profileringsdata

Profileringsdata refers to personal data that are used in profiling activities, i.e., automated processing intended to analyze or predict aspects concerning an individual. Profiling typically combines multiple data types and sources to draw inferences about preferences, behavior, attributes or risks, and may cover both directly provided information and data inferred from online or offline activities.

Common data elements involved in profileringsdata include identifiers (such as name, contact details, and account numbers), demographic information (age, gender, income), behavioral data (web browsing, app usage, purchase history), location data, and, in some cases, health or biometric information. Derived data like risk scores, segmentation labels, or predictive indicators can also constitute profileringsdata when they result from automated processing aimed at describing or predicting an individual.

The processing of profileringsdata is governed by data protection rules in many jurisdictions, notably under the EU General Data Protection Regulation (GDPR). Profiling is allowed when there is a lawful basis, such as consent or performance of a contract, or where legitimate interests are balanced against privacy rights. Special care is required for high-risk profiling, and organizations may need to conduct a data protection impact assessment (DPIA). Transparency, purpose limitation, data minimization, and appropriate security measures are central, and individuals often have rights to access, rectify, erase, restrict processing, or object to profiling, including automated decision-making.

Applications of profileringsdata span marketing and personalized services, credit and fraud risk assessment, insurance underwriting, recruitment, and security monitoring. Critics highlight risks of discrimination, bias, privacy invasion, and over-reliance on automated judgments, underscoring the need for governance, accountability, and meaningful human oversight in profiling practices.