AI tools are no longer a niche. Millions of businesses now use AI-powered writing assistants, image generators, code completion tools, chatbots, data analysis platforms, and workflow automation products. But the legal landscape for AI has shifted dramatically. The EU AI Act entered into force in 2024, with transparency and disclosure obligations phasing in through 2025 and 2026. GDPR authorities have issued specific guidance on AI and personal data. And in the US, California, Colorado, and other states have enacted AI-specific disclosure requirements.

If your product uses AI in any capacity — from a simple recommendation engine to a foundation model — your privacy policy needs to go beyond the standard data collection disclosures. Users, regulators, and business customers all expect transparency about what your AI does with their data.

Why AI Tools Need Specialised Privacy Policies

Traditional privacy policies focus on what data you collect and how you use it. AI tools introduce additional considerations that standard templates do not address:

User inputs may be used for model training. When users interact with your AI tool, their inputs (prompts, uploads, conversations) may be used to improve the model. This is a fundamental privacy concern that must be disclosed and, in many jurisdictions, requires explicit consent.
AI outputs may contain personal data. If your model was trained on data that includes personal information, it may generate outputs that contain or reference real individuals’ data. This creates GDPR obligations you must address.
Automated decision-making triggers specific rights. If your AI tool makes or assists in decisions that affect users (content moderation, credit scoring, hiring recommendations), GDPR Article 22 gives users the right to human review.
Data flows through AI pipelines are complex. User data may pass through embeddings, vector databases, prompt chains, and third-party model APIs before a response is generated. Each stage involves data processing that your privacy policy should describe.
The regulatory environment is new and evolving. The EU AI Act, updated GDPR interpretations, and US state laws are creating requirements that did not exist two years ago. A privacy policy written in 2024 is almost certainly outdated for 2026.

EU AI Act: What It Means for Your Privacy Policy

The EU AI Act is the world’s first comprehensive AI regulation. It classifies AI systems by risk level and imposes corresponding obligations. Even if your company is not based in the EU, the AI Act applies if your product is available to EU users — similar to GDPR’s extraterritorial reach.

Transparency Obligations (Article 52)

All AI systems that interact with humans must comply with transparency requirements. For AI tools, this means:

Disclosure of AI interaction: Users must be informed that they are interacting with an AI system, unless this is obvious from the context. A chatbot must identify itself as AI. An AI writing assistant must be labelled as such.
AI-generated content marking: Content generated by AI (text, images, audio, video) must be disclosed as AI-generated when it could be mistaken for human-created content. This applies to deepfakes, synthetic media, and AI-written text published as if written by humans.
Emotion recognition and biometric categorisation: If your AI analyses emotions, biometric data, or categorises individuals, additional disclosures are required.

High-Risk AI System Requirements

If your AI tool falls into a high-risk category (which includes AI used in employment, education, credit scoring, law enforcement, and critical infrastructure), you face additional obligations:

Risk management system documentation
Data governance and quality requirements for training data
Technical documentation and record-keeping
Transparency to users about the system’s capabilities and limitations
Human oversight provisions
Accuracy, robustness, and cybersecurity requirements

While many of these are operational requirements, your privacy policy should reflect the fact that these safeguards exist and explain how they protect user data.

General-Purpose AI Model Obligations

If you provide a general-purpose AI model (a foundation model that can be adapted for multiple tasks), the AI Act requires you to:

Publish a sufficiently detailed summary of the training data
Comply with EU copyright law regarding training data
Provide technical documentation about the model

For models with systemic risk (those trained with significant compute), additional obligations include adversarial testing, incident reporting, and cybersecurity measures. Your privacy policy should reference these measures as part of your transparency commitments.

Training Data Disclosures

One of the most significant privacy questions for AI tools is what data the model was trained on and whether user data contributes to ongoing training. Your privacy policy must address both.

Pre-Training Data

If you trained your own model or fine-tuned an existing one, your privacy policy should disclose:

Categories of training data: Broadly describe the types of data used for training (publicly available web data, licensed datasets, user-contributed data, synthetic data, etc.)
Whether personal data was included: If your training data includes personal information (names, contact details, biographical data), this is a GDPR processing activity that requires a lawful basis and disclosure.
Data sourcing and rights: How training data was obtained and what rights or licences apply. The EU AI Act specifically requires a training data summary that enables copyright holders to exercise their rights.
Opt-out mechanisms: Under GDPR, individuals whose data was used for training have the right to object. Explain how individuals can request removal of their data from your training set (to the extent technically feasible).

User Data and Ongoing Training

This is the disclosure that matters most to your users. Be explicit about:

Whether user inputs are used for model training: State clearly whether prompts, uploads, conversations, or other user inputs are used to train or improve your AI model. If yes, explain the process. If no, state this explicitly.
How users can opt out: If user data may be used for training, provide a clear opt-out mechanism. Many AI companies offer an account setting to disable training data contribution. This must be prominently disclosed.
Data anonymisation and aggregation: If you anonymise or aggregate user data before using it for training, explain the process. Note that GDPR sets a high bar for true anonymisation — pseudonymisation alone is not sufficient.
Third-party model providers: If your AI tool uses a third-party model (OpenAI, Anthropic, Google, Mistral, etc.), disclose this and explain whether user data is shared with the model provider. Link to the provider’s data usage policies and clarify whether the provider uses API data for training (most enterprise/API agreements explicitly state they do not).

User Data Processing in AI Tools

Beyond training data, your privacy policy must describe how user data is processed in the normal operation of your AI tool.

Input Processing

When a user sends a prompt, uploads a document, or provides any input to your AI tool, describe:

How the input is transmitted and to which services
Whether the input is stored and for how long
Whether the input passes through intermediary services (embeddings, retrieval systems, prompt chains)
Whether inputs are logged for debugging, abuse prevention, or quality assurance

Output Processing

AI-generated outputs also require disclosure:

Whether outputs are stored and associated with user accounts
Whether outputs are reviewed by humans (for quality, safety, or moderation)
Whether outputs are used for further model improvement
Intellectual property considerations for AI-generated content

Conversation and Interaction History

Many AI tools maintain conversation history or interaction logs. Disclose:

How long conversation history is retained
Whether users can view, export, and delete their history
Whether history is used for personalisation or recommendations
How history is secured and who has access to it

AI-Specific Disclosures Your Privacy Policy Needs

Based on the current regulatory landscape across all major jurisdictions, here are the AI-specific sections your privacy policy must include:

1. AI System Description

Provide a clear, non-technical description of what your AI does. Explain the type of AI (generative, predictive, classification, recommendation), what it is designed to do, and its intended use cases. Users should understand what they are interacting with at a basic level.

2. Data Inputs and Outputs

Describe what data the AI system accepts as input, what it produces as output, and how both are processed, stored, and potentially shared. This is the core of your AI-specific privacy disclosure.

3. Training Data Practices

As detailed above, disclose what data was used for training, whether user data contributes to training, and how users can opt out.

4. Automated Decision-Making

If your AI makes or assists in decisions that have legal or similarly significant effects on users (such as content moderation, account suspension, pricing, recommendations that affect access to services), disclose this under GDPR Article 22. Explain:

The logic involved in the automated decision-making (in meaningful terms, not technical jargon)
The significance and envisaged consequences for the user
How users can request human review of automated decisions
How users can contest automated decisions

5. Third-Party AI Providers

If your product relies on third-party AI models or services, disclose:

The name or category of the AI provider
What data is shared with the provider
Where the provider processes data (especially relevant for EU–US transfers)
The provider’s data retention and usage policies
Whether the provider uses your users’ data for their own model training

6. AI Safety and Limitations

Transparency about AI limitations is increasingly expected by regulators. Your privacy policy or terms of service should acknowledge:

That AI outputs may be inaccurate, incomplete, or biased
That the AI system is not a substitute for professional advice (legal, medical, financial, etc.)
Known limitations of the system
How users can report problematic outputs

7. Consent for AI Processing

Under GDPR, processing personal data through AI systems requires a lawful basis. If you rely on consent, your privacy policy must explain:

What specific AI processing the user is consenting to
How consent can be withdrawn
What happens to previously processed data if consent is withdrawn
Whether the service can still be used without consent for AI processing

Transparency Requirements Across Jurisdictions

GDPR (EU/UK)

GDPR does not mention AI explicitly, but its principles of transparency, purpose limitation, and data minimisation apply directly. The European Data Protection Board (EDPB) and national supervisory authorities have issued guidance making clear that:

AI training on personal data requires a lawful basis (legitimate interest or consent)
Data subjects have the right to know if their data was used for AI training
The right to erasure extends to training data (the “right to be forgotten” in ML)
Data Protection Impact Assessments are required for high-risk AI processing

California (CCPA/CPRA + AB 2885)

California requires disclosure of AI interactions and has specific rules for automated decision-making technology (ADMT). Your privacy policy must disclose:

Use of AI or ADMT and the logic involved
Whether personal information is used for AI training
The right to opt out of AI-based profiling
The right to access information about automated decisions

Colorado AI Act (SB 24-205)

Colorado’s AI Act, with enforcement beginning in February 2026, requires deployers of high-risk AI systems to:

Notify consumers when AI is used for consequential decisions
Provide a description of the AI system and its purpose
Explain how consumers can contest AI-driven decisions
Complete impact assessments for high-risk AI deployments

Consent for AI-Generated Content

If your AI tool generates content (text, images, code, audio, video), your privacy policy should address the following consent and disclosure issues:

Ownership and licensing: Clarify who owns AI-generated outputs. Most AI tools grant users a licence to the outputs, but the specifics vary. This is typically covered in terms of service, but your privacy policy should reference it in the context of how output data is handled.
AI labelling requirements: Under the EU AI Act, AI-generated content that could be mistaken for human-created content must be labelled. If your tool generates such content, explain the labelling mechanism and the user’s responsibility for disclosure.
Content moderation and filtering: If your AI applies content filtering, safety guardrails, or moderation to outputs, disclose this. Users should know that their outputs may be modified, blocked, or flagged by automated systems.
User responsibility: Make clear that users are responsible for reviewing AI-generated content before publication or use, and that the AI tool does not guarantee accuracy, legality, or fitness for any particular purpose.

Common Privacy Policy Mistakes for AI Tools

No mention of AI at all. The most basic error. If your product uses AI, your privacy policy must say so. A generic SaaS privacy policy that makes no reference to AI or machine learning is non-compliant under the EU AI Act and multiple US state laws.
Vague training data disclosures. Saying “we may use your data to improve our services” is insufficient. You must specifically state whether user inputs are used for model training and how users can opt out.
Not disclosing third-party AI providers. If your “AI-powered” product sends user data to OpenAI, Anthropic, or another model provider, users have a right to know. This is a GDPR transparency requirement and an EU AI Act obligation.
Ignoring automated decision-making rights. If your AI makes decisions that affect users, GDPR Article 22 gives them the right to human review. Your policy must explain this right and how to exercise it.
Using a pre-2025 privacy policy template. AI privacy requirements are new. The EU AI Act transparency obligations, Colorado’s AI Act, and California’s updated ADMT rules all took effect in 2025–2026. Any template written before these laws existed will not cover them.
No data retention policy for AI interactions. Users expect to know how long their prompts, conversations, and outputs are stored. Indefinite retention without disclosure violates GDPR’s storage limitation principle.

Generate a Privacy Policy for Your AI Tool

AI privacy requirements are new, complex, and actively enforced. The EU AI Act, GDPR, CCPA, and state-level AI laws all require specific disclosures that standard privacy policy templates do not include. Drafting these clauses from scratch requires understanding regulations across multiple jurisdictions and the technical nuances of AI data processing.

LegalForge generates privacy policies built for AI tools and AI-powered products. Tell us what type of AI your product uses, whether you use third-party models, how user data is processed, and whether data is used for training — and we produce a comprehensive privacy policy covering the EU AI Act, GDPR, CCPA, Colorado AI Act, and all applicable regulations. One-time payment, no subscription, instant delivery.

Privacy Policy for AI Tools: EU AI Act, Training Data & Transparency Requirements (2026)