396 件 ・ 関連度順
概要と使いどころ
Montana Consumer Data Privacy Act (MTDPA) compliance. Lowest consumer threshold at 50,000 consumers. Covers sensitive data consent, universal opt-out recognition, consumer rights, controller obligations, 60-day cure period, and AG enforcement. Effective October 1, 2024.
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 legal hold and data preservation processes including triggering events, custodian notification, hold-in-place technical implementation, release procedures, and interaction with retention schedules. Covers litigation hold registers, compliance monitoring, and Art. 17(3)(e) exception documentation. Activate for legal hold, litigation preservation, data freeze, e-discovery hold 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。
Kentucky Consumer Privacy Protection Act (KPPA) compliance. Effective January 1, 2026. Covers consumer rights, controller thresholds at 100,000 consumers, sensitive data processing consent, cure period provisions, and AG enforcement framework.
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。
Delaware Personal Data Privacy Act (DPPA) compliance implementation. Covers consumer rights (access, correct, delete, portability, opt-out), controller obligations, processor requirements, sensitive data consent, universal opt-out recognition, DPIA requirements, and AG enforcement. Effective January 1, 2025, with no revenue threshold for applicability.
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 biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, 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。
Conducts Data Protection Impact Assessments for employee monitoring systems per EDPB Guidelines 3/2019 on workplace data processing. Covers video surveillance, email monitoring, GPS tracking, keystroke logging, and productivity tools. Applies proportionality testing under Art. 35 GDPR. Keywords: DPIA, employee monitoring, surveillance, proportionality, EDPB, workplace privacy, keystroke logging, GPS tracking.
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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。
Provides GDPR Article 13 information at the point of direct data collection, covering all required elements under Art. 13(1)(a)-(f) and Art. 13(2)(a)-(g), layered notice design, and timing requirements. Activate for Art. 13, direct collection notice, privacy notice at collection, data collection information 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。
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。
Guides managing conflicting privacy requirements across jurisdictions. Covers data localisation vs transfer freedom, consent standards variation, age thresholds, breach timelines, and resolution frameworks for incompatible obligations. Keywords: conflicting laws, data localisation, consent variation, age thresholds, resolution framework.
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。
Manages deletion requests for children's personal data. Covers parental-initiated versus child-initiated requests, age of capacity assessment, identity verification, scope determination, third-party notification obligations, and regulatory timelines under GDPR Art. 17, COPPA Section 312.6, and UK AADC Standard 15. Keywords: deletion, children, right to erasure, parental request, data deletion, COPPA.
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 data deletion workflows for GDPR Article 17 right to erasure and retention period expiry. Covers cascading deletion across dependent systems, dependency handling for referential integrity, confirmation logging, and audit trail generation. Activate for automated deletion, erasure automation, data purge, retention expiry 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 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.
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。
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.
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 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.
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。
Manages privacy compliance for employee background checks including criminal record processing under Art. 10 GDPR, DBS checks (UK), national law variations, and reference verification. Applies proportionality and data minimisation to pre-employment screening, defines retention limits, and addresses role-based necessity assessments. Keywords: background check, criminal record, Art. 10, DBS, pre-employment screening, vetting, data minimisation, proportionality.
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。
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.
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。
Detects sensitive data stored insecurely on mobile devices (Android/iOS). Trigger on: SharedPreferences, NSUserDefaults, SQLite, Room DB, DataStore, Core Data, Keychain misconfiguration, external storage, backup exposure, plaintext files, unencrypted databases, adb backup, iCloud backup, NSFileProtection, EncryptedSharedPreferences, SQLCipher, allowBackup, FLAG_SECURE, keyboard cache, sensitive logs. Covers MASVS-STORAGE-1 (local storage) and MASVS-STORAGE-2 (exposure to unauthorized actors).
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Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive information types with regex patterns, Dağıt:s endpoint DLP rules to control file operations on Windows and macOS devices, and monitors policy effectiveness through Activity Expl...
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Complete knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping
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AIエージェントの危険なコマンドを実行前に確認し、ファイル編集を指定ディレクトリ内に制限します。単独または併用のモードで、本番作業や自律実行時の操作を管理します。