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Privacy compliance for AI companies building the future

EU AI Act, GDPR, training data governance, model risk, and enterprise customers asking hard questions. We handle all of it.

By Julian Gage Last reviewed: 7 February 2026

AI companies face a unique intersection of the EU AI Act, GDPR, and commercial pressure from enterprise buyers who want to see mature AI governance before signing. We provide fractional DPO services built specifically for this intersection. Note: outsourced DPOs are also referred to as external DPO, virtual DPO, fractional DPO, or DPaaS.

Key takeaways

  • The EU AI Act is now in force with obligations phasing in through 2027. If your product uses AI, you have new compliance requirements.
  • GDPR already regulates AI through automated decision-making rights, DPIAs, and data minimization. The AI Act adds obligations on top.
  • Enterprise buyers increasingly require evidence of AI governance before signing contracts.
  • Your DPO has led AI compliance programs across 100+ organizations at the forefront of AI development.

Why AI company privacy is different

AI companies face compliance challenges that most privacy providers don’t fully understand: training data provenance, model risk assessment, automated decision-making under GDPR, AI Act classification, and the tension between data minimization and model quality.

Most DPO providers can handle GDPR basics. Few understand how GDPR applies to training pipelines, how to assess whether your AI system is “high risk” under the EU AI Act, or how to build AI governance frameworks that satisfy both regulators and enterprise buyers.

We’ve built privacy and AI governance programs at companies at the forefront of AI development. We understand the specific challenges of LLMs, computer vision, NLP, automated decisioning, and generative AI from both a regulatory and practical perspective.

How high-risk classification works under the EU AI Act

Most of the EU AI Act’s heavier obligations attach to systems classified as high-risk, and a system reaches that tier by one of two routes:

  • Annex I (product safety). The AI system is itself a product, or a safety component of a product, that is already covered by the EU product-safety laws listed in Annex I (for example medical devices, in-vitro diagnostics, machinery, or toys) and that product is required to undergo third-party conformity assessment.
  • Annex III (listed use cases). The AI system is used in one of the specific areas listed in Annex III, such as biometrics, critical infrastructure, education, employment and worker management, access to essential services including credit and insurance, law enforcement, migration and border control, or the administration of justice.

If your system falls under either route it is high-risk, and the technical documentation, risk management, human oversight, transparency, and post-market monitoring obligations apply. We start every AI engagement with this classification so you build only what your systems actually trigger. See our EU AI Act compliance services and the high-risk classification guide for the full framework.

What we handle for AI companies

  • DPO appointment and notification to the supervisory authority (where applicable)
  • EU AI Act risk classification and compliance roadmap
  • AI-specific DPIAs for training data, model outputs, and automated decisions
  • Training data governance: provenance, lawful-basis and transparency assessments
  • GDPR automated decision-making compliance (Article 22)
  • AI governance frameworks: policies, accountability structures, human oversight mechanisms
  • Transparency implementation: user-facing disclosures for AI-generated content and chatbots
  • Data quality and bias assessments
  • Enterprise deal support: AI governance documentation that satisfies procurement teams
  • Investor due diligence for AI-specific privacy and ethics questions
  • Continuity for AI governance: we handle breach response and regulator inquiries directly, with named partner-bench backfill for lead DPO absence and enterprise procurement support. Cleared senior practitioners cover absences with a 4-hour response SLA.

Common AI company compliance scenarios

LLM and generative AI companies need training data provenance assessments, GDPR legal basis analysis for web-scraped or user-contributed data, transparency obligations for AI-generated content, and enterprise AI governance documentation.

Computer vision companies processing biometric data (facial recognition, gait analysis) face GDPR special category data requirements and may fall under prohibited AI Act use cases (such as real-time biometric identification in public spaces) depending on the specific application.

Automated decisioning platforms (credit scoring, fraud detection, hiring tools) face GDPR Article 22 automated decision-making requirements plus “high risk” classification under the EU AI Act.

AI-as-a-Service providers need robust DPA frameworks, clear data processing boundaries (controller vs processor), and transparency about how customer data interacts with model training.

Regulations

EU AI Act (risk classification, transparency, documentation), GDPR (automated decisions, DPIAs, data minimization, training data legal basis), UK GDPR, CCPA/CPRA and other US state privacy laws (Virginia, Colorado, Texas, and more), and emerging AI-specific regulations across 30+ jurisdictions worldwide, including Canada, Brazil, and China, with local counsel support where required. These rules apply wherever your company is based, to any company serving people in the EU or UK, not only European companies.

Investment

Most AI companies start with DPO Essentials (From €2,000 per month) or DPO Premium (From €5,000 per month) for companies with complex AI systems or multi-jurisdictional requirements. For standalone AI compliance projects, we offer project-based pricing. See our DPO Cost Guide.

  • Same-business-day response
  • Professional indemnity and cyber insurance
  • Named DPO notified to the supervisory authority
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FAQ

Frequently asked questions

Does the EU AI Act apply to my AI company?

If your AI systems are used in the EU or affect people in the EU, yes. The classification depends on what your AI does: high-risk (employment, healthcare, credit), limited risk (chatbots, AI-generated or manipulated content requiring disclosure), or minimal risk (internal analytics). Some AI companies have systems spanning multiple categories. See our AI Compliance page for the full framework.

Do I need a DPO if I'm an AI company?

If you process personal data at scale (including in training data) or systematically monitor individuals, you may be legally required to appoint one. Even without a legal requirement, enterprise buyers and investors increasingly expect AI companies to have formal privacy governance. See Do I Need a DPO?.

How does GDPR apply to training data?

GDPR applies to any personal data used in training, including web-scraped data, user-contributed data, and labeled datasets. You need a lawful basis for processing, transparency about how data is used, and mechanisms for data subject rights (including deletion requests that may affect trained models).

Can you help with AI governance for enterprise deals?

Yes. We build AI governance documentation packages that enterprise procurement teams expect: AI risk assessments, transparency documentation, human oversight mechanisms, and data processing boundaries. This is increasingly a deal requirement, not a nice-to-have.

What's the difference between AI Act compliance and GDPR compliance?

GDPR regulates how you handle personal data. The AI Act regulates how your AI system operates (risk classification, transparency, documentation, human oversight). You need both. They overlap in areas like automated decision-making and DPIAs, and we handle the intersection.

How do you handle the EU AI Act high-risk deadline (being revised under the Digital Omnibus provisional agreement to 2 December 2027 / 2 August 2028 pending formal adoption)?

We start with risk classification to determine if your systems fall into the high-risk category. From there we map the required obligations (technical documentation, risk management system, post-market monitoring, human oversight, transparency) against your existing program and build the gap-closure plan with a realistic timeline. The original August 2, 2026 date applies to high-risk systems until the Digital Omnibus provisional agreement is formally adopted (stand-alone systems revised to 2 December 2027, embedded systems to 2 August 2028). Compliance work typically takes 6 to 12 months for high-risk systems, so most AI companies should be scoping now.

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