Personal physiological data

Personal physiological data refers to data resulting from specific technical processing related to the physical, physiological, or behavioral characteristics of a natural person, which allows for their unique identification. Examples include facial images, fingerprints, and iris scans. The EU's General Data Protection Regulation (GDPR) defines this as "biometric data" in Article 4(14) and classifies it as a "special category of personal data" in Article 9, for which processing is prohibited unless specific conditions are met. In the ISO/IEC 27701 framework for a Privacy Information Management System (PIMS), such data is considered high-risk Personally Identifiable Information (PII) that demands enhanced security controls, as its breach can cause permanent and irreversible harm to individuals.

In enterprise risk management, managing personal physiological data involves a structured approach. Step one is conducting a Data Protection Impact Assessment (DPIA) as required by GDPR Article 35. This systematically evaluates the necessity and proportionality of processing operations and helps manage the risks to individuals' rights. Step two is implementing robust technical and organizational controls, such as end-to-end encryption (e.g., AES-256), pseudonymization, and strict access control policies. Step three is establishing continuous monitoring and an incident response plan, ensuring the ability to report a breach within 72 hours per GDPR Article 33. For example, a financial institution implementing facial recognition for ATM withdrawals would use a DPIA to identify risks, encrypt biometric templates, and thereby improve its compliance posture and reduce potential fines.

Taiwanese enterprises face several key challenges. First, regulatory ambiguity exists as Taiwan's Personal Data Protection Act does not explicitly categorize facial or fingerprint data as sensitive, creating uncertainty over required consent levels. Second, there is a high barrier in terms of technology and resources; many small and medium-sized enterprises lack the budget and expertise to implement advanced security measures like strong encryption or anonymization required by international standards. Third, managing user consent is complex, especially in IoT environments, where obtaining specific, informed, and freely given consent for each processing purpose is a significant operational hurdle. To overcome these, enterprises should adopt the strictest interpretation of the law, leverage cost-effective cloud security solutions, and implement a centralized consent management platform.

Winners Consulting specializes in Personal physiological data for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact