「pia」の検索結果

Use when a user explicitly requests Sepia refactor for minimal in-place prose revision.

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 a user explicitly requests Sepia write to create new prose.

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 a user explicitly requests Sepia recreate for a full rewrite.

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。

GDPR Art. 35 Data Protection Impact Assessment with threshold checking, risk registers, and EDPB criteria scoring. Use for DPIA evaluations.

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。

Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.

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 this skill when the user asks to verify PiAgent skill loading, asks for the PiAgent E2E skill, or asks for the skill marker.

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 Impact Assessment for vendor and third-party data processing arrangements. Covers processor due diligence, Data Processing Agreement (DPA) requirements under GDPR Article 28, sub-processor management, cross-border vendor transfers, cloud service provider assessments, and ongoing vendor monitoring. Keywords: vendor PIA, processor assessment, DPA, Article 28, sub-processor, cloud privacy, third-party risk.

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 Impact Assessment for large-scale systematic monitoring under GDPR Article 35(3)(c). Covers CCTV and video surveillance, employee monitoring, location tracking, internet monitoring, and behavioural analytics. Applies EDPB WP248rev.01 criteria for systematic monitoring of publicly accessible areas. Keywords: DPIA, large-scale monitoring, CCTV, employee monitoring, systematic monitoring, surveillance, location tracking.

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 Privacy Threshold Analysis screening process to determine whether a full DPIA is required. Provides a quick-screen questionnaire, threshold criteria based on WP248rev.01, escalation triggers, and documentation requirements. Activate when evaluating new processing activities, system changes, or procurement decisions. Keywords: PTA, privacy threshold analysis, DPIA screening, quick-screen, threshold criteria, WP248, escalation triggers.

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 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。

Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation 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。

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。

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。

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。

Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.

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.

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。

Apocalyptic and dystopian world art direction for Blender including post-apocalyptic, zombie, alien invasion, post-human, dystopian, and utopian contrast sets.

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。

Apiary integration. Manage Organizations, Users, Goals, Filters. Use when the user wants to interact with Apiary data.

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。

Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes.

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。

Comparativa de los cuatro enfoques psicoterapéuticos reglamentarios en Alemania: Terapia Cognitivo-Conductual, Psicoterapia de Orientación Psicodinámica, Psicoanálisis y Terapia Sistémica — Guía de orientación.

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。

Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.

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。

GDPR and German DSGVO compliance automation. Scans codebases for privacy risks, generates DPIA documentation, tracks data subject rights requests with Art. 12(3) one-month deadlines. Use when running GDPR compliance assessments, privacy audits, data protection planning, DPIA generation, or data subject rights (DSAR) management (e.g., 'check this service for GDPR risks', 'track an access request deadline'). Final compliance determinations route to the DPO or legal counsel.

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 compliance checklists for business scenarios involving GDPR, PIPL, or advertising/data laws. Outputs a structured checklist with check items, legal basis, risk levels, and actionable recommendations. Triggered by requests like "run a compliance check," "GDPR/PIPL compliance," "pre-launch review," "privacy impact assessment (PIA/DPIA)," or asking if a feature is compliant.

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。