AI is reshaping how organizations operate, from predictive analytics and customer segmentation to automated decision-making and personalized experiences. But as AI systems increasingly rely on personal data, they raise complex privacy and compliance challenges.
This blog explores how privacy laws apply to AI, including:
- AI-specific legislation and its implications
- How general privacy laws govern AI activities
- Regulator-issued guidance to support compliance
- Practical steps organizations can take
- The strategic role of consent and preference management
Understanding these areas is essential for any organization deploying AI responsibly and compliantly.
AI-specific laws and emerging regulations
While most jurisdictions still rely on general privacy laws to regulate AI, a growing number are introducing AI-specific legislation. These laws aim to address the unique risks posed by AI systems, such as opacity, bias, and automated decision-making.
In the United States, federal regulation remains limited, leaning towards supporting innovation and less towards imposing restrictions, but several states have taken the lead. California has enacted eighteen AI-related laws covering transparency, training data disclosures, deepfake restrictions, and sector-specific rules for healthcare and education. Colorado’s legislation focuses on preventing algorithmic discrimination, while Maryland, Texas, and New York have also passed laws targeting AI accountability.
The European Union’s Artificial Intelligence Act is the most advanced regulatory framework to date. It categorizes AI systems by risk level: unacceptable, high, limited, and minimal. Each category carries different obligations. High-risk systems must meet strict requirements around transparency, human oversight, and data governance. The Act also prohibits certain AI uses outright, such as biometric categorization and exploitation of vulnerable groups.
As of August 2, 2025, the Act began applying to general-purpose AI models with systemic risk. This signals a shift toward broader accountability for foundational AI technologies.
Applying general privacy laws to AI use
Even in the absence of AI-specific laws, most jurisdictions apply existing privacy legislation to AI systems that process personal data. This creates a complex compliance landscape where organizations must interpret traditional privacy principles in the context of emerging technologies.
Legal basis for processing
In regions like the EU and Brazil, organizations must establish a valid legal basis for processing personal data. This applies to both AI training datasets and deployment activities. If consent is used, it must meet high standards of clarity and granularity. If legitimate interest is claimed, organizations may need to conduct a Legitimate Interest Assessment (LIA) or Data Protection Impact Assessment (DPIA) to demonstrate compliance.
Automated decision-making
AI systems that make decisions about individuals, such as credit scoring, hiring, or insurance pricing, may trigger additional obligations. Under laws like the GDPR, individuals have the right to meaningful information about the logic involved, as well as the right to contest decisions. Organizations must assess whether their AI systems fall under these provisions and implement safeguards accordingly.
Transparency requirements
Transparency is a cornerstone of privacy compliance, but it can be difficult to achieve in AI contexts. Data subjects may not be aware that their information is being used to train models, especially if it was collected for unrelated purposes. Organizations must find ways to communicate clearly about AI data use, including the nature of processing, the source of data, and the impact on individuals.
Regulator guidance on AI and privacy
Regulators are increasingly issuing guidance to help organizations navigate the intersection of AI and privacy law. These resources often fill gaps where legislation is still evolving.
- South Korea’s Personal Information Protection Commission issued a set of guidelines and a checklist for AI developers/deployers.
- The EU Commission issued a voluntary set of guidelines to help companies comply with the required general purpose AI model obligations of the AI Act.
- The United Kingdom’s Information Commissioner’s Office (ICO) published an AI and data protection risk toolkit, along with additional guidance for applying purpose limitation and individual rights requirements to AI.
- Canada’s Office of the Privacy Commissioner released principles specific to generative AI and privacy protection.
Practical steps to compliance
To manage AI-related privacy risks effectively, organizations should take a structured approach:
- Identify applicable AI-specific laws: Understand which jurisdictions have enacted AI legislation and assess how those laws apply to your use cases.
- Review regulator guidance: Use official toolkits and checklists to align internal practices with regulatory expectations.
- Map general privacy laws to AI activities: Evaluate how existing privacy obligations, such as legal basis, transparency, and data subject rights, apply to AI systems.
- Develop internal governance frameworks: Create policies that integrate privacy-by-design, data minimization, retention schedules, and security controls.
- Establish a review process: Monitor new AI deployments and regulatory developments to ensure ongoing compliance.
These steps help organizations build a scalable and defensible approach to AI governance.
Consent and preference management in AI compliance
Consent and preference management are critical components of AI privacy compliance, ensuring that individuals retain control over how their data is used, even in complex AI environments.
When AI systems rely on personal data, especially for profiling or automated decision-making, organizations must obtain valid consent. This includes:
- Clear explanation of how data will be used in AI training and deployment
- Granular options for different types of processing
- Easy mechanisms for withdrawal or modification
Consent must reflect informed, ongoing engagement and be supported by transparent communication.
By integrating consent and preference management into AI workflows, organizations can:
- Reduce regulatory risk
- Improve customer trust
- Align AI innovation with ethical standards
Building a privacy-aware AI strategy
AI offers transformative potential, but it must be deployed with care. Organizations need to understand the legal landscape, apply privacy principles to AI systems, and stay ahead of regulatory developments.
By embedding privacy into every stage of AI development and deployment, organizations can unlock value while protecting individuals and maintaining trust.
