by PolicyForge (LX AI)
Product policy drafting is the work of turning what your app actually does — data collected, AI features, payment terms — into the documents you publish. The drafting is mechanical; knowing which facts matter is the part that cannot be templated.
When I started PolicyForge I looked at the apps I built and the ones I advised. Four documents cover the majority of cases:
If your app does not set cookies you can skip the cookie policy. If you sell a service rather than licence code you may not need an EULA. The key is to match the document to the actual product flow, not to a template.
I drafted the first version of the questionnaire by walking through a typical release cycle and noting every point where a legal fact could change.
We ask for a plain‑language description of the app’s main features, the platforms (iOS, Android, web), and any third‑party SDKs you embed. This tells us which statutes might apply (e.g., COPPA for children’s apps).
Next we request a list of every data point collected, including inputs to AI models, logs, and metadata. We differentiate between personal data (name, email, device ID) and non‑personal aggregates.
We ask which external parties receive the data: model providers, analytics services, payment processors, and any advertising networks. For each we note the legal basis for transfer.
If you take payments, subscriptions, or in‑app purchases we capture the currency, billing cycle, and refund policy. This feeds the payment terms section of the ToS.
We check whether the app is likely to be used by children under 13 and which countries you plan to launch in first. Those answers steer the privacy notices toward GDPR, CCPA, or COPPA language.
The questionnaire is deliberately short — usually under ten questions — because each answer maps directly to a clause or a set of clauses in the final draft.
AI components add three layers of risk that are not present in a traditional CRUD app.
We need to describe whether the model generates text, images, or code, and whether the output is shown directly to users or used internally. If the output is user‑facing we add a clause about prohibited content and a notice that the user remains responsible for what they do with the generated material.
If you fine‑tune a foundation model with user data we ask for the retention period, the opt‑out mechanism, and whether the data leaves the European Economic Area. This informs the privacy policy’s “purpose” and “transfer” sections.
We reference the OWASP LLM risk list (https://genai.owasp.org/llmrisk/llm01-prompt-injection) to remind teams to guard against prompt injection, data poisoning, and model theft. The NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) provides a concrete set of controls we map to our questionnaire answers.
These three points are added to the standard privacy and security questions; they do not replace them.
I want to be clear about the limits of the output.
If any of those points sound like a promise, they are not. The service provides mechanical drafting; the judgment layer stays with you.
Below is the step‑by‑step process I use when a founder submits the questionnaire.
[APP_NAME] and [EFFECTIVE_DATE] are replaced with the values you gave.To illustrate the mapping, here is a small table that shows how three questionnaire items turn into privacy‑policy sentences.
| Questionnaire answer | Triggered clause | Resulting sentence (plain English) |
|---|---|---|
| We collect email addresses for account creation. | Personal data – identifiers | We collect your email address to create and verify your account. |
| We send usage statistics to an external analytics provider. | Third‑party sharing – analytics | We share aggregated usage statistics with analytics-provider.example.com to improve performance. |
| We allow users to generate images with a text‑to‑image model. | AI output – user‑facing | Images created by the model are shown to you; you remain responsible for how you use or share those images. |
The table is only a sample; the real draft contains dozens of similar mappings.
Even with a thorough questionnaire, certain situations require a lawyer’s eye.
If you launch in both California and Brazil, the privacy notices may need to satisfy both CCPA and LGPD. The draft will contain a baseline that covers the stricter of the two, but a lawyer should verify that no contradictory obligations appear (for example, differing age‑of‑consent rules).
When your app incorporates open‑source components with copyleft licenses, the EULA must reflect the obligation to provide source code. The questionnaire does not ask about license compatibility; a legal reviewer adds the appropriate clause.
If you offer recurring subscriptions in the EU, the draft will include a generic cancellation clause. A lawyer should check that the wording meets the exact timing and information requirements of the EU Consumer Rights Directive.
Apple’s App Store Review Guidelines and Google Play Policies sometimes demand additional statements about data use for advertising. The draft includes a placeholder for “advertising‑related data” but the exact phrasing may need tweaking after a review.
In each case the draft saves time by providing a solid foundation; the human reviewer adds the nuance that turns a generic template into a fit‑for‑purpose document.
Legal documents are not static. I built PolicyForge to make updates painless.
Every time you regenerate a set of documents we store a PDF snapshot with a timestamp and a hash of the answers. This lets you compare what changed between releases.
When you edit the questionnaire we highlight which answers differ from the last submission. Changed answers trigger a re‑run of the rule engine only for the affected sections, keeping the rest of the document stable.
If you enable the email option we send a short notice whenever a new regulation is added to our rule set (for example, a new state privacy law). You can then decide whether to regenerate.
The goal is to reduce the manual copy‑paste work while keeping you in control of the final text.
No. It is an editable draft produced from your answers. Review by a qualified professional is still required.
Most apps need a privacy policy and terms; a EULA applies where you licence rather than provide a service, and a cookie policy applies where you set cookies or similar technologies.
It drafts against common requirements across those, but coverage claims should be confirmed by a reviewer for the markets you launch in.
Yes — the output is editable text. Editing it is expected.
Regenerate the documents and re-publish. A policy that no longer matches the product is a disclosure problem.
It produces the disclosures reviewers typically look for. Review outcomes depend on the app itself.
Compliance rules expressed as code, reviewed and tested like software.
GDPR, EU AI Act, and custom internal controls out of the box.
Each rule ships with test cases; CI runs them on change.
No - it governs policy logic, not data loss prevention.
Pro includes a template editor; changes still go through review.
Git-backed; each ruleset has a version for audit.
Try the product: PolicyForge