The implementation of artificial intelligence (AI) is rapidly evolving across different industries. AI models are routinely used in business operations and rely on huge amounts of data to function effectively. This data often includes personal or even sensitive information, that is protected by data privacy laws such as GDPR, CPRA, etc.
While a few AI laws exist globally, over 120 countries have privacy laws regulating how AI algorithms can manage personal data. Thus, companies must adapt to integrate the best AI-related data practices to maintain trust, transparency, and legal compliance.
Read this blog post to learn about the best practices for AI data governance.
What Is AI Data Governance?
AI data governance is a system of policies, frameworks, practices, and technologies used to implement AI technologies responsibly to collect and manage data in compliance with privacy laws at international and national levels.
AI data governance goes beyond traditional data governance by addressing AI-specific risks such as generative AI applications, algorithmic decision-making, and possible bias or dynamic data reuse.
Why AI Data Governance Matters in Consent Management?
AI systems are used for personalization, profiling, and automation of processes that contain personal data of customers. People require more control over how their data is used.
Regulations such as the EU AI Act, GDPR, CPRA, and Canada’s Artificial Intelligence and Data Act (AIDA) demand clear documentation, purpose limitation, risk assessments, and rights to human intervention in decision making.
Implementing AI data governance is no longer just good practice— it’s becoming a legal obligation. The requirements are also increasing. It’s no longer enough to get consent for cookies; organizations must now disclose if and how they use personal data for AI models.
Effective AI data governance helps mitigate risks associated with data breaches, biases in AI models, and misuse of sensitive information.
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Key Principles of AI Data Governance
Key principles of AI data governance include:
- Accountability
Organizations should assign a responsible person to monitor AI model behavior and data integrity and identify and mitigate potential biases, errors, and unintended consequences from AI systems. The accountable person must ensure that the legal and ethical responsibilities are clearly defined and maintained. - Transparency
Organizations must clearly document AI systems' data collection and usage processes. - Consent Management
Organizations must obtain user consent for data usage in AI systems, especially when involving personal data. - Auditability
AI systems should allow interested third parties to understand and review the decisions made by the algorithms. This means that AI systems should be designed to allow independent third parties to inspect and evaluate their operations and decisions. - Explainability
AI systems should be designed in such a way that the relationships between input data and decisions should be understandable. It means that the decision making of the AI systems should be clear. - Fairness
AI systems should be designed to treat all individuals fairly and not discriminate against specific characteristics. - Robustness
AI systems should withstand tampering and manipulation and function reliably and effectively under unexpected and challenging conditions. - Ethical use
Organizations must ensure that AI systems align with social, legal, and ethical requirements and expectations. - Security
AI systems and related infrastructure must ensure data security, confidentiality, integrity, and protection against data loss, intervention, or fraud. - Reproducibility
AI results should be reproducible, meaning that other AI systems should replicate outcomes given the same inputs and conditions. - Human Oversight
Humans should remain in control of data and AI-based decisions. Humans should be able to intervene, oversee, and correct decisions made by AI systems.
References:
- Ethics Guidelines for Trustworthy AI by European Commission.
- IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems.
- UNESCO Recommendation on the Ethics of Artificial Intelligence.
- OECD AI Principles.
Addressing AI In Your Privacy Policy
If your business uses AI for data processing activities you must mention it in your Privacy Policy to inform your consumers and ensure legal compliance.
Inform users if you share any user personal data with third parties or services and list these third parties in your Privacy Policy. Be transparent in your Privacy Policy about whether you use data to train your AI system and disclose what AI services you use. Explain how and why you share their data with external AI services. If a third party hosts the algorithm, this may be considered sharing personal data.
Add a separate chapter to your Privacy Policy explaining what data you input, what AI systems you use, and how decisions are made.
Inform users how they can opt out of AI-based decision-making and implement other user rights such as right for data correction or deletion.
Need a Privacy Policy? CookieScript Privacy Policy Generator can automatically create a unique and up-to-date Privacy Policy for you, which includes AI data governance.
AI Data Governance Compliance Checklist
Read this checklist to assess your organization's readiness for AI data governance and regulatory compliance:
- Privacy Policy
Mention in your Privacy Policy how your business uses AI in relation to your data processing activities.
Write your Privacy Policy in clear language and make it easily available. - Data collection & consent
Clearly inform users that their data may be used for AI training or profiling.
Obtain explicit consent before using personal data for AI systems.
Provide a granular opt-in and opt-out mechanism for AI-specific data processing. - Purpose limitation & minimization
Limit AI usage to the specific purposes disclosed in your Privacy Policy.
Implement data minimization practices by using only the data necessary for the AI system. - Transparency
Document all data sources used for AI systems’ input and make them accessible to relevant stakeholders.
Explain how your organization uses AI for data collection and management. - Data quality & consistency
Clearly define standards for data quality, ensuring that AI systems work with trustworthy data.
Implement a system for managing data updates, corrections, and inconsistencies. - Fairness and robustness
Ensure that AI systems treat all individuals fairly and not discriminate against specific characteristics.
Test your AI systems against tampering and manipulation under unexpected and challenging conditions. - User rights
Implement a system to execute user rights such as right to be informed, right for data correction, etc.
Provide a mechanism for users to opt out of automated decision-making.
Implement a mechanism for human review of sensitive decisions made by AI. - Security & data retention
Ensure data security, confidentiality, integrity, and protection against data loss, intervention, or fraud.
Regularly review datasets used as AI input and delete data when no longer needed. - Vendor & third-party compliance
Ensure that AI vendors include data governance and comply with privacy laws.
Sigh contracts with any third parties using AI data on your behalf.
Regularly audit third-party AI models or datasets for safety and compliance. - Data Protection Impact Assessment
Regularly conduct a Data Protection Impact Assessment (DPIA)
Identify, monitor, and document AI applications involving sensitive data. - Mitigate legal & compliance risks
Establish processes for regular data audits.
Monitoring data usage and compliance.
Keep a log of compliance with privacy logs.
How to Implement AI Data Governance?
Generative AI applications require effective and up-to-date AI data governance. Use these steps to implement AI data governance:
- Establish a clear data governance framework
The framework must define roles, responsibilities, and processes for managing AI data. - Train staff
Educated staff that understands data ethics and governance principles is essential for implementing AI data governance effectively. - Use dedicated technologies
Organizations should use adequate technologies and tools that enable data tracking, quality control, and compliance monitoring. - Use a Consent Management Platform
A professional CMP is a must to implement cookie consent mechanism and comply with privacy laws.
The Role of CMPs in AI Governance
One of the key tools for implementation of AI Data Governance is a Consent Management Platform (CMP).
Features such as Privacy Policy Generator, dynamic consent mechanisms with the options to grant and revoke consent, automated DPIA execution, geo-targeting, and AI-related data usage disclosures can help organization to implement AI data governance and achieve compliance.
CookieScript CMP is a professional and easy-to-use tool and one of the best options for implementing AI Data Governance with the following features needed to implement AI Data Governance:
- The Cookie Consent mechanism with the options to grant and revoke cookie consent.
- Highly customizable Cookie Banner.
- Automatic Cookie Scanner.
- Privacy Policy Generator.
- geo-targeting functionality for adapting the banner to the required jurisdiction.
- Google Consent Mode v2 integration
- IAB TCF v2.2 integration
- Multiple languages
- Multiple integrations
- Compliance hints.
In 2024, users ranked CookieScript CMP on G2, a peer-reviewed website, as the best CMP for small and medium-sized companies.
Frequently Asked Questions
What is AI data governance?
AI data governance is a system of policies, frameworks, practices and technologies used to implement AI technologies responsibly to collect and manage data in compliance with privacy laws at international and national levels. The best way to achieve compliance with AI data governance is to use a Consent Management Platform such as CookieScript CMP.
How to implement AI data governance?
To implement AI data governance in your business, establish a clear data governance framework, train staff, and use dedicated technologies and a Consent Management Platform such as CookieScript.
What are the key principles of AI data governance?
Key principles of AI data governance include accountability, transparency, consent management, auditability, explainability, fairness, robustness, ethical use, security, reproducibility, and human oversight. Use CookieScript CMP to implement AI data governance in your company.
How does AI data governance differ from traditional data governance?
AI data governance concentrates on data privacy and features of AI systems, such as algorithmic transparency and accountability, decision-making processes, and the potential for bias. Due to AI models' self-learning nature, AI data governance demands continuous monitoring and updating of governance policies. Use CookieScript CMP to implement AI data governance.
How can AI be used in data governance?
AI-based data governance uses artificial intelligence to automate and enhance data management processes such as data classification, update, quality control, security checks, compliance monitoring, and policy enforcement. Use CookieScript CMP to implement AI data governance.
Do I need to mention AI in my Privacy Policy?
Yes, if your business uses AI for data processing activities, you must mention it in your privacy policy to inform your consumers and ensure legal compliance. You should also inform users if you share any user personal data with third parties or services and list these third parties in your Privacy Policy. If a third party hosts the AI algorithm, this may be considered sharing personal data. Use CookieScript CMP to create a Privacy Policy for your website or app.