A privacy and data-governance hub for understanding how AI systems use, transform, protect, and expose data.
25
Terms
9
Comparisons
8
Questions
Data privacy in AI focuses on what data enters a system, how it is transformed, whether people remain identifiable, and what controls protect the data throughout the lifecycle. It bridges privacy law, data engineering, model training, and vendor oversight.
AI systems can create privacy risk even when the output looks harmless, because training data, prompts, logs, embeddings, and retrieved documents can expose sensitive information.
Useful for GDPR analysis, AI Act data governance reviews, DPIA or PIA work, vendor assessments, RAG source governance, and incident response.
Data categories that determine privacy duties and heightened safeguards in AI systems.
Techniques and governance concepts used to reduce identifiability and exposure risk.
Controls for knowing where data came from, how it is used, and where it is stored.
A side-by-side comparison of Personal Data and Non-Personal Data. Understand how identifiability changes privacy analysis, lawful use, and AI governance controls.
A side-by-side comparison of Personal Data and Special Categories of Personal Data. Understand why all special-category data is personal data, but not all personal data receives the same heightened protection.
A side-by-side comparison of Anonymisation / De-identification and Pseudonymisation. Understand the difference between reducing identifiability and replacing identifiers while keeping re-identification possible through separate information.
A side-by-side comparison of Biometric Data and Special Categories of Personal Data. Understand how biometric identifiers relate to the broader set of sensitive personal data protected in EU privacy analysis.
A side-by-side comparison of Data Anonymisation and Differential Privacy. Understand how reducing identifiability in data differs from limiting what can be inferred from computation or model outputs.
A side-by-side comparison of Synthetic Data and Personal Data. Understand how artificially generated data differs from information relating to an identified or identifiable person.
A side-by-side comparison of General Data Protection Regulation (GDPR) and EU AI Act. Understand how personal data protection obligations differ from risk-based AI system and general-purpose AI model obligations.
A side-by-side comparison of Production Data and Training Data. Understand how the concepts differ, when each term applies, and why the distinction matters for AI governance, evaluation, or system design.
A side-by-side comparison of Data Provenance and Data Source Register. Understand how the concepts differ, when each term applies, and why the distinction matters for AI governance, evaluation, or system design.
Use Anonymisation / De-identification when discussing reduced or removed identifiability; use Pseudonymisation when personal data can still be attributed with protected additional information.
Use Data Anonymisation when modifying data to reduce identifiability; use Differential Privacy when limiting individual inference from outputs through a privacy framework.
Synthetic data is artificially generated data designed to resemble selected patterns, structures, or statistical properties of real data.
Data provenance is the recorded history of data, including its origin, creation, transformations, movements, and changes over time. In AI governance, provenance helps verify dataset lineage, assess trustworthiness, support audits, and understand how digital content or model inputs were produced.
Data residency and sovereignty concern where data is stored, processed, and governed, and which legal regimes apply to it. For regulated AI use, these concepts affect cloud-region selection, cross-border transfers, contractual controls, and compliance with sectoral or national requirements.
Use GDPR when the issue is processing personal data; use the EU AI Act when the issue is AI system risk category, safety, transparency, governance, or conformity obligations.
Training data is the dataset used to teach or fit a machine learning model by adjusting its learnable parameters. Its quality, quantity, diversity, labeling, and provenance strongly influence the performance, reliability, and compliance posture of the resulting model.
Data encryption is the transformation of readable data into an encoded form that is unintelligible without an authorized key or decryption process. It is used to protect sensitive information from unauthorized access during storage, transmission, or processing.