### 3.1 Quality Grade The **A (Excellent)** grade reflects a dataset with exceptional structural integrity: all 15,000 job postings in `ai_job_dataset` contain complete records across every field, with zero missing values in critical dimensions like salary, experience level, employment type, and required skills. This 100% completeness score means business leaders can confidently analyze salary drivers and remote-work patterns without concern for gaps that might skew regional comparisons or skill-premium calculations. As a single-table dataset, referential integrity constraints are structurally inapplicable, but the absence of null values across all columns provides the clean foundation necessary for both executive reporting and machine-learning applications targeting compensation benchmarking or talent-market forecasting. ### 3.2 Key Risk Areas | Risk Type | Detail | Severity | |-----------|--------|----------| | PII/Sensitive | ai_job_dataset.salary_currency (salary) | High | | PII/Sensitive | ai_job_dataset.salary_usd (salary) | High | The flagged salary fields—`salary_currency` and `salary_usd`—represent the dataset's only material risk, classified as high-severity due to their potential to reveal compensation details that may be subject to privacy regulations or competitive-intelligence concerns. While aggregated salary analysis (median AI engineer compensation by region, skill-based pay premiums) poses minimal risk, any reporting that could re-identify individual job postings or employers through salary combinations with location and company size must implement access controls and anonymization protocols before distribution to broader stakeholder groups or external partners. ### 3.3 Remediation Priorities - **Implement role-based access controls for `ai_job_dataset.salary_usd` and `salary_currency`**: Restrict raw salary data to compensation analysts and senior leadership; provide downstream teams with pre-aggregated salary bands (e.g., quartiles by experience level and region) to support skills-premium analysis without exposing individual posting details. - **Establish aggregation thresholds for public-facing reports**: Require minimum cell sizes (≥10 postings) when publishing salary statistics segmented by company location, size, or skill combinations to prevent re-identification of specific employers or roles in niche AI specializations. - **Document data lineage and refresh cadence**: Clarify whether the 15,000 postings represent a point-in-time snapshot or rolling window, and establish update frequency expectations so remote-work trend analysis and salary benchmarks reflect current market conditions rather than stale 2025 data. - **Create derived privacy-safe metrics**: Generate calculated fields such as "salary percentile within region" or "premium vs. market median" that preserve analytical value for identifying high-demand skills while eliminating direct salary exposure in standard business intelligence dashboards.