Data Protection for AI Products
Understanding the legal grounds for processing personal data is essential for AI product businesses.
We help AI companies, SaaS products, and IT businesses review their data processing models, prepare necessary documentation, and mitigate risks associated with GDPR, client contracts, AI providers, and enterprise client requirements.
When Data Protection Analysis for an AI Product Is Needed
Data protection analysis should be conducted if the company:
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Launches an AI product, SaaS, mobile application, or AI functionality within an existing product.
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Interacts with users, clients, or partners from the European Union.
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Processes personal data of users, clients, employees, or counterparty representatives.
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Transfers data to OpenAI, Microsoft, Google, Anthropic, or other AI providers.
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Uses prompts, logs, uploaded files, customer data, or user profiles in the operation of the AI system.
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Plans to use data for training, fine-tuning, testing, or improving AI models.
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Sells the AI product to enterprise clients and receives privacy/security questionnaires.
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Prepares a DPA, DPIA, Privacy Policy, Data Processing Terms, or data flow descriptions.
What We Check
Data Categories
We determine what data the AI product processes: personal data, technical data, client documents, prompts, logs, user files, and other categories of information.
Purposes and Legal Bases of Processing
We check for what purposes data is used: service provision, customer support, analytics, security, etc. For each purpose, we determine the relevant legal basis.
Data Flows
We determine the movement of data in the product: collection, storage, team access, transfer to AI providers, hosting, CRM, analytics, customer support tools, payment services, sub-processors, retention periods, and deletion.
Roles of the Parties
We determine what role the company plays in specific data processing processes: controller, processor, independent controller, or joint controller. This is important for DPA, Privacy Policy, client contracts, and compliance requests.
AI Providers and Sub-processors
We analyze what third-party services are involved in data processing, what data is transferred to them, where it is stored, whether it can be used to train models, and what confidentiality, retention, security, and deletion terms apply.
Data Use for Training or Model Improvement
We separately check whether the company can use client data for training, fine-tuning, testing, or improving AI models.
International Data Transfers
If data is transferred outside the EU or Ukraine, we check whether additional contractual or organizational mechanisms are needed for such transfer, specifically within relations with suppliers.
DPIA and Risk Assessment
We assess whether data processing in the AI product can create an increased risk to the rights and freedoms of individuals. If necessary, we determine whether a DPIA should be conducted.
Workflow Stages
Product Analysis
We examine how the AI product functions, which features utilize AI, target user personas, what data is collected, and which jurisdictions may be applicable.
Data Mapping
We map data flows: collection, storage, access permissions, transfer to AI providers, sub-processors, retention periods, deletion, and usage for product improvement.
Legal Assessment
We assess the data processing framework under GDPR, client contracts, controller/processor roles, cross-border data transfers, engagement of AI providers, and potential DPIA triggers.
Gap Analysis
We identify missing policies, provisions, or workflows: Privacy Policy, DPA, sub-processor lists, AI disclosures, data retention policies, internal data handling protocols, or data subject request procedures.
Document Preparation
We draft or update all legal documentation required for product launch, client negotiations, security reviews, investor due diligence, or onboarding enterprise clients.
Practical Recommendations
We deliver a concrete action plan for the team: required updates to documentation, provider settings to review, contractual safeguards to implement, and internal processes to enforce.
What Documents Might Be Needed
Privacy Policy
Describes what personal data the product collects, for what purposes it is used, to whom it is transferred, how long it is stored, and what rights the user has.
DPA
Required if the company processes personal data on behalf of a client or engages vendors who process personal data on behalf of the company.
Data Processing Terms
May be part of Terms of Use, SaaS Agreement, or a separate document for clients who want to understand data processing conditions in the product.
Sub-processor List
A list of vendors who may have access to personal data or participate in processing: hosting, AI providers, analytics, CRM, customer support, payment providers, etc.
Data Flow Description
A document or diagram explaining how data moves within the product and between vendors. Often required for enterprise clients, security reviews, or due diligence.
DPIA
A Data Protection Impact Assessment may be required if processing using AI creates a high risk to the rights and freedoms of individuals.
AI Data Use Notice
A separate notice or section in documents explaining how the product uses AI, what data is transferred to AI providers, and whether it can be used to train or improve models.
Internal Data Handling Rules
Internal rules for the team regarding data access, prompt usage, handling client files, uploading documents to AI services, and incident response.
Are clients or investors asking
how your AI product handles data?
For an AI product, having just a Privacy Policy is not enough — you need a transparent data processing model.
We will help audit your AI product, identify risks, draft the necessary documents, and formulate clear responses for clients, partners, or investors.
Fill out the form, and our lawyers will contact you to clarify the details.
FAQ
Does an AI product always fall under GDPR?
How does a data protection review differ from a Privacy Policy?
Can client data be transferred to AI services?
Can user data be used to train AI models?
When is a DPIA required?
What do enterprise clients typically ask before purchasing an AI product?
Is having a DPA with the AI provider sufficient?
Does an AI product always fall under GDPR?
No. GDPR applies if the AI product processes personal data and such processing falls within the scope of the GDPR. If the product works exclusively with non-personal or anonymized data, GDPR may not apply. However, other issues remain relevant: confidentiality, IP, client contracts, security, and AI provider terms.
How does a data protection review differ from a Privacy Policy?
A Privacy Policy is a document for users. A data protection review is an audit of the actual data processing model: what data is collected, where it is transferred, who has access, which AI providers are engaged, whether data is used for training or fine-tuning, and whether a DPA, DPIA, or additional contractual safeguards are required.
Can client data be transferred to AI services?
This depends on the contract with the client, the nature of the data, the company’s role, AI provider settings, confidentiality obligations, and whether the data can be used for model training. In many cases, such transfers require explicit client consent, a DPA, restrictions on sub-processors, or technical opt-outs from training.
Can user data be used to train AI models?
Not automatically. You must evaluate the legal basis, Privacy Policy, Terms of Use, client contracts, data categories, transparency for users, and AI provider terms. For client, sensitive, or special categories of data, specific restrictions or an outright ban on using such data for training or fine-tuning are often required.
When is a DPIA required?
A Data Protection Impact Assessment (DPIA) may be required if data processing involving AI creates a high risk to the rights and freedoms of individuals. This is especially relevant for high-risk AI systems involving profiling, automated decision-making, large-scale processing of sensitive data, candidate/employee evaluations, credit scoring, health-related data, or other sensitive use cases.
What do enterprise clients typically ask before purchasing an AI product?
Enterprise clients usually verify what data is processed, where it is hosted, who has access permissions, whether data is shared with AI providers, which sub-processors are involved, whether a DPA is in place, whether data is used for model training, what security measures are implemented, and how the company handles data subject requests and security incidents.
Is having a DPA with the AI provider sufficient?
Not always. A DPA is essential, but by itself does not prove that the AI service is used securely and in compliance with commitments to your clients. You also need to verify actual service configuration, terms of use for input/output, model training opt-outs, retention and deletion periods, team access controls, international data transfers, and compliance with your client agreements.
