「mechanism」の検索結果

Guides implementation of GDPR Article 42-43 data protection certification mechanisms including accredited certification bodies, criteria development, and periodic review. Activate when pursuing privacy certifications, evaluating certification bodies, or developing certification criteria. Keywords: certification, Article 42, Article 43, accreditation, seal, privacy mark.

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 GDPR Article 56 one-stop-shop mechanism for determining lead supervisory authority in cross-border processing. Covers main establishment identification and cooperation. Activate when processing across EU borders. Keywords: one-stop-shop, Article 56, lead authority, cross-border.

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。

Complete CCPA/CPRA compliance implementation covering California Civil Code §1798.100-199. Includes consumer rights framework, business obligations, service provider and contractor requirements, enforcement mechanisms, and CPPA rulemaking. Triggers on CCPA, CPRA, California privacy, consumer rights.

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 building a consent record-keeping system to demonstrate valid consent per GDPR Article 7(1). Covers required fields including timestamp, version, purpose, mechanism, and identity. Implements audit-ready consent receipts per the Kantara Initiative Consent Receipt Specification and supervisory authority expectations.

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 Kantara Initiative consent receipt specification including machine-readable receipt structure, JWT-based verification mechanisms, receipt lifecycle management, and integration patterns for consent management platforms. Supports ISO/IEC 27560 consent record information structure.

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。

Colorado Privacy Act (CPA) compliance implementation. Covers universal opt-out mechanism required since July 2024, profiling opt-out rights, sensitive data consent requirements, AG rulemaking under 4 CCR 904-3, and consumer rights framework. Effective July 1, 2023.

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 compliance with China's Personal Information Protection Law (PIPL, effective 1 November 2021). Covers consent requirements, cross-border transfer mechanisms (CAC security assessment, standard contracts, certification), separate consent triggers, and critical information infrastructure obligations. Keywords: PIPL, China data protection, CAC security assessment, cross-border transfer, separate consent, CIIO.

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 GDPR Article 7(3) consent withdrawal mechanisms. Covers the equal ease requirement ensuring withdrawal is as easy as giving consent, one-click withdrawal implementation, cascading effects on downstream processing, third-party notification workflows, and technical architecture for real-time consent revocation.

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 systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.

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。

Develops data classification policies with tiered handling (public, internal, confidential, restricted), labeling requirements, enforcement mechanisms, and procedures per tier. Covers policy governance, exception handling, and compliance monitoring. Keywords: classification policy, data tiers, handling procedures, labeling, enforcement, data governance, information security.

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。

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 compliance with Brazil's Lei Geral de Proteção de Dados (LGPD, Lei 13.709/2018). Covers the 10 lawful bases under Art. 7, DPO appointment, ANPD enforcement, data subject rights under Arts. 17-22, and international transfer mechanisms. Keywords: LGPD, Brazil data protection, ANPD, lawful bases, data subject rights, international 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 age-gating mechanisms for online services to restrict access based on user age. Covers hard gates versus soft gates, neutral age prompts, re-verification triggers, circumvention prevention, and regulatory requirements under GDPR, COPPA, UK Online Safety Act, and DSA. Keywords: age gate, age restriction, neutral prompt, children, online services, access control.

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。

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.

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 a Seedance 2.0 science, education, or general-knowledge explainer using layered handmade papercraft, miniature diorama depth, physical paper mechanisms, clear knowledge beats, restrained miniature camera, tactile sound, and stable reference roles. Use for one 4–15 second clip or a longer multi-clip plan; not for flat vector cartoons, live action, or paper-texture-only styling.

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。

Surfaces Han's shared standard for explaining technical work to a reader who will not implement it into the calling skill's own context, so the caller writes its escalations, confirmation turns, and stops as a concrete outcome the reader could observe rather than as a mechanism. Use when a skill is about to ask a person a question, stop for an input, or explain a technical consequence to someone who will not open the code. Governs what a run says to a person in a turn, where readability-guidance governs the shape of a written deliverable. Runs in the caller's context and hands control straight back; it does not produce a deliverable of its own, rewrite anything, or judge the caller's work. Carries guidance only and adds no self-check step.

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。

Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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 test failures when adding retry/fallback logic causes mock call count mismatches. Use when: (1) Test fails with "Expected: <1>, Actual: <2>" or similar call count errors after adding retry or fallback mechanisms, (2) MissingStubError appears for methods in new fallback code paths, (3) Mockito verify().called(N) assertions fail after behavior changes. Applies to Dart/Flutter with Mockito, but pattern is universal.

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。

Use when implementing feedback mechanisms for AI responses.

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。

Analyze failing tests to detect functional bugs in code. Takes repository and failing test output as input, analyzes execution behavior, assertions, and stack traces to identify suspicious code regions and root causes. Use when debugging test failures, investigating regression bugs, or understanding why tests fail. Explains the bug mechanism, identifies affected code, and suggests fixes based on test expectations vs actual behavior.

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。

Analyzes code to identify security-critical time intervals and timing vulnerabilities in authentication, authorization, and time-sensitive security operations. Use this skill when reviewing code for proper timeout enforcement, token expiration, session management, rate limiting, password reset validity, or any time-sensitive security mechanism. Detects missing expiration checks, excessive timeout values, lack of rate limiting, client-side only validation, hardcoded timeouts, and timing attack vulnerabilities. Triggers when users ask to check security timeouts, verify token expiration handling, audit session timeout implementation, review rate limiting, or analyze time-based security controls.

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。