DPIA and AI Act: how to optimize AI compliance through threat automation

The entry into force of theAI Act marks a decisive turning point for data governance. For organizations, this new legal situation is not limited to technical compliance. It requires a profound overhaul of the impact assessment relating to data protection (DPIA). While the RGPD laid the groundwork for the security of personal data, theAI Act introduces dimensions of systemic risks, algorithmic biases, and automated cyber threats that must now be at the heart of every assessment.

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Convergence needed between RGPD and AI Act

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The deployment of an artificial intelligence system, particularly when it is classified as high risk, almost always triggers the obligation to carry out a DPIAunder the aegis of RGPD. In 2026, the border between these two regulations is being erased in favor of an integrated approach. The challenge is to no longer treat privacy protection and algorithm security as two separate silos. The DPIA becomes the pivotal document to demonstrate the control of risks for fundamental rights and freedoms, including now the risks associated with the use of AI itself.

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An industrialized methodology to gain efficiency

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To meet the requirements ofAI Act without increasing the administrative burden, the software Adequacy is based on methodology EBIOS adapted to the IT context and freedoms.

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The transition from register to impact assessment

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The first step of DPIA includes the elements describing the treatment, the data and the life cycle of the data. In Adequacy, the information relating to the treatment and the data is taken directly from the register of processing activities. This mechanism saves valuable time and increases efficiency by avoiding redundant entries. Therefore, the compliance manager's effort focuses on documenting the data lifecycle in the operations tab.

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Evaluation of measures and risk analysis

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Once the context has been set, the security measures already present in the treatment sheet are automatically integrated. The analysis then moves to risk assessment in five steps: the identification of risk sources (where AI is specifically identified), the feared events, the threats, then the analyses of initial and residual risks.

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The emergence of new AI-related threat scenarios

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Traditional methodologies focused on threat scenarios from traditional mediums such as applications or hardware. With artificial intelligence, the spectrum is broadening to include this new data medium.

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• Training data poisoning (Data Poisoning) represents a major threat where an attacker corrupts sources to bias long-term results
• Model evasion and data inference are also vigilance points for guessing sensitive information about the persons concerned.

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Adequacy makes it possible to automatically generate these threat scenarios based on the type of violation and the medium identified. This automation ensures that AI-specific attack vectors are taken into account in an industrial manner. While this automation saves a significant amount of time in the production of DPIA, it does not in any way remove the need for DPO to understand the concepts behind these specific threats to validate the relevance of the analysis.

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Best practices for operational DPIA

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• Capitalize on the register: make sure your treatment sheets are up to date so that the first step in your DPIA be generated instantly in Adequacy
• Document the life cycle in detail: specify the media used at each stage (learning, testing, production) for total traceability required by theAI Act
• Identify AI as a source of risk: don't treat AI as just software, but as a specific source of risk in your analysis EBIOS
• Analyze the residual risk rigorously: use complementary measures to reduce the risk of bias or hallucinations to an acceptable level
• Centralize evidence: combine remediation measures and evidence of human control into a single medium to simplify audits

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FAQ - the essentials of DPIA and AI

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How does the interconnection with the registry optimize the DPIA?

‍By automatically entering the data and purposes of the register, you eliminate input errors and reduce the time required to prepare the impact assessment in half.

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Why is the data lifecycle central to an API for AI?

‍AI transforms data at several stages (training, inference). Documenting each operation makes it possible to precisely identify where the risks of corruption or flight are located.

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What is the difference between a software threat and an AI threat?

‍A software threat aims to stop the service, while an AI threat (like the Data Poisoning) aims to hijack the system's decision logic while leaving it operational.

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Is the residual risk always higher with AI?

‍Not necessarily. If remediation and human supervision measures are robust and well documented, the residual risk can be perfectly controlled.

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Is scenario automation accepted by the authorities?

‍Yes, as long as it is based on a methodology recognized as EBIOS and that it is complemented by human analysis on the relevance of the results obtained.