709 件 ・ 関連度順
概要と使いどころ
Guides compliance with Australia's Privacy Act 1988 including the 2024 reform amendments. Covers automated decision-making transparency, children's privacy code, individual rights expansion, enforcement strengthening, and the Australian Privacy Principles (APPs). Keywords: Australia Privacy Act, APPs, OAIC, automated decisions, children privacy code, privacy reform.
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。
Design privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential privacy implementation for GDPR-compliant statistical analysis.
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。
처리방침·이용약관 자동 생성 진입점. 호출 즉시 6개 하위 스킬(privacy-kr·privacy-eu·privacy-us·privacy-jp·privacy-global·privacy-global-jp)을 번호 메뉴로 제시하고, 번호 입력 즉시 해당 스킬 인터뷰로 직행한다.
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。
Maps the US federal privacy landscape including sectoral laws (HIPAA, GLBA, FERPA, COPPA, FCRA, ECPA, VPPA), FTC Section 5 enforcement, proposed federal comprehensive legislation, and the interaction between federal and state privacy regimes. Keywords: federal privacy, HIPAA, GLBA, FERPA, COPPA, FCRA, FTC, sectoral, preemption.
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 implementation of the NIST Privacy Framework IDENTIFY function covering ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Maps NIST PF controls to GDPR requirements for dual-framework compliance. Keywords: NIST Privacy Framework, IDENTIFY function, ID.BE, ID.DA, ID.IM, ID.RA, privacy risk assessment, data actions.
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 privacy program maturity assessment using the AICPA/CIPT Privacy Maturity Model with five levels: Ad Hoc, Repeating, Defined, Managed, and Optimized. Covers assessment methodology across ten privacy domains, scoring criteria, gap analysis, maturity roadmap generation, and benchmarking against industry peers. Keywords: privacy maturity, AICPA, maturity model, assessment, roadmap, benchmarking.
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 privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational, compliance, risk, and strategic privacy metrics across the program lifecycle. Keywords: privacy metrics, KPIs, benchmarking, executive reporting, dashboard, program effectiveness.
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 HIPAA Privacy Rule requirements for research uses of protected health information under 45 CFR §164.512(i). Covers IRB and Privacy Board waivers of authorization, individual authorization for research, limited data set and data use agreements, preparatory to research provisions, and decedent research provisions. Keywords: HIPAA research, IRB waiver, Privacy Board, authorization, limited data set, preparatory research, de-identification, Common Rule.
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。
Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.
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。
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.
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。
Expert ISO 27701 Privacy Information Management System (PIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 27701:2025, ISO/IEC 27701:2019, privacy information management, PIMS certification, PII controller or processor obligations, privacy risk assessment, Statement of Applicability for privacy, privacy by design, data subject rights, DPIA, records of processing activities, transitioning from ISO 27701:2019, GDPR alignment with ISO 27701, or any privacy management system topic. Also trigger for questions about Annex A.1 (controller controls), A.2 (processor controls), A.3 (shared security controls), or implementing a standalone PIMS without ISO 27001. When in doubt, use this skill — it covers the full ISO 27701 lifecycle from gap assessment through certification.
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 zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.
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。
Expert ISO 27701 Privacy Information Management System (PIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 27701:2025, ISO/IEC 27701:2019, privacy information management, PIMS certification, PII controller or processor obligations, privacy risk assessment, Statement of Applicability for privacy, privacy by design, data subject rights, DPIA, records of processing activities, transitioning from ISO 27701:2019, GDPR alignment with ISO 27701, or any privacy management system topic. Also trigger for questions about Annex A.1 (controller controls), A.2 (processor controls), A.3 (shared security controls), or implementing a standalone PIMS without ISO 27001. When in doubt, use this skill — it covers the full ISO 27701 lifecycle from gap assessment through certification.
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。
Audits a website for compliance with Azerbaijan's Law on Personal Data No. 998-IIIQ and, where applicable, EU GDPR plus ePrivacy/cookie consent rules. Inventories the privacy documents present (privacy policy, cookie policy, cookie banner, consent flow, controller and DPO contact, data subject rights channel, cross-border transfer disclosures, AZ operator-registration references) and scores each against the applicable statutory requirements. Produces a dual-layer report: a plain-language traffic-light summary for business owners plus a clause-by-clause findings table with article-level citations for lawyers. Assessment-only — no drafting. Use whenever the user shares a URL or privacy/cookie policy text for an AZ-based or AZ-targeted site; also when the user mentions an .az domain, Law 998, the AZ State Register, ePrivacy, an Art. 27 EU representative, or asks "is my site GDPR compliant", "do I need to register as an operator in Azerbaijan", or "is our cookie banner lawful" — even without the word "audit".
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。
처리방침·이용약관 자동 생성 스킬 패키지 (v4.0). 호출 시 privacy-kr·privacy-eu·privacy-us·privacy-global 4개 하위 스킬을 번호 메뉴로 제시하고 번호 입력 즉시 해당 스킬 인터뷰로 직행. 한국 PIPA + EU GDPR + US CCPA/CPRA 대응.
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 SOC 2 Type II Privacy Trust Services Criteria preparation and audit execution. Covers AICPA TSP Section 100 Privacy criteria P1-P8 including notice, choice/consent, collection, use/retention/disposal, access, disclosure, security, and quality. Includes evidence collection, control testing, and report review. Keywords: SOC 2, privacy criteria, TSP, AICPA, Type II, trust services.
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。
Tracks and monitors US state privacy legislation across all 50 states, DC, and territories. Covers enacted comprehensive privacy laws (California CCPA/CPRA, Virginia VCDPA, Colorado CPA, Connecticut CTDPA, Utah UCPA, and subsequent enactments), pending bills, effective dates, and key requirement differences. Keywords: state privacy law, CCPA, CPRA, VCDPA, CPA, CTDPA, UCPA, multi-state.
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。
Build privacy KPI dashboards tracking DSAR volume and response time, breach count and severity, DPIA completion rate, training coverage, and consent rates. Includes metric definitions, data collection patterns, visualization designs, and executive reporting templates for privacy program measurement.
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。
Implement the NIST Privacy Framework GOVERN function covering GV.AT awareness and training, GV.MT monitoring and review, GV.PO policy development, and GV.RR roles and responsibilities. Provides governance structure templates, training programs, and accountability frameworks for privacy governance.
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。
Implement the NIST Privacy Framework COMMUNICATE function covering CM.AW awareness raising and CM.PO communication policies. Provides transparency mechanisms, stakeholder engagement frameworks, privacy notice templates, and communication workflow guidance.
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 HITECH Act privacy and security requirements including breach notification expansion, four-tier penalty structure, state attorney general enforcement authority, EHR meaningful use privacy conditions, and business associate direct liability. Keywords: HITECH Act, breach notification, penalty tiers, state AG enforcement, meaningful use, EHR privacy.
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。
Implementation guide for Global Privacy Control (GPC) automated opt-out signal per CPRA Section 1798.135(e). Covers Sec-GPC HTTP header detection, JavaScript navigator.globalPrivacyControl API, and state-specific requirements for CA, CO, CT, MT, TX, and OR. Includes server-side detection code and compliance 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。
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.
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。