Japan's Act on the Protection of Personal Information was significantly revised in 2022, and the amendments that came into force that year added new obligations around data lineage, breach notification, and third-party data transfers. For companies that handle personal data across multiple systems, demonstrating compliance requires more than a privacy policy. It requires documented evidence of where personal data lives, who can access it, and what happens to it.
What APPI requires from a data documentation perspective ¶
The 2022 amendments require organisations to maintain records of personal information handling, including the purpose of use, the categories of data held, and the third parties to whom data is transferred. For organisations with data spread across multiple systems, this means being able to trace a personal data field from its origin table through every transformation and every downstream consumer. That is a data lineage problem.
The practical challenge: schemas that were not designed for compliance ¶
Most enterprise databases in Japan were not designed with APPI compliance in mind. Column names are often in Japanese, abbreviated, or inherited from legacy systems with no documentation. A column named 'ref_id_2' in a 2008 Oracle schema might contain names, phone numbers, or nothing sensitive at all. Manual documentation of these schemas is time-consuming and error-prone, and it goes out of date every time a schema changes.
Automated PII detection as a starting point ¶
Automated PII detection scans actual data values rather than relying on column names. A pattern matcher looks for formats consistent with personal names, addresses, phone numbers, email addresses, and My Number identifiers. When a match is found, the column is flagged and added to the lineage graph. This is not a substitute for legal review, but it gives a compliance officer a documented starting point that is far more complete than a manual survey.
Generating audit-ready documentation ¶
A compliance officer needs to be able to produce documentation on demand. For an internal audit, for a regulator, or in the event of a breach. That documentation should show: which tables contain personal data, who has queried those tables and when, and what downstream systems receive that data. A data platform that generates this automatically, on a weekly schedule, removes the manual effort and reduces the risk of gaps in the record.
The Govern module in Meshblend handles lineage mapping, PII detection, and audit report generation. If you want to see how it maps to your specific schema, book a call with our team.