Minors' Data and AI: Understanding Systemic Compliance Debt
By 2026, the exploitation of data derived from "sharenting" will no longer be just an ethical debate, but a major operational risk for organizations. The massive sharing of minors' data by third parties creates a systemic compliance debt. Between the technical impossibility of algorithmic unlearning and evolving regulations on the digital sovereignty of digital natives, companies face exposure to mass litigation. This briefing note analyzes why managing minors' digital identity is becoming a pillar of data governance and a critical issue of civil and administrative liability.
By 2026, the exploitation of data derived from "sharenting" will no longer be just an ethical debate, but a major operational risk for organizations. The massive sharing of minors' data by third parties creates a systemic compliance debt. Between the technical impossibility of algorithmic unlearning and evolving regulations on the digital sovereignty of digital natives, companies face exposure to mass litigation. This briefing note analyzes why managing minors' digital identity is becoming a pillar of data governance and a critical issue of civil and administrative liability.
Minors' Consent and Legal Fragility Under GDPR
The current collection model relies on the delegation of parental authority to validate the processing of minors' data. However, this legal foundation is crumbling. With the entry into force of new interpretations of the AI Act and the GDPR, "substitute consent" is increasingly perceived as a precarious solution for long-term processing, such as training AI models.
The risk for the company lies in the retroactive nullity of consent. Upon reaching digital majority, a minor could contest the lawfulness of processing undergone during their childhood. For organizations, this means entire sections of their training databases could become illegal overnight. This would entail a colossal loss of intangible assets and sanctions based on the absence of a persistent legal basis.
Machine Unlearning: The Technical Cost of AI Compliance
The right to erasure (Article 17 of the GDPR) takes on a new dimension with artificial intelligence. If a minor's data has been used to refine the statistical weights of a model, simply deleting the source file is no longer sufficient. Compliance now demands "Machine Unlearning" or algorithmic unlearning.
For a technical department, the cost of this compliance is exorbitant. Surgically extracting an individual's influence from a massive model is a complex operation. If it fails, it may force the company to destroy and fully retrain its model. This "technical compliance debt" represents a major financial risk that DPOs and CISOs must now integrate into their risk mapping.
Profiling Risks and Algorithmic Model Contamination
The uncontrolled ingestion of minors' data pollutes the integrity of AI systems. By using "sharenting" data, companies expose themselves to predictive profiling biases based on family histories. The AI Act classifies these practices among high-risk or even prohibited activities.
An organization processing this data exposes itself to several consequences:
- Class Actions brought by child protection associations seeking compensation for the spoliation of digital identity
- Brand depreciation, as associating a company with the algorithmic exploitation of childhood becomes an insurmountable reputation risk in the trust economy
AI Act: New Sanctions for Protecting Vulnerable Persons
The entry into force of the AI Act radically changes the scale of sanctions. The European legislator has placed the protection of vulnerable persons, and particularly minors, at the top of the risk pyramid.
Here are the breaking points identified for organizations:
- Prohibition of cognitive manipulation practices: Any AI system using subliminal techniques or exploiting age-related vulnerabilities to substantially alter a minor's behavior is now banned. Algorithm-driven commercial "sharenting" falls directly into this danger zone
- Record sanctions: In case of violation of prohibited practices, including the exploitation of minors' vulnerabilities, fines can reach 35 million euros or 7% of total worldwide annual turnover, whichever is higher. This severity exceeds that of the GDPR and marks a clear desire to sanctify the digital integrity of minors
- Post-market monitoring obligation: Companies must not only certify their model upon entry but also continuously document the absence of negative impact on children's fundamental rights
This regulatory pressure transforms the passive storage of minors' data into an immediate financial liability. Compliance is no longer a stable state, but dynamic surveillance.
Conclusion: Towards Strict Digital Identity Governance
Sharenting is the symptom of a society that has confused memory with exposure. By treating childhood as public data, we are creating a generation of citizens who are "predictable" and "pre-analyzed" by machines. Tomorrow's digital resilience will require a brutal realization: protecting a child also means protecting their absence of data. The role of regulators will be to transform this observation into a technical obligation, ensuring that tomorrow's identity is no longer held hostage by yesterday's posts.
FAQ - Risks Related to Minors' Data and AI Governance
Why is "sharenting" a risk for B2B companies or third-party services?
Because data circulates. A company may ingest minors' data via APIs or database acquisitions without knowing it. Liability is joint: the final user of the model is responsible for the integrity of the training data.
Can AI identify a minor even if their name is removed?
Yes, through biometric inference or metadata cross-referencing. Anonymization is often a technical mirage when faced with the analytical power of neural networks.
What is the expected position of the European AI Office on this subject?
Increased severity is expected. The protection of vulnerable groups is a pillar of the AI Act. We anticipate specific audit obligations for any model that has been exposed to minors' data.
Audit Your Digital Debt !
Adequacy supports legal and technical departments in evaluating their exposure through three levers:
- Flow Audit to identify where and how minors' data enters your ecosystem
- "Unlearnability" Assessment to test the resilience of your models against future complex erasure requests
- Predictive Governance to anticipate AI Act evolutions and turn your risk into a competitive advantage
