How Regulators Build AI Cases: Evidence and Patterns
Regulatory scrutiny of artificial intelligence systems in the European Union is shifting from theoretical compliance checklists to evidence-based enforcement. For organizations deploying AI, robotics, biometric systems, or automated decision-making tools, the critical question is no longer simply whether a system falls within the scope of the AI Act, but rather how a regulator would prove non-compliance if an incident occurs or a market surveillance authority initiates an investigation. Understanding the evidentiary lifecycle of a regulatory case provides the blueprint for internal compliance engineering. It reveals that documentation is not merely an administrative burden but the primary defense mechanism in a liability landscape increasingly shaped by the General Data Protection Regulation (GDPR), the AI Act, and the Product Liability Directive.
Building a regulatory case is a forensic exercise in reconstructing decisions, data flows, and risk management practices that occurred months or years prior to the investigation. Regulators, including Data Protection Authorities (DPAs) under GDPR and the future National Competent Authorities (NCAs) under the AI Act, rely on a specific pattern of evidence gathering. They look for the digital paper trail that an organization leaves behind. When this trail is fragmented, contradictory, or nonexistent, the regulator builds a case based on the absence of evidence, which often leads to the most severe penalties and corrective orders. Therefore, preparing for regulatory scrutiny requires a shift in mindset: documentation must be treated as a legal artifact, not just a technical one.
The Anatomy of a Regulatory Investigation
Regulatory investigations rarely begin with a surprise raid. They typically originate from a trigger event: a consumer complaint, a data breach notification, a media report on algorithmic bias, or a self-reporting obligation under the AI Act’s post-market monitoring system. Once triggered, the authority initiates an information request. In the GDPR context, this is often an Article 58 request for access to information. Under the AI Act, it mirrors the market surveillance powers defined in the Regulation (EU) 2019/1020.
The regulator’s objective is to establish two things: what happened (the factual timeline) and what should have happened (the legal standard). To establish the facts, they request specific artifacts. These are not generic “compliance documents” but granular evidence of the engineering and governance process.
The Evidentiary Framework
When a regulator opens a file, they are essentially asking the organization to prove its innocence. The burden of proof regarding compliance generally rests with the entity placing the system on the market. The evidence requested usually falls into three distinct buckets:
1. Technical and Architectural Evidence
Regulators are increasingly technically literate. They do not accept vague statements like “the AI is neutral.” They require a dissection of the system. In the context of the AI Act, this means requesting the Technical Documentation required under Annex IV. This includes detailed descriptions of the algorithms, the data sources used for training, validation, and testing, and the metrics used to evaluate performance.
For high-risk AI systems, a regulator will look for the Design Control records. They want to see how the system was architected to mitigate risks. If a biometric identification system is under scrutiny, they will request logs showing how the system handles false positives and how human oversight was technically implemented, not just stated in a policy. They will ask for the “system card” or “model card” to understand the intended purpose and the limitations explicitly communicated to the user.
2. Governance and Procedural Evidence
Compliance is often decided in meetings, not just code. Regulators place heavy weight on Minutes of the Risk Management Team, Minutes of the Quality Management System (QMS) reviews, and records of the Conformity Assessment procedures.
A common pattern in case building is the “gap in the minutes.” If a technical team identified a potential bias in a dataset during a meeting, but the minutes fail to record a decision on how to mitigate it, the regulator interprets this as a failure of the risk management process. They look for the Chain of Custody regarding decisions. Who authorized the deployment of a model update? Was the regulatory affairs officer consulted? If the documentation shows that a high-risk system was updated without a re-evaluation of conformity, the case is straightforward for the authority.
3. User and Market Evidence
The regulator investigates the gap between the “intended purpose” and “actual use.” They will request copies of user manuals, API documentation, and marketing materials. If the marketing claims the system can diagnose cancer with 99% accuracy, but the technical documentation only supports a 70% accuracy rate for a specific demographic, the regulator builds a case based on misleading commercial practices or non-compliance with the accuracy requirements of the AI Act.
Furthermore, under the AI Act’s obligation to ensure human oversight, regulators will interview users. They will ask: “Did the system provide enough information for you to override it?” If the user manual is silent on this, or if the interface design makes overriding the system practically impossible, the regulator uses this as evidence of non-compliance by design.
Common Documentation Gaps: The “Fatal Flaws”
Through the lens of GDPR enforcement and the emerging guidance on the AI Act, a clear pattern of “fatal flaws” emerges. These are the documentation gaps that regulators exploit to build strong cases against organizations. They usually stem from a disconnect between legal requirements and engineering realities.
The “Black Box” Defense Failure
Organizations often claim that an AI system is too complex to explain. While interpretability is a technical challenge, it is not a legal excuse. Regulators build cases against “black box” systems by demanding Explainability (XAI) Evidence. If an organization cannot produce documentation explaining the logic behind a specific automated decision (e.g., why a loan was denied or why a specific worker was flagged for safety inspection), they are in violation of GDPR Article 22 and the AI Act’s transparency obligations.
The fatal flaw here is the lack of Traceability. A robust system allows an auditor to trace an output back to the specific input data and model version used. If the logging is insufficient to reconstruct a decision, the regulator assumes the worst: that the decision was arbitrary or discriminatory.
The Static Risk Management File
The Risk Management System (Article 9 of the AI Act) is a continuous process, not a one-time file created at the start of the project. A common case-building pattern involves the regulator comparing the “Live” version of an AI system with the “Certified” version. They find that the model has drifted, or the data distribution has changed, but the Risk Management File remains unchanged.
When an organization fails to document Post-Market Monitoring data, the regulator argues that the organization failed to identify new risks that emerged after deployment. This is particularly critical in biotech and robotics, where environmental interactions can change risk profiles. The absence of a log recording incidents, near-misses, or user feedback is treated as evidence that the monitoring system is defective.
The “Paper-Only” Quality Management System
Under the AI Act, high-risk systems must adhere to a QMS. Regulators distinguish between a QMS that exists on paper and one that is integrated into the organization’s culture. They build cases by cross-referencing QMS documentation with actual engineering logs.
For example, a QMS might require that all data labeling be reviewed by a senior expert. If the regulator requests the review logs and finds that thousands of labels were approved in minutes, or that the “expert” was a junior contractor without proper training, they build a case for document fraud. The documentation provided contradicts the operational reality.
Confusion between National Implementations
In Europe, the regulatory landscape is layered. While the AI Act is an EU Regulation (directly applicable), it relies on national designated authorities. Similarly, GDPR allows for national derogations and specific interpretations. A common documentation gap is the “one-size-fits-all” approach.
For instance, in the context of biometric data, some countries (like France via the CNIL) have historically stricter interpretations of “sensitive data” than others. An organization operating across the EU might have a central policy that meets the baseline GDPR requirement but fails to account for the specific documentation requirements of a stricter national authority. When a case is opened in that specific member state, the regulator builds the case on the failure to meet the national standard, arguing that the organization failed to perform adequate local legal analysis.
Regulator Methodology: From Request to Sanction
To prepare effectively, organizations must simulate the regulator’s methodology. This involves a forensic review of internal systems to identify where the narrative breaks down.
The “Proportionality” Test
Regulators apply a proportionality test to evidence requests. They do not ask for everything at once; they ask for what is relevant to the specific allegation. If a system is accused of gender bias, they will request the dataset composition statistics and the training logs specifically related to gender features.
How organizations fail: They often over-redact documents or refuse to provide code, citing trade secrets. While protecting IP is valid, an overly aggressive refusal strategy often signals to the regulator that there is something to hide. This leads to escalated enforcement, including unannounced inspections. The better approach is to provide structured evidence that explains the system without necessarily revealing the proprietary algorithm’s weights. For example, providing a “logic flow diagram” rather than raw code, if agreed upon, can satisfy the regulator’s need for understanding without compromising IP.
The Interview Phase
Regulators interview the “Data Protection Officer” (DPO), the “Chief Technical Officer” (CTO), and the “Compliance Lead.” They look for consistency. If the DPO states that they were not consulted on a specific data processing activity, but the CTO states that all processing was approved by compliance, the discrepancy creates a case for internal governance failure.
The regulator will ask: “Who decided that this system was high-risk?” If the answer is “nobody,” or “we didn’t think it was high-risk,” the regulator will review the system against the criteria in Annex III of the AI Act. If the system clearly falls into the high-risk category (e.g., CV screening software), the lack of a documented justification for not classifying it as high-risk is itself a violation.
Technical Audits and Sandboxing
In complex cases, regulators may request access to a test environment or conduct a technical audit. They will feed “adversarial inputs” into the system to see how it reacts. They will check if the system’s “kill switch” or “human override” functions work as documented.
If the documentation claims “human-in-the-loop” oversight, but the audit reveals that the human operator has only 5 seconds to review a complex decision, the regulator builds a case based on ineffective oversight. The documentation promised a safeguard that was practically impossible to utilize.
Comparative Approaches: The European Patchwork
While the AI Act harmonizes the rules, enforcement culture varies. Understanding these nuances is vital for multi-national organizations.
Germany: The Engineering Rigor
German regulators (such as the BfDI or the market surveillance authorities under the Federal Ministry for Economic Affairs) tend to focus heavily on the Technical Documentation and the Quality Management System. They approach AI regulation much like product safety regulation. They expect engineering rigor, traceability, and adherence to standards like ISO 27001 or ISO 42001 (AI Management Systems). A case in Germany often hinges on whether the organization followed its own internal engineering standards. If the documentation says “we follow a rigorous testing protocol,” but the evidence shows skipped tests, the German regulator will view this as a severe breach of the “due diligence” culture.
France: The Data Sovereignty and Bias Focus
The CNIL (Commission Nationale de l’Informatique et des Libertés) is a powerful actor. They are particularly focused on the legality of the training data and the protection of personal data within the model. French regulators are quick to investigate whether consent was validly obtained for data scraping. They are also highly sensitive to algorithmic bias, particularly regarding race and gender. A French regulatory case often starts with a request for the “Data Protection Impact Assessment” (DPIA) and looks for evidence that the organization consulted the CNIL before deploying high-risk processing.
The United Kingdom: The Pro-Innovation Sandbox Approach
Post-Brexit, the UK is taking a “context-based” approach rather than a rigid list-based approach like the EU AI Act. The ICO (Information Commissioner’s Office) focuses on “Accountability” and “Explainability.” The UK approach is more likely to offer “regulatory sandboxes” where companies can test AI under supervision. However, if a case goes to enforcement, the UK courts look for reasonableness. Did the organization act reasonably given the state of the art? The documentation must reflect a reasonable assessment of risks, even if it isn’t as prescriptive as the EU’s Annex requirements.
Preparing the “Defense File”: A Practical Guide
To survive regulatory scrutiny, organizations must build a “Defense File” proactively. This is not a single document but a repository of evidence that tells a coherent story of compliance.
1. The Decision Log (The “Why” Log)
Every significant decision regarding the AI system must be logged. This includes decisions on dataset selection, model architecture, and risk acceptance. The log must record who made the decision, what data they reviewed, and why they made the decision. If a regulator asks, “Why did you use this proxy variable which correlates with race?”, the Decision Log should provide the justification (e.g., “It was the least discriminatory alternative available to achieve the legitimate aim”).
2. The “Living” Risk Management File
This file must be updated continuously. It should contain a register of risks, the mitigation measures applied, and the residual risk level. Crucially, it must include Residual Risk Statements. If a risk cannot be fully mitigated (e.g., the inherent risk of bias in historical data), this must be documented, along with the justification for why the system is still safe enough to deploy. This transparency protects the organization from accusations of hiding known risks.
3. The Data Provenance Map
Regulators will trace the data. Organizations need a map that shows exactly where training data came from, the legal basis for its use (e.g., legitimate interest, consent), and how it was cleaned. This is vital for GDPR compliance and the AI Act’s data governance requirements. If the data was sourced from a third party, the organization must keep the due diligence records proving the third party had the right to share it.
4. The “Intended Purpose” Definition
One of the most litigated concepts in the AI Act is “intended purpose.” Organizations often try to keep this vague to maximize market flexibility. Regulators exploit this vagueness. A strong defense file contains a precise, narrow definition of the intended purpose. It explicitly lists what the system is not designed to do. This prevents the regulator from accusing the organization of deploying a high-risk system for an unauthorized purpose.
5. The Human Oversight Interface Logs
For high-risk systems, the AI Act requires effective human oversight. Organizations must prove that the human oversight was technically feasible and effective. This requires logging user interactions with the system. Did the human override the AI? Did they ignore the AI’s advice? These logs are the only evidence that the “human-in-the-loop” requirement is being met in practice.
The Role of the AI Practitioner in Legal Defense
As an AI systems practitioner, the role extends beyond coding. It involves “compliance by design.” The practitioner must understand that the code they write generates the evidence that will be used in a legal proceeding.
For example, when designing a logging system, the practitioner must ensure that logs are immutable, timestamped, and retained for the required duration (often years). When designing a user interface, the practitioner must ensure that the “human override” button is not buried in a submenu but is accessible and intuitive. If the regulator investigates an accident and finds that the override function was hidden, the practitioner’s design choices become part of the legal case against the company.
Furthermore, practitioners must be prepared to translate technical concepts into legal arguments. When a regulator asks, “Is this model biased?”, the practitioner cannot simply say “no.” They must be able to explain the metrics used (e.g., demographic parity, equalized odds), the thresholds chosen, and the trade-offs made. The documentation must bridge the gap between mathematical optimization and legal fairness.
Conclusion: The Future of Evidence
The regulatory landscape in Europe is moving towards a model of continuous assurance. The AI Act’s requirement for a “Conformity Assessment” is not a one-time stamp of approval; it is a commitment to a standard that must be maintained. Regulators are building their capacity to conduct deep technical audits, likely utilizing AI themselves to analyze the code and data of investigated entities.
Organizations that view documentation as a reactive chore will find themselves vulnerable. They will be unable to reconstruct the rationale for their systems when the regulator knocks on the door. Conversely, organizations that treat documentation as an integral part of the AI lifecycle—generating evidence of compliance as they build—will find that regulatory scrutiny is manageable. The regulator’s case is built on the evidence they can find. The organization’s defense is built on the evidence they have preserved. In the European regulatory framework, the best defense is a well-maintained file.