「privacy」の検索結果

Implementation guide for CNIL cookie guidelines compliance. References the EUR 150M Google fine and EUR 60M Meta fine. Covers equal prominence accept/reject buttons, cookie wall prohibition, 6-month reconsent intervals, essential cookies exemption, and detailed CNIL Deliberation No. 2020-091 requirements.

日本語の概要は準備中です。原文の説明を表示しています。

Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.

日本語の概要は準備中です。原文の説明を表示しています。

Designs and executes tabletop breach simulation exercises for testing organizational breach response capabilities. Covers scenario creation with realistic inject timelines, participant role assignment, communication testing across internal and external channels, decision-point evaluation, and after-action report generation. Keywords: tabletop exercise, breach simulation, incident response testing, scenario design, after-action report.

日本語の概要は準備中です。原文の説明を表示しています。

Executes breach notification under California Civil Code Section 1798.82 (California data breach notification law). Covers data elements triggering notification, timing requirements (most expedient time possible), AG notification for 500+ California residents, specific content and format requirements, and substitute notice provisions. Keywords: California, breach notification, Cal. Civ. Code 1798.82, attorney general, CCPA, data elements.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for obtaining explicit consent for international data transfers under GDPR Article 49(1)(a). Covers informed consent requirements including risks of transfers without adequacy decisions or appropriate safeguards, specific destination country disclosure, and the narrow scope of derogation-based transfers.

日本語の概要は準備中です。原文の説明を表示しています。

Guides DPIA for migrating personal data to cloud infrastructure covering controller-processor analysis under Art. 28, international transfer assessment, encryption requirements, and shared responsibility model evaluation. Activate for cloud adoption, SaaS procurement, or data centre migration projects. Keywords: cloud migration, DPIA, Art. 28, processor, encryption, shared responsibility, SaaS, IaaS, PaaS.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive methodology for auditing website cookies and tracking technologies. Covers automated scanning, cookie categorization, lifecycle documentation, and compliance gap analysis referencing the Planet49 CJEU ruling (C-673/17).

日本語の概要は準備中です。原文の説明を表示しています。

Implements strict data minimization and retention limits for children's personal data under GDPR Art. 5(1)(c), Recital 38, UK AADC Standard 8, and COPPA Section 312.7. Covers strict necessity testing, shorter retention periods, limited profiling, parental dashboard design, and automated deletion. Keywords: data minimization, children, retention, necessity test, parental dashboard.

日本語の概要は準備中です。原文の説明を表示しています。

Guides the end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.

日本語の概要は準備中です。原文の説明を表示しています。

Conducts digital forensics investigations following a personal data breach, covering evidence preservation, chain of custody documentation, log analysis, scope determination, and root cause analysis. References industry-standard tools including Splunk, ELK Stack, and Wireshark. Provides forensic workflow from initial evidence collection through final investigation report. Keywords: digital forensics, breach investigation, evidence preservation, chain of custody, root cause analysis, Splunk, ELK, Wireshark.

日本語の概要は準備中です。原文の説明を表示しています。

Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Manages GDPR Article 22 rights related to solely automated decision-making and profiling, including identification of automated decisions, meaningful human oversight implementation, logic explanation requirements, and contestation mechanisms. Activate for automated decision, profiling, Art. 22, algorithmic decision, AI decision queries.

日本語の概要は準備中です。原文の説明を表示しています。

Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Preparing EU AI Act compliance documentation for high-risk AI systems. Covers Annex III classification, technical documentation under Art. 11, conformity assessment, risk management systems, and CE marking requirements. Keywords: EU AI Act, high-risk AI, Annex III, conformity assessment, CE marking.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。