Impact Assessments for Generative AI: DPIAs, AIA Requirements, and Templates

Generative AI is changing how businesses operate - from drafting emails to analyzing medical records. But with great power comes great responsibility, and regulators aren’t waiting around. If you’re using generative AI in the EU, UK, or anywhere that follows GDPR or the AI Act, you need to do an impact assessment. Not just any assessment. A proper one. And if you skip it, you could be looking at fines up to €1.2 million - or more.

Why DPIAs for Generative AI Are No Longer Optional

Data Protection Impact Assessments (DPIAs) aren’t new. They’ve been part of GDPR since 2018. But generative AI? That’s the wildcard. Unlike traditional systems that follow fixed rules, generative AI learns from massive datasets, creates new content, and sometimes spits out personal data it shouldn’t even know exists. Think of it like a student who memorized confidential files during training and now repeats them in exams.

The European Data Protection Board (EDPB) says you need a DPIA if your AI system does any of these:

Profiles people systematically - like scoring job applicants or predicting employee performance
Processes large volumes of sensitive data - health records, racial info, sexual orientation
Monitors public spaces at scale - think facial recognition in offices or retail stores

And here’s the kicker: if your system hits even two of the EDPB’s nine high-risk criteria, you’re legally required to do a DPIA. For generative AI, that’s almost always true. Why? Because these models are innovative, use scoring, and often involve personal data without clear consent.

CNIL, France’s data watchdog, says it plainly: if you’re building or using a foundation model - like ChatGPT or Gemini - and it’s trained on personal data, you need a DPIA. Period.

DPIA vs. FRIA: What’s the Difference?

The EU AI Act, which took effect in August 2024, added another layer: the Fundamental Rights Impact Assessment (FRIA). Now you don’t just need to protect data - you need to protect rights.

A DPIA asks: Are we handling personal data legally and securely?

A FRIA asks: Is this AI system fair? Transparent? Non-discriminatory? Does it respect human dignity?

They’re not the same. A DPIA might catch that your AI is using employee emails to train a model. A FRIA will ask: Is it right to use performance data to fire someone without human review?

TechGDPR.com found that 87% of European companies now run both assessments for high-risk generative AI. That’s not because they’re being overly cautious - it’s because the law demands it. The average combined assessment takes 127 hours. That’s over three full workweeks. But skipping either? That’s where the fines start piling up.

What Must Be in Your DPIA Template

A generic DPIA won’t cut it anymore. You need one built for AI. The ICO’s official template (version 4.1, March 2023) now includes specific fields for:

How the AI makes decisions
What data was used to train it
How users can challenge or correct outputs

CNIL’s updated template (July 2024) goes even further. It requires you to answer:

What percentage of your training data includes personal information?
How do you prevent the AI from generating fake personal data (like names, addresses, or SSNs)?
Can users request deletion of outputs that contain their data?

Google Cloud’s template (June 2024) adds metrics: how much synthetic data you used to reduce real-person exposure, and how well your data minimization strategy worked. Early users say these templates cut assessment time by nearly 30%.

Don’t just copy a template. Customize it. Most organizations now stitch together pieces from the ICO, CNIL, and EDPS templates. The average generative AI DPIA is now 47 pages long - up from 32 in 2023. That’s not bloat. That’s due diligence.

A pyramid-like legal document with symbols of fairness, security, and bias, under a looming red X.

Who Needs to Be Involved?

This isn’t a task for the IT team alone. You need:

Your Data Protection Officer (DPO) - legally required by CNIL and GDPR
Legal counsel - to interpret AI Act obligations
AI engineers - to explain how the model works, not just what it does
HR or compliance - if you’re using AI for hiring, promotions, or performance reviews

And here’s what most companies miss: you need to involve the people affected. Not just consult them - listen to them. If your AI is used in customer service, talk to frontline staff. If it’s used in hiring, talk to applicants. The ICO says ignoring human feedback is a red flag for regulators.

When Do You Need to Start?

The EU AI Act’s deadlines are rolling out. Here’s what matters right now:

February 2, 2025: All high-risk AI systems (including recruitment, credit scoring, and healthcare tools) must have completed DPIAs and FRIAs.
June 30, 2025: General-purpose AI models - like those powering chatbots or image generators - must also undergo assessments.
January 1, 2026: Enforcement begins. Regulators are already auditing. CNIL reported 78% of AI violations from 2021-2023 could’ve been avoided with proper assessments.

Gartner predicts that by 2026, 92% of enterprise generative AI deployments will require formal DPIAs - up from 67% today. And while costs are dropping (from $18,500 to $14,200 per assessment), enforcement actions are expected to rise 40%.

Deconstructed AI model with training data, synthetic outputs, and human intervention in abstract planes.

What Happens If You Don’t Do It?

The ICO’s 2024 enforcement report shows that companies skipping DPIAs for AI systems face average fines of €1.2 million. That’s not a penalty. That’s 2.8% of global annual turnover - the maximum under GDPR Article 83(5).

In Q2 2024, three European employers were fined for using AI to monitor employee productivity without a DPIA. One company automated performance reviews that flagged workers for “low engagement.” The AI flagged people based on how often they took breaks - even if they were working remotely or had medical conditions. No one checked if the system was fair. No one did a DPIA. Result? Fines, lawsuits, and reputational damage.

It’s not just about money. It’s about trust. Customers won’t use your product if they think it’s spying on them. Employees won’t stay if they feel judged by an algorithm.

How to Get Started Today

You don’t need to wait for a regulator to knock on your door. Start now:

Screen: Does your generative AI process personal data? If yes, move to step two.
Describe: Write down exactly what the AI does, what data it uses, and who it affects.
Assess: Is it high-risk? Use the EDPB’s nine criteria. If two apply, you need a DPIA.
Identify risks: What could go wrong? Data leaks? Biased outcomes? False information?
Fix it: Add safeguards - human review, data minimization, synthetic data, opt-outs.
Sign off: Get your DPO and legal team to approve. If risks remain high, contact your data authority before deploying.

Use the ICO, CNIL, or EDPS templates as your base. Don’t reinvent the wheel. But do fill it with your own details. Generic answers get flagged. Specific answers get approved.

What’s Coming Next?

The EDPB is working on harmonized criteria for generative AI assessments, with public consultation set for March 2025. The ICO plans to release updated guidance in Q2 2025, focusing on data leakage risks from large language models.

The trend is clear: AI assessments are getting more detailed, more frequent, and more enforced. The companies that treat this as a compliance checkbox are going to get burned. The ones that treat it as part of building trustworthy AI? They’ll lead the market.

If you’re using generative AI today, you’re already in the regulatory spotlight. The question isn’t whether you need an assessment. It’s whether you’ve done it right.

Do I need a DPIA if I’m using a third-party generative AI tool like ChatGPT?

Yes - if you’re using it to process personal data. Even if OpenAI built the model, you’re still the data controller under GDPR. If you input employee emails, customer health records, or financial data into ChatGPT, you’re responsible for ensuring the processing is lawful. That means a DPIA. The CNIL and ICO both confirm this. Using a third-party tool doesn’t absolve you of responsibility.

Can I skip the DPIA if I use synthetic data?

Not necessarily. Synthetic data reduces risk, but it doesn’t eliminate it. If your synthetic data was generated from real personal data - and if the AI can still reconstruct or leak that data - regulators will still require a DPIA. Google’s template now includes a metric for synthetic data usage percentage because even synthetic outputs can carry bias or traces of originals. The goal isn’t to avoid the DPIA - it’s to prove you’ve minimized harm.

What if my AI system is only used internally?

It doesn’t matter. GDPR applies to any processing of personal data - whether it’s for customers, employees, or internal operations. If your AI analyzes employee performance, tracks attendance, or predicts turnover, you’re processing personal data. That triggers a DPIA. CNIL fined a French company in 2023 for using AI to monitor internal communications without a DPIA, even though no customers were involved.

How often should I update my DPIA?

At least once a year - or whenever there’s a significant change. That includes updating the model, adding new data sources, changing how outputs are used, or scaling to more users. The ICO says AI systems evolve fast. A DPIA done in January might be outdated by June. Treat it like a living document, not a one-time form.

Do small businesses need to do DPIAs too?

Yes - size doesn’t matter. GDPR applies to all organizations, regardless of size. If a small startup uses AI to screen job applicants or analyze customer sentiment, and it processes personal data, they need a DPIA. The ICO has fined small businesses before for AI-related violations. The only exception is if your processing is very low-risk and clearly doesn’t meet any of the three triggers under Article 35(3). Most generative AI uses don’t qualify.

What’s the biggest mistake companies make with AI DPIAs?

Treating it as a paperwork exercise. The most common failure? Writing a DPIA that says, “We use AI,” but doesn’t explain how it works, what data it uses, or how risks are actually managed. Regulators don’t care about polished PDFs. They care about evidence: logs, test results, human review records, user feedback. If you can’t show you’ve done more than copy a template, you’re not compliant - you’re just pretending.