Operationalizing Convergence: A Comparative Framework for GDPR and AI Act Impact Assessments in 2026

Introduction: The 2026 Compliance Landscape

The regulatory landscape of 2026 is defined by the intersection of the General Data Protection Regulation (GDPR) and the fully matured EU Artificial Intelligence Act (AI Act).While the GDPR has been the de facto global standard for data privacy for nearly a decade, the operationalization of the AI Act—specifically regarding High-Risk AI Systems—introduces novel complexities.

For our clients, the most pressing operational challenge is the overlap between Article 35 of the GDPR (DPIA) and Article 27 of the AI Act (FRIA). While distinct in scope, these assessments share a teleological goal: the mitigation of risk to natural persons. However, treating them as siloed bureaucratic exercises risks both compliance fatigue and gaps in oversight.

Structural Divergences: Privacy vs. Societal Harm

To harmonize these requirements, one must first understand their divergent epistemologies.

• The DPIA (GDPR): Historically, the DPIA focuses on data processing risks. It is a granular analysis of how personal data flows, is stored, and is secured. Its primary lens is individual privacy and the rights of the data subject (e.g., access, erasure).

• The FRIA (AI Act): The FRIA, mandatory for deployers of HRAIS (such as those in employment or credit scoring), adopts a broader lens. It assesses risks not just to privacy, but to the full spectrum of fundamental rights protected by the EU Charter—including non-discrimination, freedom of assembly, and consumer protection.

Critically, a system might be GDPR-compliant (e.g., perfectly anonymized data) but fail an AI Act assessment because the model outcomes are biased against a protected group.

The "Double Burden" of Assessment

By August 2026, organizations operating HRAIS must have performed a FRIA. The friction arises because the input data for these systems triggers a DPIA.

A common pitfall we observe in consultancy is the "parallel track" approach, where the Privacy Office conducts a DPIA and the AI Ethics Committee conducts a FRIA. This leads to:

1. Duplication of Documentation: Both assessments require detailed descriptions of the system’s logic and purpose.

2. Inconsistent Mitigation Strategies: The Privacy team might demand data minimization (reducing training data), while the AI team demands more diverse data to mitigate bias (per Article 10 of the AI Act).

Proposed Framework: The Integrated Impact Assessment (IIA)

We advocate for an Integrated Impact Assessment (IIA) methodology. This approach treats the DPIA as a foundational module within the broader FRIA architecture.

Step A: The Data Foundation (GDPR Focus)
The assessment begins with the "input layer." Is the data legally sourced? Is there a lawful basis under Article 6 GDPR? Here, strict adherence to data minimization applies.

Step B: The Model Logic (Hybrid Focus)
This phase analyzes the processing. It satisfies Article 22 GDPR (Automated Individual Decision Making) transparency requirements while simultaneously addressing Article 13 of the AI Act (Transparency and Information to Users). The key here is explainability: can the system’s outputs be audited?

Step C: The Rights Radius (AI Act Focus)
Finally, the assessment expands to downstream impacts. If the model is technically sound and data-secure, does it still produce discriminatory outcomes? This is where the FRIA supersedes the DPIA, requiring "human oversight" measures (Article 14 AI Act) that go beyond data security.

Conclusion

As the August 2026 deadline looms, the organizations that will succeed are those that view compliance not as a checklist, but as a holistic governance architecture. The convergence of GDPR and the AI Act is not merely a legal hurdle; it is the new baseline for trust in the digital economy.