1.2万 件 ・ 関連度順
概要と使いどころ
Guides the Art. 36 prior consultation process when a DPIA indicates high residual risk that cannot be mitigated. Covers required documentation per Art. 36(3), the 8-week DPA response timeline, outcome management, and interaction protocols with supervisory authorities. Keywords: prior consultation, Art. 36, supervisory authority, DPA, high residual risk, DPIA escalation, consultation documentation.
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。
Processes GDPR Article 16 right to rectification requests, covering verification of corrected data accuracy, notification to recipients under Article 19, timeline management, and completion of incomplete data. Activate for rectification, correction request, inaccurate data, Art. 16, data correction 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。
Guides the three-part Legitimate Interest Assessment (LIA) required under GDPR Article 6(1)(f): purpose test, necessity test, and balancing test. Activate when evaluating legitimate interest as a lawful basis, conducting LIA reviews, or documenting proportionality analysis. Keywords: LIA, legitimate interest, balancing test, necessity test, purpose test, Article 6(1)(f).
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 establishment and management of joint controller arrangements under GDPR Article 26, including determination of joint controllership, allocation of responsibilities, and transparency obligations. Activate when two or more controllers jointly determine purposes and means of processing, or when evaluating shared data platforms. Keywords: joint controller, Article 26, shared responsibility, arrangement, joint determination.
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 health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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。
Architecture patterns for GDPR Article 5(1)(c) data minimization and Article 25(1) data protection by design. Covers field-level encryption, data masking, aggregation, pseudonymization per Article 4(5), and anonymization per Recital 26. Includes ENISA pseudonymization techniques and a data minimization assessment matrix.
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 data subject and stakeholder consultation requirements during Data Protection Impact Assessments under GDPR Article 35(9). Covers consultation planning, data subject engagement methods, DPO involvement per Art. 35(2), and documentation of views received. Keywords: DPIA consultation, stakeholder engagement, Art. 35(9), data subject views, DPO advice, public consultation, representative groups.
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。
Governs employee health data processing for fitness-for-work assessments, occupational health surveillance, COVID testing legacy programmes, and absence management. Applies Art. 9(2)(b) employment obligations and Art. 9(2)(h) health professional exceptions. Covers data minimisation, occupational health provider relationships, and return-to-work procedures. Keywords: health data, Art. 9, occupational health, fitness-for-work, special category, employment, sickness absence.
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。
Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d). Covers mitigation measure identification, implementation tracking, residual risk acceptance, and Art. 36 prior consultation triggers. Keywords: DPIA mitigation, risk treatment, residual risk, Art. 35(7)(d), safeguards, mitigation tracking, prior consultation.
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 Data Subject Access Request procedures for employee requests under Art. 15 GDPR. Covers scope of disclosable HR records, emails, CCTV footage, performance reviews, monitoring data, and training records. Implements third-party data redaction, legal professional privilege, exemptions for ongoing proceedings, and the one-month response timeline. Keywords: DSAR, subject access request, Art. 15, employee records, redaction, privilege, HR data, SAR.
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 creation and review of data processing agreements under GDPR Article 28(3), covering all eight mandatory clauses. References the 2021 Standard Contractual Clauses and provides a compliance checklist for processor contracts. Activate when onboarding processors, reviewing DPAs, or auditing processor compliance. Keywords: DPA, data processing agreement, Article 28, processor, mandatory clauses, standard contractual clauses.
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 a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment 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。
Builds comprehensive data inventory per GDPR Art. 30 Records of Processing Activities. Covers system-by-system discovery, data flow diagramming, third-party identification, and legal basis per category. Keywords: data inventory, data mapping, Art 30, RoPA, data flow, processing activities.
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 development and approval of Binding Corporate Rules under GDPR Article 47 for intra-group international data transfers. Covers Art. 47(2)(a)-(n) content requirements, BCR approval process with lead supervisory authority, and WP256/WP257 referentials. Keywords: BCR, binding corporate rules, intra-group transfers, Art. 47.
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。
Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB Guidelines 3/2019 on video surveillance. Keywords: biometric, facial recognition, fingerprint, DPIA, Art. 9, special category, EDPB Guidelines 3/2019.
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 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.
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。
Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.
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。
Create diagrams and visualizations using Mermaid syntax. Use when generating flowcharts, sequence diagrams, class diagrams, entity-relationship diagrams, Gantt charts, or any visual documentation. Triggers on Mermaid, flowchart, sequence diagram, class diagram, ER diagram, Gantt chart, diagram, visualization.
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。
This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.
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。
Extract clean article content from URLs (blog posts, articles, tutorials) and save as readable text. Use when user wants to download, extract, or save an article/blog post from a URL without ads, navigation, or clutter.
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。
Fix slow Flutter app startup caused by blocking network operations during initialization. Use when: (1) App takes several seconds to show first frame, (2) Startup logs show sequential network operations (WebSocket connections, API calls), (3) Services initialize during startup that aren't needed until user authenticates. Covers: converting sequential network ops to parallel with Future.wait(), deferring service initialization until actually needed (lazy init), and identifying blocking operations in Riverpod/provider initialization chains.
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。
A high-level conceptual mapping from Active Directory attack techniques to the compliance controls they touch. Kerberoasting relates to authentication and logging, so it brushes ENS op.acc.5 / op.exp.8, NIS2 Art.21(2)(h), DORA RTS Art.9 / Art.21. Use this when a reader wants to understand which regulatory control an AD finding relates to, or to add an orientation note to a technical finding. This is a conceptual, orientative mapping only; it is NOT an auditor-defensible, curated, ID-by-ID control matrix. Covers ENS (op.acc.*, op.exp.*), NIS2 (Directive 2022/2555 Art.21), and DORA (RTS 2024/1774).
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。