External vs. Internal Data Protection Officer: Who Can Better Cover AI Compliance Topics?

By Eckhart Mehler for CISOsCISO — a perspective on cybersecurity leadership, governance and the decisions that determine whether organizations retain control.

AI Compliance: A New Challenge for Data Protection Officers

The rise of artificial intelligence (AI) presents significant compliance challenges for organizations. Regulations such as the GDPR, ISO/IEC 27001, ISO/IEC 42001, and the EU AI Act impose strict requirements on AI systems, from data privacy to transparency and accountability.

One critical decision organizations face is whether AI compliance should be managed by an internal Data Protection Officer (DPO) or delegated to an external expert. Both options have distinct advantages and limitations, particularly when addressing AI-specific compliance, data privacy, and ethical concerns.

This article explores the benefits and challenges of each approach and provides guidance on selecting the right model for AI governance.

The Case for an Internal DPO

An internal DPO has the advantage of deep institutional knowledge. As an embedded part of the organization, they understand the company’s business model, data processing workflows, and AI use cases. This familiarity allows for proactive risk assessments and the seamless integration of compliance measures into AI projects.

A full-time DPO can also foster a strong culture of AI governance by ensuring that privacy-by-design principles are incorporated early in the development cycle. Close collaboration with IT, legal, and compliance teams enables faster decision-making and better alignment with corporate objectives.

However, internal DPOs often face challenges when dealing with AI-specific regulatory requirements. Many lack specialized expertise in algorithmic bias, explainability, and transparency, which are crucial for ensuring compliance with AI-related laws. Additionally, they may struggle with resource constraints, as managing AI compliance is often just one of their many responsibilities. Another concern is the risk of conflicts of interest, particularly when providing oversight for high-impact AI initiatives within the organization.

A global financial services company, for example, developed an AI-driven fraud detection system but faced difficulties aligning it with GDPR’s right to explanation (Article 22). The internal DPO lacked expertise in AI model transparency and had to bring in external specialists, leading to delays and additional costs.

The Case for an External DPO

An external DPO brings specialized expertise in AI governance and compliance. Many external advisors have deep knowledge of AI ethics, bias mitigation, and regulatory frameworks, ensuring that organizations remain aligned with evolving regulations. Their independence also strengthens credibility, particularly during regulatory audits or high-stakes compliance reviews.

External specialists continuously monitor legal developments, enforcement actions, and best practices in AI governance. This breadth of knowledge enables organizations to identify risks and implement corrective measures before regulatory intervention becomes necessary.

However, outsourcing AI compliance oversight has its drawbacks. External DPOs often lack direct knowledge of a company’s specific data handling practices and AI development processes. This can result in longer response times for compliance-related decisions. Additionally, engaging external experts for continuous oversight can be costly, making it an expensive long-term solution.

A healthcare technology company using AI for medical diagnostics illustrates this challenge. The company faced scrutiny over potential biases in its algorithm. While its external DPO had expertise in AI fairness and GDPR-compliant data anonymization, the lack of day-to-day involvement in product development created delays in implementing necessary compliance adjustments.

Choosing the Right Approach for AI Compliance

The decision between an internal or external DPO depends on multiple factors, including company size, industry, AI use cases, and regulatory exposure. Organizations developing high-risk AI applications, such as automated hiring systems, credit scoring models, or biometric identification tools, may require specialized external oversight to ensure compliance with evolving legal requirements.

Conversely, companies with established internal AI compliance capabilities may benefit from having an embedded DPO who can integrate governance processes seamlessly into daily operations. In many cases, a hybrid approach—where an internal DPO handles routine compliance while an external expert provides periodic audits and assessments—can offer the best balance between operational efficiency and regulatory expertise.

With the increasing complexity of AI regulations, organizations must ensure they have the right expertise in place. Whether through internal resources, external advisors, or a combination of both, a proactive AI compliance strategy will be essential for maintaining regulatory alignment and building trust in AI systems.

How is your company handling AI compliance? Are you relying on internal expertise, outsourcing, or a mix of both? Let’s discuss in the comments.

Further Reading

• GDPR Article 22: Automated Decision-Making & Profiling – Link
• EU AI Act Regulatory Proposal – Link
• SO/IEC 42001: AI Management System Standard – ISO Official Site

Publication Note & Disclaimer
This article was originally published on LinkedIn on February 15, 2025 and may have been edited or updated for publication on this site.

It reflects my personal professional perspective and does not represent the official policy or position of my employer. Drafting and editorial refinement may have been supported by commercially available AI-assisted tools. The analysis, conclusions and final curation are entirely my own.

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