What must a GenAI product's privacy policy cover under DPDP? A DPDP-compliant privacy policy for a GenAI product must be specific about the data a large language model actually touches: the prompts users type in (which frequently contain personal data), the outputs the model generates, conversation logs and embeddings you store, whether any of that data is used to fine-tune or train a model, and every third-party LLM or inference API the prompt is routed through. The DPDP Act 2023 is technology-neutral — personal data fed to or produced by an AI system is personal data, and the same consent, purpose-limitation and breach obligations apply. A generic SaaS privacy template will not disclose prompt-logging, training re-use or model-provider sub-processing, which are exactly the disclosures a GenAI product needs. This GenAI privacy policy generator builds a policy specific to your product's real data flows.
Generate a privacy policy built for a generative-AI product — prompt data, training re-use, output logs, embeddings and third-party LLM APIs — tailored to your product's actual data flows.
This section discloses, in plain language, that anything a user types into your product — the prompt, any pasted text, any uploaded document — is processed by the AI model and may be logged. That matters because users routinely paste personal data into prompts without thinking of it as a data-submission: names, email addresses, customer records, medical or financial details, even other people's information. Under the DPDP Act 2023, all of that is personal data the moment it is processed, and your policy must say clearly what is collected at the prompt, why it is processed (to generate a response), how long it is retained, and whether a human ever reviews it. A one-line 'we collect usage data' clause does not satisfy this — prompt content is qualitatively different from clickstream telemetry and must be disclosed as its own category.
The section also addresses the third-party data problem unique to GenAI: a user pasting a third party's personal data into a prompt makes your product a processor of that person's data even though that person never signed up. Your policy should set the expectation that users should not submit others' personal data without a lawful basis, describe how you handle such data if submitted, and explain that this is why prompt content is minimised, access-controlled and not retained longer than necessary for the stated purpose.
This section documents exactly what the user is consenting to and when. DPDP requires consent to be free, specific to each purpose, informed and unambiguous — a single blanket 'I agree' at signup does not cover distinct purposes like (a) generating a response, (b) storing conversation history, and (c) using that conversation to improve or train a model. Each of these is a separate purpose and, where you rely on consent, should be presented so the user can see and control it. The policy states which processing is strictly necessary to deliver the product (and therefore not separately optional) versus which is optional and opt-in — training re-use in particular should never be bundled into the necessary-processing consent.
The section also documents the withdrawal mechanism: where a user turns off conversation-history storage, opts out of training re-use, or deletes their prompt history, and what happens to data already processed versus data collected going forward. Because model outputs and embeddings can be derived from earlier prompts, the policy explains honestly what withdrawal can and cannot undo — for example, that a response already generated and shown cannot be un-generated, but that stored history and future training use will stop.
Data categories selected for your policy:
A generative-AI product processes personal data in ways a typical SaaS app does not, so a generic privacy policy leaves the exact disclosures regulators and enterprise buyers look for unmade. The three data flows unique to GenAI — prompt content that users freely paste personal data into, re-use of that content to train or fine-tune models, and routing of prompts through third-party LLM APIs that may retain or train on them — are precisely the flows a template built for a web app or mobile game never mentions. Under the DPDP Act 2023, which is technology-neutral, none of this is exempt: personal data fed to or produced by a model is personal data, and the same consent, purpose-limitation, retention and breach-notification obligations apply as to any other processing.
The commercial reality reinforces the legal one. Enterprise procurement teams evaluating an AI vendor now ask specifically whether prompts are used for training, which model providers sit in the sub-processing chain, and how long conversation data is retained. A privacy policy that answers these questions clearly is both a compliance document and a sales asset, while a vague one becomes a deal blocker in security review.
The highest-risk disclosure gap in most AI privacy policies is training re-use. Using customer prompts to improve or fine-tune a model is a distinct processing purpose that cannot be silently bundled into the consent a user gives simply to use the product — it should be separately disclosed with a genuine opt-out (or opt-in), and any data used for training should be minimised and, where possible, de-identified first. The second gap is sub-processor transparency: if a prompt travels to a third-party inference API, that provider is part of your processing chain and users are entitled to know who it is and what it does with their data.
With DPDP enforcement expected around May 2027, AI-native companies should treat the privacy policy as the visible tip of a broader compliance programme — data mapping, consent architecture, retention controls and vendor agreements underneath it. Niti Bharat runs fixed-price DPDP compliance engagements (₹75,000–₹3.2 lakh) for AI and SaaS companies that need the full programme, not just the policy, built and defensible.
One real DPDP development explained in plain English, one practical how-to, one number from our own assessment data. Nothing else — no daily noise, no sales pitch.
No spam. Unsubscribe with one click, anytime.
