Why Privacy Teams Are the Missing Link in AI Governance 2026
As artificial intelligence (AI) becomes deeply embedded in business processes, products, and decision-making, organizations are increasingly confronted with a critical challenge: how to govern AI in a way that is ethical, lawful, transparent, and trustworthy. While much attention is paid to technical controls, model accuracy, and new AI-specific regulations, one essential group is often overlooked in AI governance discussions — privacy teams.
AI Governance Is More Than a Technical Problem
AI governance is frequently framed as a technological issue, focused on algorithms, data pipelines, and system performance. However, effective governance goes far beyond code. It requires clear accountability, risk assessment, transparency, and oversight across the entire AI lifecycle. These are areas where privacy professionals already have years of hands-on experience.
Privacy teams are accustomed to working at the intersection of law, technology, and organizational processes. They understand how to translate abstract regulatory principles into operational controls and policies. This makes them uniquely positioned to support AI governance frameworks that are practical, scalable, and defensible.
Strong Overlap Between Privacy and AI Risks
AI systems rely heavily on data — often personal data — and can introduce new risks such as profiling, bias, discrimination, and lack of explainability. Many of these risks closely resemble those privacy teams have been managing under data protection laws like the GDPR.
Tools such as Data Protection Impact Assessments (DPIAs) provide a structured way to identify, assess, and mitigate risks before deployment. These same methodologies can be adapted for AI risk assessments, helping organizations proactively address issues such as unfair outcomes, excessive data use, or unintended secondary purposes.
As a result, privacy teams often become involved in AI projects organically, even when AI governance is not formally part of their mandate. Their structured approach and risk-based mindset add much-needed discipline to fast-moving AI initiatives.
From Reactive Compliance to Proactive Governance
Without strong privacy involvement, organizations tend to take a reactive approach to AI — addressing issues only after incidents occur, such as regulatory scrutiny, public backlash, or legal claims. Privacy teams, however, are trained to think proactively.
By embedding privacy-by-design and risk-by-design principles into AI development, organizations can identify potential harms early and implement safeguards before systems go live. This not only reduces regulatory risk but also builds trust with customers, employees, and partners.
Aligning With Emerging AI Regulations
The regulatory landscape for AI is rapidly evolving, particularly in Europe with the introduction of the EU AI Act. Many of the new obligations — transparency, documentation, risk classification, and accountability — mirror concepts already familiar to privacy professionals.
Privacy teams have extensive experience navigating complex regulatory frameworks and engaging with regulators. Leveraging this expertise allows organizations to align AI governance with existing compliance structures instead of creating entirely new, disconnected processes.
Conclusion: Privacy Teams as a Strategic Asset
AI governance is not solely the responsibility of data scientists or IT departments. It is an organizational challenge that requires legal, ethical, and operational oversight. Privacy teams bring proven skills in risk management, governance, and accountability that are directly applicable to AI.
Rather than treating privacy as a separate compliance function, organizations should recognize privacy teams as a strategic pillar of AI governance. In doing so, they move closer to deploying AI systems that are not only innovative, but also trustworthy, ethical, and compliant by design.
