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.
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Art. 35(7)(c) GDPR requires a DPIA to include "an assessment of the risks to the rights and freedoms of data subjects." This skill provides a quantifiable risk scoring framework that converts qualitative privacy risks into comparable, prioritised scores supporting mitigation decisions.
| Level | Score | Description | Examples |
|---|---|---|---|
| Negligible | 1 | Minor inconvenience, easily recoverable | Temporary inability to access non-essential service |
| Limited | 2 | Significant inconvenience, recoverable with effort | Targeted advertising based on inferred preferences |
| Significant | 3 | Serious consequences, difficult to recover from | Financial loss, discrimination, reputational harm |
| Maximum | 4 | Irreversible or very difficult to recover from | Identity theft, physical safety risk, loss of employment |
| Level | Score | Description | Indicators |
|---|---|---|---|
| Negligible | 1 | Unlikely given current controls | Strong technical controls, limited access, encrypted at rest and in transit |
| Limited | 2 | Possible but requires specific conditions | Some access controls, partial encryption, known but unproven attack vectors |
| Significant | 3 | Probable given known threat landscape | Weak controls in specific areas, prior incidents in sector, active threat actors |
| Maximum | 4 | Near-certain or already occurring | No controls, known vulnerabilities, prior breach of similar system |
Severity → Negligible(1) Limited(2) Significant(3) Maximum(4)
Likelihood ↓
Maximum(4) 4(M) 8(H) 12(VH) 16(VH)
Significant(3) 3(L) 6(M) 9(H) 12(VH)
Limited(2) 2(L) 4(M) 6(M) 8(H)
Negligible(1) 1(L) 2(L) 3(L) 4(M)
Risk Levels: L=Low(1-3), M=Medium(4-6), H=High(7-9), VH=Very High(10-16)
When residual risk remains High or Very High after all feasible mitigation measures, the controller must consult the supervisory authority under Art. 36(1) before commencing processing.
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概要と使いどころ
Implements 42 CFR Part 2 protections for substance use disorder patient records. Covers written consent requirements stricter than HIPAA, re-disclosure prohibition, court order procedures, qualified service organization agreements, and 2024 amendments aligning Part 2 with HIPAA. Keywords: 42 CFR Part 2, substance use disorder, SUD records, re-disclosure, consent, Part 2 amendments.
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 of third-country adequacy decisions under GDPR Article 45 for international data transfers. Covers the current EC adequacy decisions list, adequacy assessment criteria, partial adequacy handling, and monitoring of adequacy decision reviews. Keywords: adequacy decision, Article 45, third country, adequate protection, EC adequacy list.
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。
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.
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。
Preparing EU AI Act compliance documentation for high-risk AI systems. Covers Annex III classification, technical documentation under Art. 11, conformity assessment, risk management systems, and CE marking requirements. Keywords: EU AI Act, high-risk AI, Annex III, conformity assessment, CE marking.
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。