Why "Personal Data Protection" Suddenly Feels Hard in the AI Era
When you incorporate generative AI or machine learning into work, it becomes convenient, but at the same time the anxiety of "isn't that handling personal data?" increases all at once. The true nature of the difficulty is that, because AI "eats data to get smarter," personal data easily gets mixed into one of input (prompts), training data, logs, or output.
For example, the following scenes are typical.
- A customer's name, contact, and purchase history go into an internal inquiry-handling chatbot
- Call-center audio transcription is summarized by AI and linked to CRM
- HR evaluation comments are formatted with generative AI (employee information is included)
- In image generation/analysis, face photos or car license plates get mixed in
In this article, while grasping the relationship between the EU's GDPR and Japan's Act on the Protection of Personal Information (APPI), we organize "where to be careful" and "how to design" in a practical way so it is easy to judge in the field.
First, Master This: The "Points Where AI and Personal Data Appear"
The places where personal data is involved in AI use are easy to organize if divided broadly into four.
- Input: prompts, attached files, and conversations that users or employees throw at the AI
- Training / fine-tuning: additional training of the model (fine-tuning) and internal documents for RAG
- Storage / logs: prompt logs, audit logs, error logs, vector-DB embeddings
- Output: whether summarization, recommendation, scoring, etc. have become "judgments about an individual"
Decomposing here makes "what to protect" clear all at once.
GDPR and APPI (Act on the Protection of Personal Information): Same? Different?
The shared basic philosophy: purpose, minimization, safety management
Both GDPR and APPI are similar in direction. Roughly speaking, it is to clarify the purpose and protect appropriately within the necessary scope.
- Clarifying the purpose: what it is used for (avoid use beyond the purpose)
- Data minimization: narrow to only the truly necessary items
- Safety management: access control, encryption, contractor management, etc.
GDPR's characteristics: extraterritorial application and a strong "lawful basis"
GDPR is the EU's personal-data protection regulation, and handling EU residents' data can make even a Japanese company subject to it (extraterritorial application). Also, as a premise for processing you need to be able to explain "why processing is permitted," and commonly used bases (lawful basis) include the following.
- Consent
- Performance of a contract
- Legal obligation
- Legitimate interests
In AI use, there are many scenes where you want to proceed with "legitimate interests" in particular, but it is important to be able to explain the balance with the rights and interests of the data subject.
APPI's characteristics: the distinctions of personal information / personal data / retained personal data work in practice
APPI is Japan's personal data protection law. In practice, in addition to "personal information" (information that can identify an individual), distinctions like databased "personal data" and "retained personal data" subject to disclosure work in operations.
