'data-protection' 검색 결과

Guides privacy audit evidence collection processes including evidence planning, sampling strategies, documentation standards, chain of custody, interview techniques, system walkthrough procedures, and evidence evaluation. Covers ISO 19011 evidence categories (records, statements of fact, observations) and ISACA audit evidence requirements for privacy compliance assessments. Keywords: audit evidence, evidence collection, sampling, chain of custody, audit documentation, interview techniques.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides assessment and application of GDPR Article 49 derogation conditions for international data transfers in the absence of adequacy decisions or appropriate safeguards. Covers explicit consent, contract necessity, public interest, vital interests, public register, and compelling legitimate interests with restrictive interpretation per EDPB Guidelines 2/2018. Keywords: Art. 49, derogations, transfer exceptions, explicit consent, compelling legitimate interests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Systematic application of the eight privacy design patterns per Hoepman: minimize, hide, separate, abstract, inform, control, enforce, and demonstrate. Covers pattern selection methodology per processing activity, mapping to GDPR principles, and practical implementation guidance for privacy-by-design system architecture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides APEC Cross-Border Privacy Rules system certification process including self-assessment against the APEC Privacy Framework principles, accountability agent selection, intake questionnaire completion, certification decision, annual recertification, and Global CBPR Forum transition. Keywords: APEC, CBPR, cross-border privacy, accountability agent, certification, Global CBPR.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides management of cross-border data transfers under Asia-Pacific regulatory frameworks including APEC CBPR, ASEAN Model Contractual Clauses, Japan APPI supplementary rules, South Korea PIPA provisions, and Thailand/Singapore PDPA mechanisms. Keywords: APEC CBPR, ASEAN MCCs, APPI, PIPA, PDPA, APAC transfers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Managing consent for analytics cookies and implementing privacy-preserving measurement. Covers GA4 privacy configuration, consent mode fallback behavior, aggregate reporting alternatives, and cookieless measurement approaches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assesses lawful basis for AI training data processing per EDPB April 2025 report on LLMs and general-purpose AI. Covers legitimate interest balancing tests, consent challenges for ML training, public dataset assessment, and web scraping lawfulness. Keywords: AI training data, lawful basis, EDPB LLM, legitimate interest, consent, web scraping.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides combined DPIA and AI Act conformity assessment template with integrated risk scoring matrix. Covers GDPR Art. 35 DPIA elements, AI Act high-risk system requirements, mitigation measures, and human oversight assessment. Keywords: DPIA template, conformity assessment, risk scoring, AI Act, combined assessment, high-risk AI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pre-deployment privacy compliance checklist for AI/ML systems covering DPIA completion, lawful basis verification, transparency notices, human oversight mechanisms, bias testing, and post-deployment monitoring setup. Keywords: AI deployment, privacy checklist, go-live, model deployment, compliance gate.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implements data subject rights mechanisms for AI systems including right to explanation of AI decisions, contestation procedures, human review, model output correction, and training data access. Covers GDPR Arts. 15-22 and AI Act Art. 86. Keywords: data subject rights, AI explanation, contestation, human review, training data access, model correction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manages backup and archive data under retention schedules and erasure obligations. Covers the technical infeasibility exception for backup deletion, backup cycle alignment with retention periods, restore-and-delete procedures, and interim protective measures during backup retention. Activate for backup deletion, archive erasure, backup retention, restore and delete, technical infeasibility queries.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates and implements age estimation and verification technologies for online services. Covers facial age estimation, digital ID verification, self-declaration with risk assessment, AI-based age estimation, and the accuracy versus privacy tradeoff. Includes ICO guidance and euCONSENT framework. Keywords: age verification, age estimation, facial analysis, digital ID, children, online safety.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides assessment of third-country adequacy decisions under GDPR Article 45 for international data transfers. Covers the current EC adequacy decisions list, adequacy assessment criteria, partial adequacy handling, and monitoring of adequacy decision reviews. Keywords: adequacy decision, Article 45, third country, adequate protection, EC adequacy list.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.