mukul975
278 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
mukul975ai-automated-decisions
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-bias-special-category
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-data-retention
- Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-data-subject-rights
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-deployment-checklist
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-dpia
- Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-model-privacy-audit
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-privacy-assessment
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-privacy-impact-template
- Provides combined DPIA and AI Act conformity assessment template with integrated risk scoring matrix. Covers GDPR Art. 35 DPIA elements, AI Act high-risk system requirements, mitigation measures, and human oversight assessment. Keywords: DPIA template, conformity assessment, risk scoring, AI Act, combined assessment, high-risk AI.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-training-data-class
- Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-training-lawfulness
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-transparency-reqs
- Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975ai-vendor-privacy-due
- Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975analytics-cookie-consent
- Managing consent for analytics cookies and implementing privacy-preserving measurement. Covers GA4 privacy configuration, consent mode fallback behavior, aggregate reporting alternatives, and cookieless measurement approaches.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975anonymization-alternative
- Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975apac-transfers
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975apec-cbpr-cert
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975applying-privacy-design-patterns
- 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.
- installs
- 1
- GitHub stars
- 315
- Updated
- Mar 16
mukul975art49-derogations
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975audit-evidence-collect
- Guides privacy audit evidence collection processes including evidence planning, sampling strategies, documentation standards, chain of custody, interview techniques, system walkthrough procedures, and evidence evaluation. Covers ISO 19011 evidence categories (records, statements of fact, observations) and ISACA audit evidence requirements for privacy compliance assessments. Keywords: audit evidence, evidence collection, sampling, chain of custody, audit documentation, interview techniques.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975audit-follow-up-verify
- 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.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975audit-remediation-program
- Guides audit findings remediation program management including finding prioritization by severity (critical, high, medium, low), owner assignment, remediation planning, deadline tracking, verification testing, closure criteria, escalation protocols, and management reporting. Covers remediation lifecycle from finding issuance to verified closure. Keywords: audit remediation, finding management, prioritization, verification testing, closure criteria, remediation tracking.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975audit-report-writing
- Guides privacy audit report writing including executive summary drafting, findings classification (critical, high, medium, low), evidence referencing, root cause analysis documentation, recommendation formulation, management response tracking, and report distribution protocols. Covers report structure from scope definition through appendices and sign-off. Keywords: audit report, findings documentation, executive summary, recommendations, report structure, privacy audit deliverables.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16
mukul975audit-risk-assessment
- Guides privacy audit risk assessment including risk universe development, inherent and residual risk scoring, control effectiveness evaluation, risk-based audit planning, heat map generation, risk appetite alignment, and audit prioritization by risk exposure. Covers the full audit risk assessment cycle from scoping through ongoing monitoring. Keywords: audit risk assessment, risk universe, inherent risk, residual risk, control effectiveness, risk-based audit planning.
- installs
- 1
- GitHub stars
- 295
- Updated
- Mar 16