How to create an AI privacy policy
From chatbots to recommendation engines, artificial intelligence (AI) systems are becoming part of everyday products, and they rely on personal data to work well. This makes transparent, reliable documentation and notifications essential.
In this chapter, you’ll learn what an AI privacy policy is, why it matters, and how it helps you meet global regulatory requirements while building user trust. We walk you through the core elements to include, the laws that may apply, and the tools that can help you stay accurate and legally compliant as your AI features evolve.
At a glance
- An AI privacy policy explains how AI systems collect, use, and protect personal data, with a focus on transparency.
- These policies require broader disclosures than standard privacy policies, especially around training data and automated decision-making.
- Regulations such as the GDPR, EU AI Act, and CCPA/CPRA define requirements for consent, user rights, and AI risk classification.
- Core components include data sources, purposes of processing, legal basis, retention periods, third-party sharing, and user controls.
- Consent is central under many laws and is often managed through a consent management platform (CMP).
- A well-defined AI privacy policy supports regulatory compliance, strengthens user trust, and helps manage organizational risk.
What is an AI privacy policy?
An AI privacy policy explains how your AI systems collect, use, store, and protect personal data. It helps people understand what happens to their information when they interact with your site’s AI-powered features.
It should answer key questions, such as:
- What personal data does the AI system access or generate?
- Where does that data come from: directly from users, third-party sources, or training datasets?
- How can users control, review, or challenge automated decisions?
How AI privacy policies differ from standard privacy policies
A traditional privacy policy should describe data collection and processing at a high level. It covers forms, cookies, analytics tools, marketing communications, customer support, and similar activities.
An AI privacy policy (or AI-specific section) goes further. It provides extra transparency for any features powered by machine learning or large language models.
An effective policy will:
- Describe AI-specific data use: For example, how behavioral data feeds a recommendation engine, or how an AI assistant processes prompts and conversation history.
- Explain automated decision-making: Users should know when profiling or algorithmic decisions might affect them, such as credit scoring, fraud detection, or personalization that changes prices or offers.
- Clarify training vs. operational data: Many AI systems use data both to train models and to provide real-time responses. An AI privacy policy distinguishes these purposes and explains how long data is kept for each.
- Highlight safeguards: Clearly explain the technical and organizational measures that protect models and reduce bias.
Why AI systems need additional disclosure
AI models often work in ways that are complicated and hard for the average person to understand. Clear disclosure helps users understand how the system works and what choices they have:
- Explain, in plain language, how the system operates
- Make users aware of automated decision-making, including decisions with legal or other significant effects
- Describe any additional data the system may infer or generate
- Help users decide whether to use AI-powered features and how to exercise their rights
This clarity helps to strengthen user trust and support responsible, legally-compliant AI use.
4 reasons why businesses need an AI privacy policy
For organizations building or integrating AI, a dedicated privacy policy brings clarity to how data is used and helps teams manage AI responsibly as systems grow. Here are the key reasons it matters.
Support regulatory compliance
Global privacy laws like the EU GDPR and U.S. CCPA increasingly address automated decision-making, profiling, and AI-driven data use. An AI privacy policy documents how your system meets these legal obligations. Clear disclosure also reduces compliance risk and provides reliable documentation of how your AI systems handle data.
Strengthen transparency and user trust
People want to know how AI assistants and chatbots make decisions about them. When data use is explained in a direct, human way, it builds confidence and trust. This increases users’ willingness to adopt your AI-powered features.
Reduce risk and protect your organization
AI systems create new categories of operational and regulatory risk, including:
- Data misuse: Using training or behavioral data outside its intended purpose
- Bias and discrimination: When model outputs unfairly impact groups or individuals
- Security vulnerabilities: Model extraction, data poisoning, prompt injection, and other attack vectors unique to AI
- Noncompliance penalties: Fines and enforcement actions tied to automated decision-making, data minimization, or unlawful processing
A detailed AI privacy policy helps show how you mitigate these risks through safeguards like retention limits, access governance, consent management, and model monitoring.
Enable responsible scaling of AI features
A clear policy supports product development, compliance reviews, and coordination among data, legal, and engineering teams. It also provides a structure you can update as your AI systems evolve. This helps teams ship new AI features with greater consistency and confidence.
Key laws governing AI data privacy
AI systems operate within a fast-moving regulatory landscape. Existing global privacy laws still apply, and new data protection laws now address automated decision-making, governance, and AI-specific risks.
General Data Protection Regulation (GDPR): lawful basis, transparency, and automated decision-making
Under the GDPR, organizations must have a lawful basis for collecting and processing personal data used by artificial intelligence systems. This applies to both training and operational use cases.
Key obligations include:
- Lawful basis for processing: Consent, legitimate interest, contractual necessity, or another valid basis
- Data minimization: Only collect what’s necessary for the AI system to function
- Transparency: Users must understand how the system processes their data
- Automated decision-making restrictions: Art. 22 GDPR limits decisions with legal or significant effects (unless specific conditions are met)
- Right to explanations: Companies must provide meaningful information about the logic involved in automated decisions
Organizations that fall short of these obligations risk GDPR enforcement actions and substantial fines, particularly when processing lacks a lawful basis or transparency.
EU AI Act: risk classification, oversight, and documentation
The EU AI Act introduces a dedicated legal framework for AI use across EU Member States. It classifies AI systems into risk levels — unacceptable, high, limited, and minimal — and attaches obligations to each category.
For most businesses, the relevant requirements include:
- Risk classification: Determine whether your AI system falls into a high-risk category based on use case.
- Technical documentation: Maintain detailed records describing datasets, model behavior, testing, and intended use.
- Human oversight: Ensure people can monitor the system and intervene when needed.
- Transparency duties: Inform users when they are interacting with AI, including chatbots, virtual assistants, and automated content generators (generative AI).
- Data governance policies: Document data quality, fairness, and processes for detecting and mitigating bias.
California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA): consumer rights and opt-outs for AI profiling
In the United States, the CCPA/CPRA give California residents specific rights related to personal data and automated profiling. Across the U.S. states are increasingly passing new laws to regulate AI, or updating existing ones to address AI and data privacy.
Key requirements for AI-powered services include:
- Right to know what data is collected and how it’s used
- Right to opt out of automated decision-making and certain types of profiling
- Right to access, correct, or delete personal data used in AI systems
- Enhanced transparency when AI affects pricing, offers, recommendations, or eligibility decisions
Ignoring these rights can trigger enforcement under the CCPA, with statutory damages and other corrective measures applied when violations occur.
To help teams meet these obligations, we’ve outlined clear steps you can follow to achieve CCPA compliance and manage personal data responsibly.
OECD and ISO frameworks: global standards for responsible AI
Beyond legislation, many businesses follow global standards to support trustworthy and responsible AI development. While not legally binding, these frameworks shape regulators’ expectations and provide structure for organizations building privacy-aligned AI systems.
The most relevant leading frameworks are:
- OECD AI Principles: Focuses on fairness, transparency, accountability, and human-centric design
- ISO/IEC 42001: Provides detailed guidance on AI risk management, governance, quality control, and dataset documentation
What to include in an AI privacy policy
An effective AI privacy policy outlines how your AI systems use personal data. It should include the following core elements:
Your AI privacy policy
Data collection and sources
Describe what data your AI system collects and where it comes from: such as user inputs, behavioral data, device information, third-party datasets, and publicly available content. Clarify whether data is used for training, real-time operation, or both.
Purpose of processing
Explain why your system processes personal data. For example, is it for delivering recommendations, improving model performance, detecting fraud, or enabling conversational interactions?
Legal basis for processing
State the lawful basis for each processing activity, such as consent, legitimate interest, or contractual necessity, and link to your broader privacy policy where useful.
Automated decision-making disclosure
Explain when AI is used for profiling or automated decisions, whether the outcomes have legal or other significant effects, and how users can request human review.
Data retention and anonymization
Describe how long data is stored, how retention periods are determined, and whether personal data is anonymized, pseudonymized, or aggregated.
Data sharing and third parties
List the vendors and sub-processors that support your AI systems, such as cloud providers, API partners, analytics services, and model hosting platforms. Make it clear whether your vendors act as processors and only use personal data according to your instructions, and not for their own training, profiling, or other business purposes.
User rights and human intervention
Outline how users can access, correct, delete, or export their data; withdraw consent; object to profiling; or request human review.
Security and governance
Describe the technical and organizational measures that protect your AI systems, including encryption, access controls, auditing, fairness testing, and safeguards against model manipulation.
Contact information and policy updates
Provide contact details for your privacy team, your Data Protection Officer (DPO), your last update date, and how users will be informed of changes.
Example of an AI privacy policy
Below is a short example showing how a business might describe its AI-driven data use in a clear, user-first way. This example can be adapted for chatbots, recommendation engines, AI assistants, or internal automation tools.
AI Features and Data Use
Our AI features analyze user interactions, prompts, and service usage to deliver personalized recommendations, support responses, fraud detection, or other automated outputs. We may also use aggregated or anonymized data to train and improve our models.
To provide transparency, we document each AI-powered feature: including the data it uses, why it is processed, whether automated decision-making is involved, the legal basis (if relevant), and how long data is stored.
You can find this information in the AI Feature Data Use Table below.
Data categories and sensitivity
We do not process sensitive personal data (such as health, biometric, or financial information) unless it is necessary for a specific feature and you choose to provide it. When we do, we apply the appropriate legal basis and safeguards.
Data sources and third-party partners
Our AI systems may use data from your device, your interactions with our services, third-party providers, or publicly available sources. We only share personal data with vetted subprocessors who support infrastructure, analytics, or model hosting. These partners process data solely on our behalf and under strict contractual controls.
Automated decision-making and your choices
If an AI system contributes to an automated decision, you can opt out when applicable or request human review. You can also access, correct, delete, export, or withdraw consent for your personal data at any time.
Retention and security
We retain AI-related data only for as long as needed to provide the service, train our models responsibly, and meet legal obligations.
We apply strict technical and organizational measures to protect AI systems from unauthorized access, bias, and misuse.
Contact: For questions about how we use AI or how to exercise your privacy rights, please contact our privacy team using the details below.
AI Feature Data Use Table
Note: Organizations should replace the example features with their own AI systems.
| AI feature | Data used | Purpose of processing | Automated decision-making? | Legal basis | Retention | Human review? |
| AI recommendation engine | Interaction history, clicks, device data, preference signals | Deliver personalized recommendations and improve relevance | Yes (influences user experience) | Consent (GDPR Art.6(1)(a)) | 12 months (or as defined) | Yes |
| AI chatbot assistant | Prompts, messages, contextual metadata | Provide automated responses and improve support quality | No: suggestions only | Contractual necessity (GDPR Art. 6(1)(b)) | 6 months | Not applicable |
| Fraud detection model | Transaction data, login activity, behavioral patterns | Identify and prevent fraudulent activity | Yes: may temporarily restrict activity | Legitimate interest (GDPR Art. 6(1)(f)) | 24 months | Yes |
| Content moderation AI | Uploaded text, images, files | Detect prohibited content and maintain platform safety | Yes: may limit visibility | Legal obligation (GDPR Art. 6(1)(c)) or Legitimate interest (GDPR Art. 6(1)(f)) | 12 months | Yes |
Step-by-step: How to write an AI privacy policy
Creating an AI privacy policy is easier with a structured process. These steps help you document how your AI systems handle personal data and communicate that information clearly to users.
1. Identify where AI interacts with personal data
Map where personal data enters your AI systems: including user inputs, behavioral signals, training datasets, and any third-party tools. A full data mapping exercise can help you understand how each data category flows through your models.
2. Assess the AI system’s risk level
Use the EU AI Act’s risk categories as a reference point and match safeguards to the level of risk. Higher-risk features require stronger oversight, documentation, and transparency.
3. Create disclosures for each data type and purpose
For every data category, outline what’s collected, why, how it’s used, who has access to it, how long it’s stored for, and whether it contributes to automated decisions. Keep explanations clear, factual, and user-focused.
4. Review with legal and data protection teams
Have your organization’s legal leads and data privacy experts check compliance with the GDPR, CCPA/CPRA, EU AI Act, and other relevant regulations, along with your data minimization, fairness, and user rights workflows.
5. Generate your policy using trusted tools
A privacy policy generator helps you produce accurate, consistent disclosures and update them as your AI features evolve.
6. Link your AI privacy policy to all AI-powered features
Make the policy easy to find in your main privacy policy, product pages, and AI interfaces. When AI relies on tracking technologies, you should also add a link in your cookie and consent banners.
AI privacy and user consent
AI features often rely on personal data to work effectively. When that data can be linked to individuals, consent becomes a core part of responsible AI governance. A clear consent framework helps people understand what’s happening, decide what they’re comfortable with, and stay in control of how their data is used.
Why consent matters for AI
Many AI assistants and recommendation engines use cookies, device identifiers, and interaction history to personalize results and improve accuracy. When these technologies involve personal data, they fall under laws like the GDPR and CCPA. Clear disclosure and real-time consent controls are essential.
What the law requires
The GDPR requires informed, specific consent for personal data processing and explicit consent for sensitive data. Users must also be able to withdraw consent or object to profiling and automated decision-making.
The EU AI Act builds on this by adding transparency notice and human oversight requirements for certain AI use cases. Your AI privacy policy should make these rights easy to understand and simple for users to exercise.
How to operationalize it with a consent management platform (CMP)
A consent management platform (CMP) helps you manage these obligations reliably. It enables signaling and application of user choices consistently across websites, apps, and AI-driven features, synchronizes permissions with backend systems, and maintains audit trails for compliance.
If your AI relies on tracking technologies, a CMP can help provide the clarity and control users expect, and help your organization operate AI responsibly.
Tools to automate AI privacy compliance
As AI systems evolve, manual documentation and consent management become difficult to maintain. Automated tools and compliance audit software can help teams stay accurate and consistent by supporting documentation, consent workflows, model governance, and ongoing monitoring.
The tools below help streamline these processes so your teams can manage AI-related data use reliably at scale.
Usercentrics CMP
The Usercentrics Consent Management Platform (CMP) brings consent, preference, and AI-related data controls into one reliable workflow. It applies user choices consistently across your digital products and synchronizes permissions with backend systems in real time.
Built on a privacy-first architecture, the Usercentrics CMP provides audit-ready records, region-specific consent experiences, and seamless integration with AI features that depend on cookies, device data, or behavioral signals. This helps you operate AI responsibly while keeping user trust at the center of your data strategy.
AI model documentation and audit logs
Accurate model documentation is essential for AI governance. Automated logging tools such as MLflow, Weights & Biases, Neptune.ai, DVC, Kubeflow, or platform-native services like AWS SageMaker, Google Vertex AI, and Azure Machine Learning help track training data sources, model versions, testing results, decision pathways, and access events.
These records support audits, compliance reviews, and internal quality monitoring, and give your teams a clear, traceable view of how each model works and evolves.
Data lineage and transparency tools
Data lineage tools show how personal data moves through your AI systems, from origin to transformation to retention. Platforms such as Apache Atlas, OpenLineage, Collibra, Alation, Informatica, and cloud-native services like Google Cloud Data Catalog or AWS Glue help teams map data flows, track dependencies, and document transformations.
This visibility supports GDPR data minimization, enables clearer risk assessments, and helps you create transparent, user-facing disclosures.
Best practices for responsible AI privacy
Using artificial intelligence responsibly means you’ll need accurate documentation of your data processing activities, reliable governance, and ongoing review. These practices help you to protect user rights, reduce operational risk, and maintain long-term compliance.
Use anonymized or synthetic data when possible
Limit personal data exposure by training and testing models with anonymized datasets, pseudonymized identifiers, or synthetic data. This supports GDPR data minimization and reduces the impact of potential security breaches.
Implement bias detection and fairness testing
Regularly evaluate models to prevent biased or discriminatory outcomes. This includes pre-deployment testing, monitoring for drift, reviewing training data quality, and incorporating diverse datasets when appropriate. Documenting these steps will help you to demonstrate responsible marketing data governance.
Maintain human oversight for automated decisions
Human involvement is essential for AI systems that influence pricing, eligibility, hiring, credit, or safety-related decisions. Your AI privacy policy should clearly explain when human review is available and how users can request it.
Review your AI data governance regularly
As AI systems evolve, your governance program should evolve with them. Regular reviews help you update data categories, refine consent flows, strengthen safeguards, document new model behavior, and align with any changes to relevant regulations.