High-Risk Processing

High-risk processing, a core concept from Article 35 of the EU's GDPR, refers to processing likely to result in a high risk to the rights and freedoms of individuals, especially when using new technologies. The European Data Protection Board (EDPB) provides criteria for identification, such as large-scale systematic monitoring, processing of special categories of data, or automated decision-making with legal effects (e.g., AI-driven credit scoring). If two or more criteria are met, a Data Protection Impact Assessment (DPIA) becomes mandatory. This proactive risk assessment mechanism is more explicit than general security requirements found in other regulations, serving as a critical trigger for in-depth privacy analysis before a project launch. It shifts the compliance focus from reactive breach response to proactive, risk-based data protection by design.

In practice, enterprises manage high-risk processing by integrating the Data Protection Impact Assessment (DPIA) into their project lifecycle. The process involves three key steps: 1) Screening: Use checklists based on EDPB criteria to identify processing activities that are potentially high-risk. 2) Assessment: For identified activities, conduct a formal DPIA to describe the data flows, assess the necessity and proportionality of the processing, and evaluate the specific risks to data subjects. 3) Mitigation: Implement technical and organizational measures, such as pseudonymization, encryption, or access controls, to address the identified risks. For example, a healthcare provider developing an AI diagnostic tool would use a DPIA to assess risks of misdiagnosis or data bias, leading to improved algorithm training and data governance. This can increase regulatory audit pass rates to over 95% and significantly reduce the likelihood of privacy-related incidents.

Taiwan enterprises face several challenges: 1) Regulatory Ambiguity: Taiwan's Personal Data Protection Act (PDPA) lacks the explicit concept of 'high-risk processing' and mandatory DPIAs, leading to lower awareness and inconsistent practices. 2) Resource Constraints: SMEs often lack dedicated Data Protection Officers (DPOs) and the budget for comprehensive risk assessments. 3) Technical Complexity: Assessing the privacy risks of complex systems like AI and IoT requires a rare combination of legal and technical expertise. To overcome these, enterprises should proactively adopt GDPR standards as best practice, engage external experts to leverage standardized frameworks, and form cross-functional teams (Legal, IT, Business) to conduct assessments. The priority should be to inventory and assess all AI-driven and large-scale monitoring activities.

Winners Consulting specializes in high-risk processing for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact