If you’re a Data Protection Officer, you’ve probably noticed that AI is making your job more complex. AI systems process personal data at scale, make inferences about individuals, and operate in ways that can be difficult to explain. Traditional data protection approaches weren’t designed for this, and the gap between privacy compliance and AI governance is where risk accumulates.
The intersection of AI and data privacy
Data protection and AI governance are not separate disciplines, they overlap significantly. Every AI system that processes personal data is subject to data protection law. But AI introduces challenges that go beyond what GDPR was originally designed to address.
Consider automated decision-making. Article 22 of the UK GDPR gives individuals rights in relation to solely automated decisions that produce legal or similarly significant effects. But many AI systems operate in a grey area, they assist human decisions rather than replacing them entirely. Determining whether a system falls within scope requires careful analysis of how the AI output actually influences the decision.
Then there’s the question of purpose limitation. Personal data collected for one purpose may be used to train an AI model that serves a different purpose entirely. Data minimisation becomes complex when machine learning models may benefit from more data, not less. And transparency obligations take on new dimensions when the logic of an AI system isn’t always straightforward to explain.
What DPOs need to focus on
Start with Data Protection Impact Assessments. Any AI system that processes personal data at scale, involves profiling, or makes automated decisions should trigger a DPIA. But a standard DPIA template may not capture AI-specific risks. You need to assess algorithmic bias, the quality and representativeness of training data, the explainability of the system’s outputs, and the effectiveness of human oversight mechanisms.
Lawful basis is another area requiring careful attention. Legitimate interest is commonly relied upon for AI processing, but the balancing test needs to account for the specific risks that AI introduces, including the potential for unexpected inferences and the difficulty individuals may face in understanding how their data is being used.
Vendor management deserves particular scrutiny. Many organisations are adopting AI through third-party tools and platforms. Your data processing agreements need to address AI-specific considerations: where is the data being processed, is it being used to train models, who has access to the outputs, and what happens to the data when the service ends?
The AI governance connection
This is where AI governance and data privacy converge. A well-designed AI governance framework doesn’t replace your data protection programme, it complements it. The AI risk assessment process should feed into your DPIA. Your AI inventory should inform your Record of Processing Activities. And your AI transparency measures should support your obligations under data protection law.
ISO/IEC 42001 explicitly addresses data governance as part of its control framework. If your organisation is building an AI Management System, data protection should be embedded from the start, not bolted on as an afterthought.
Practical steps for DPOs
Map your organisation’s AI landscape. Identify every AI system that processes personal data, including tools that staff may have adopted without formal approval. Shadow AI is a significant and growing risk.
Review your existing DPIAs and assess whether they adequately address AI-specific risks. Update your data processing agreements with AI vendors. And build a working relationship with whoever is responsible for AI governance in your organisation, if that person doesn’t exist yet, that’s a conversation worth having.
The organisations that manage the AI-privacy intersection well are those that treat it as a collaboration between disciplines, not a competition for ownership.