Let’s say your marketing team needs better deadline tracking for its social media campaigns. 

Instead of adapting the project management system the company already uses—the one with established workflows, integrations, and historical data—the marketing department goes all in on a new tool specifically “built for marketing teams.” 

Now marketing is entering deadlines in two places, client information lives in separate systems, and stakeholders can’t see the full picture of what’s actually happening for key initiatives. 

They’ve effectively reinvented the wheel. That’s the same thing that’s happening with privacy programs and AI governance.

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Where privacy and AI governance actually overlap

Many companies have already recognized the overlap between privacy and AI governance. That’s why so many privacy teams now own AI governance responsibilities. However, some are still building AI governance programs from scratch even as their privacy teams maintain data inventories, run risk assessments, manage vendor relationships, and handle transparency requirements.  

However, if there were a Venn diagram of privacy and AI governance, the overlap would be substantial.  

Data inventories are necessary foundations for both

Both privacy laws and AI regulations ask many of the same questions: what data do you have, where does it live, and how are you using it? 

Your privacy program’s data inventory is already answering this. It’s the cornerstone for General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) compliance, and it’s equally critical for AI governance. When Colorado’s AI Act requires deployers to document “the categories of data that the system processes as inputs and produces as outputs,” or when California’s proposed regulations require documenting the data used in automated decision-making systems, these are data inventory issues.  

Risk assessments follow similar frameworks

Depending on your regulatory obligations, both privacy impact assessments and AI impact assessments require similar strategies for understanding risk.  

  • Privacy impact assessments (PIAs): These assessments identify the personal data being processed, evaluate risks to individuals, and document the measures implemented to mitigate those risks. However, requirements vary between regulations. For example:
    • GDPR requires Data Protection Impact Assessments for processing that creates high risks to individuals’ rights and freedoms
    • In the United States, 17 out of the 19 states with privacy laws have some kind of privacy impact assessment requirement, with Utah and Iowa being the outliers.
      • Among the states that do require PIAs, differences hinge on which activities trigger a PIA. Some, like Virginia, Colorado, and Connecticut, specifically list activities while others, like California, use broader language around significant risk. States also vary on technical details, including submission timing and whether cure periods apply to violations.
  • AI impact assessments: These assessments evaluate how AI systems may produce discriminatory outcomes, identify safeguards in place to prevent bias, and outline the process for reviewing or appealing the system’s decisions. For example, the EU AI Act adds Fundamental Rights Impact Assessments for certain high-risk AI deployers, while Colorado’s AI Act requires impact assessments examining algorithmic discrimination.  

The key difference here is scope. 

  • Privacy assessments evaluate risks around how personal data is collected, used, shared, and protected under privacy laws. 
  • AI assessments expand this to include how systems make decisions: whether they’re fair and unbiased, how they reach conclusions, how accurate their outputs are, and whether humans can effectively oversee them. 

However, the core methodology—identifying risks, documenting mitigation measures, and maintaining records—remains the same

Transparency obligations are a necessary element

Privacy laws already require disclosures about data processing. For example, the GDPR mandates information about processing purposes and the legal basis, while the CCPA requires notice about data collection and use.

AI regulations introduce disclosure requirements regarding system involvement in decision-making. While the EU AI Act requires informing individuals when they interact with AI systems, Colorado requires disclosure before high-risk AI makes consequential decisions. Similarly, Utah’s AI Policy Act requires generative AI disclosure in regulated occupations such as healthcare or accounting. 

(And, as you might anticipate by now, more states are expected to add disclosure requirements in 2026.)

This means that businesses need to provide privacy notices explaining data collection and AI-specific disclosures about automated interactions; it’s a similar process, just different talking points. 

Automated decision-making needs to be factored in

Privacy laws already regulate automated decisions. GDPR Article 22 puts restrictions on solely automated decisions with legal or significant effects. 17 out of the 19 U.S. state privacy laws laws include opt-out rights for profiling in automated decision-making that produces legal or similarly significant effects. Minnesota goes even further, giving consumers the right to question automated decisions and request explanations.

AI regulations build on this foundation. The EU AI Act requires human oversight for high-risk systems, while Colorado’s AI Act mandates human review opportunities for consequential decisions. California’s proposed automated decision-making technology regulations also seek to expand beyond profiling to cover AI decision-making in broader contexts.

Where are the gaps between privacy and AI governance?

As a rule, anytime personal information is used in an AI system, the laws that govern that personal information are applicable. That creates a pretty significant overlap, but that doesn’t mean there aren’t gaps. Treating privacy and AI governance as perfectly identical processes can lead to governance missteps.

AI regulations impose requirements beyond privacy law. 

Privacy laws focus on how personal data is collected, used, and shared, but AI regulations add requirements that go beyond data handling data.

Take the EU AI Act, which requires technical documentation about model development, bias testing protocols, quality management systems, and post-market monitoring. Your privacy program might not need to go as deep for AI, though. 

(And, as mentioned above, Utah’s AI Act requires disclosure obligations that aren’t currently part of privacy laws, requiring regulated professions to proactively disclose generative AI use before interactions begin.)

Privacy law still applies even if AI regulations don’t cover it

Your AI system might not qualify as “high-risk” under the EU AI Act or Colorado’s AI Act, but if it processes personal data, privacy laws still apply in full. Individual rights—access, deletion, correction—don’t disappear because your AI falls into a lower risk category under AI-specific regulations

Sensitive data for debiasing needs are considered

The AI Act allows processing of special categories of personal data for bias detection and correction in high-risk systems, while GDPR generally restricts such processing. You may need to process sensitive personal data to comply with AI Act anti-discrimination requirements while navigating GDPR’s special category prohibitions. 

It’s a technical/legal balancing act that requires coordination between privacy and AI governance functions.

U.S. state variation multiplies complexity

Privacy policies in the US aren’t uniform, and they’re not straightforward. There’s a lot of complexity, and this can create confusion when trying to overlay AI governance with privacy programs. Just consider the following:

  • Different definitions of “high-risk” across states mean the same AI system might require impact assessments in Colorado but not trigger regulation elsewhere
  • Regulated professions like healthcare and accounting require AI-specific disclosures in Utah, while other states have no such sector-specific requirements HR departments may have additional complexity, with New York City’s Local Law 144 requiring bias audits for automated hiring tools
  • Disclosure timing varies. Some states require notification before AI involvement, others at the time of decision, others only upon request
  • The US’s 19 state privacy laws vary in their requirements. While some states line up (broadly or in certain areas), others have unique requirements that need to be carefully considered. 

Three steps to align your privacy program and AI governance  

So how do you actually build integrated governance without duplicating work? These three steps can help you start. 

Step 1: Map your existing privacy controls to AI governance requirements.

Start with your data inventory, but understand that identifying AI use is more complex than adding a column to a spreadsheet. You need to trace data flows through AI systems, which may require conversations with teams to understand which systems actually involve AI versus other automation.

Once you understand where AI intersects with data, look at your existing risk assessment process. These processes already evaluate risks to individuals. For AI systems, you’re adding specific questions: how might the system produce discriminatory outcomes? What bias testing has been conducted? How is model performance monitored over time?

The same approach applies to vendor management. Your existing vendor questionnaires cover data processing. For AI vendors, add questions about training data sources, model update frequency, and how the vendor addresses algorithmic transparency.

Step 2: Identify where AI-specific requirements exceed your current privacy program.

Your privacy program likely doesn’t include ongoing bias testing protocols. It probably doesn’t address model documentation requirements or conformity assessments. It may not contemplate the specific transparency requirements around AI-generated outputs versus human decisions.

Make a list. For high-risk AI systems under any applicable law—EU AI Act, Colorado, California’s proposed ADMT regulations—compare required elements against your current privacy program deliverables.  

Step 3: Build integrated workflows, not parallel compliance tracks.

Your AI governance program shouldn’t operate separately from your privacy operations. It should be an extension of existing data governance infrastructure, adding AI-specific controls where needed while leveraging shared foundations.

When your team assesses a new AI-powered customer service tool, the intake process should capture: data types processed, decision-making authority, individual rights mechanisms, human oversight protocols, security measures, and bias testing results. Many privacy risk assessment platforms now include AI governance modules that integrate these evaluations. 

Your existing data inventory can also act as an early warning system, helping spot when new AI tools enter the picture. For companies wanting a specific AI inventory, this is a great way to get started.

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