors use data for training
Whether humans review inputs/outputs (support, debugging, abuse handling)

Common mistakes (and how to avoid them)

1) Copy-pasting a template that doesn’t match your stack. If you run Bubble + Stripe + PostHog + an LLM API, your policy must reflect that.

2) Forgetting side channels. Support tools, error tracking, and admin exports can hold personal data even when your database is small.

3) Saying “we don’t share data” while using vendors. Users read that as “we don’t send data to anyone.” Be honest about service providers.

4) No deletion story. If a user asks, “Can you delete my data?” you need an operational answer (including backups).

5) Overpromising compliance. Avoid blanket claims like “fully GDPR compliant.” Describe what you actually do instead.

How to draft responsibly (minimum viable process)

A short inventory beats guessing:

1) List every vendor that can touch personal data (hosting, auth, analytics, support, payments, AI, automations). 2) Map data by feature (signup, AI chat, file upload, billing, support): what comes in, where it’s stored, where it’s sent. 3) Minimize: remove fields you don’t need, redact logs, avoid sending unnecessary personal data to the AI step. 4) Write the policy from the map, then make sure the product behavior matches the document.

Checklist: minimum viable privacy policy (AI/no‑code)

When to talk to a lawyer

Talk to a lawyer if any of these are true:

You operate in, or market to, the EU/UK (or similar strict regimes) and you’re unsure what applies.
You process sensitive data (health, finance, biometrics) or children’s data.
You use targeted advertising or combine data across products.
You sell B2B and customers demand a DPA or security questionnaires.
You’re doing anything close to model training on user data, or vendor settings are unclear.

Where Spin fits (briefly)

If your AI/no-code MVP is getting traction and you’re not sure what your tools are collecting—or your policy doesn’t match your implementation—Spin by Fryga helps teams map data flows and reduce surprises without a rewrite.

Privacy Policy for AI & No‑Code Apps: Minimum Viable Guide