Between Regulatory Innovation and Legal Liability: What is the Practical Meaning for Organizations?
The accelerated development of Artificial Intelligence (AI) systems poses a real challenge to privacy protection laws: who is responsible for the data, how are risks mitigated, and how is the fair and transparent use of personal information ensured? The document emphasizes that even when the technology is new, the core obligations of privacy laws do not disappear—they simply receive a new practical interpretation.
About a year ago, the Privacy Protection Authority published a draft guideline clarifying how the Protection of Privacy Law applies to AI systems, emphasizing that the use of AI does not exempt organizations from privacy, transparency, and information security obligations. For business clients, the practical implication is that organizations must prepare for the publication of the final guideline and are obligated to preemptively examine the entire lifecycle of the AI system—from training to output—rather than sufficing with a technological assessment alone.
What are the key points?
* The Protection of Privacy Law may also apply to personal information that the system infers or creates, and not just to information directly entered into it.
* Informing data subjects must be more detailed as the use of AI becomes more complex or sensitive.
* In cases of high risk to privacy, the burden of justifying the use of information and legal defense claims increases.
* Uses such as scraping, model training, chatbots, agents, and automated insight generation increase the risk to the organization and require prior preparation.
What are the central principles?
The document highlights several principles that recur throughout the scope of analysis:
* Transparency and explainability toward users and data subjects.
* Data minimization and collecting only what is necessary for the purpose.
* Information security and risk management throughout the system’s lifecycle.
* Corporate accountability and documentation of decision-making processes.
* Integration of Privacy by Design starting from the planning and development stages.
Why is this important for organizations? Primarily, what needs to be done?
The document is significant because it addresses practical questions facing organizations: Is informed consent required? How is scraping handled? What is the status of personal information in trained models? And what preliminary tests must be conducted before launch? Additionally, it addresses the importance of a Privacy Impact Assessment (PIA) and a systematic review of privacy risks, especially in systems that create or infer sensitive information.
For organizations using or developing AI, the message is clear: technological innovation is not enough; compliance, control, and documentation mechanisms must also be built. The document suggests viewing privacy not as a barrier, but as a condition for the responsible, reliable, and stable use of systems.
Organizations operating or implementing AI systems must update privacy policies, examine the legal basis for processing, and ensure that documentation, data minimization, and access control mechanisms are in place. When the system directly interfaces with customers or employees, it is particularly important to provide clear disclosure regarding the nature of the processing, types of information, purposes of use, and potential risks.
By the way, why is this important for the organization’s customers?
In a world where AI is becoming everyday infrastructure, privacy is no longer a side issue—it is a fundamental component in the design, operation, and risk management of the system. The draft reflects a clear regulatory trend: AI is not a “gray area” regarding privacy, but a field where organizations are expected to demonstrate responsibility, advance planning, and compliance with legal requirements. Organizations that address the issue now will reduce the risk of regulatory exposure, privacy violation claims, and damage to customer trust.
The Bottom Line:
Anyone developing or using AI must first examine not only what the system can do, but also what it is permitted to do under privacy laws. Subsequently, they must implement the necessary safeguards to ensure the system indeed does only what is permitted.
Interested in a gap analysis against the requirements of the Protection of Privacy Law in general, and Amendment 13 in particular? Contact the experts at IPV Security!
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IPV Security has specialized for 20 years in information security, cyber, risk assessments, and standards and regulations concerning information security and more.