「sse」の検索結果

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。

Guides compliance with country-specific data localization requirements across key jurisdictions including Russia (242-FZ), China (PIPL Art. 40, CAC measures), India (DPDP Act), Turkey, Vietnam, and Indonesia. Covers localization assessment, architecture design, and exemption procedures. Keywords: data localization, data residency, PIPL, 242-FZ, cross-border restrictions.

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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.

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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.

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Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.

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Technical enforcement of GDPR Article 5(1)(b) purpose limitation principle. Covers purpose-tagged data stores, access control per purpose, Article 6(4) compatibility assessment factors, and system design for preventing purpose creep. Includes purpose binding architecture and compatibility test implementation.

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 guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation mapping to privacy design patterns, and step-by-step process.

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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.

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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。

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.

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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。

Guides audit follow-up and verification processes including follow-up scheduling, remediation effectiveness testing, finding closure criteria, re-testing procedures, status reporting, and escalation of unremediated findings. Implements IIA Standard 2500 monitoring requirements and ISO 19011 follow-up guidance for privacy audit engagements. Keywords: audit follow-up, verification testing, finding closure, remediation effectiveness, re-testing, follow-up 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。

Executes the GDPR Article 33 mandatory breach notification to the supervisory authority within 72 hours of becoming aware of a personal data breach. Covers required notification content, deadline calculation, risk assessment for notification threshold, and DPO involvement. Keywords: GDPR, Article 33, breach notification, 72 hours, supervisory authority, DPO, EDPB.

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Evaluates and implements age estimation and verification technologies for online services. Covers facial age estimation, digital ID verification, self-declaration with risk assessment, AI-based age estimation, and the accuracy versus privacy tradeoff. Includes ICO guidance and euCONSENT framework. Keywords: age verification, age estimation, facial analysis, digital ID, children, online safety.

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Guides APEC Cross-Border Privacy Rules system certification process including self-assessment against the APEC Privacy Framework principles, accountability agent selection, intake questionnaire completion, certification decision, annual recertification, and Global CBPR Forum transition. Keywords: APEC, CBPR, cross-border privacy, accountability agent, certification, Global CBPR.

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。

Audit preparation register: required document, period covered, request and receipt dates, preparer and reviewer, auditor queries and adjustments. Use for assembling an audit file.

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。

AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture.

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。

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data 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。

Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

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。

Authorized security assessment of REST, GraphQL, WebSocket, and SOAP APIs: discovery, authentication and authorization flaws (BOLA/IDOR, JWT/OAuth), rate-limit testing, and a structured multi-phase methodology.

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。

XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.

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。

Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).

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 the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality 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。

After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

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。