AI Risk Assessment — Legal & Regulatory Exposure Analysis

Why AI risk assessment matters now

AI systems increasingly make or influence decisions with significant legal consequences: who gets hired, who receives credit, who is investigated, and who qualifies for services. Each of these decisions creates potential liability under multiple overlapping regulatory frameworks.

In New York alone, Local Law 144 mandates independent bias audits of automated employment decision tools. The EU AI Act classifies many of these same systems as high-risk under Annex III. GDPR Article 22 gives individuals the right to contest automated decisions. And US employment, fair lending, and anti-discrimination laws apply regardless of whether a human or algorithm made the decision.

A comprehensive AI risk assessment maps these overlapping obligations and identifies where your organization is exposed — before a regulator, plaintiff's attorney, or auditor does.

What we assess

• Bias and fairness risk — Statistical analysis of AI system outputs for disparate impact across protected categories. Assessment of training data composition and validation methodology.
• Privacy and data protection exposure — GDPR Article 22 compliance for automated decision-making, Data Protection Impact Assessments (DPIAs), and data minimization analysis.
• Employment law risk — Compliance of automated hiring, performance evaluation, and workforce management tools with NYC Local Law 144, Title VII, ADA, and EU employment directives.
• Regulatory classification — Multi-framework risk classification across EU AI Act Annex III, NIST AI RMF, and ISO/IEC 42001 to identify which obligations apply to each system.
• Transparency and explainability gaps — Assessment of whether AI system outputs can be adequately explained to affected individuals, regulators, and courts.
• Third-party and vendor risk — Evaluation of AI components procured from third parties, including liability allocation, documentation requirements, and supply chain compliance.

Engagement format

AI risk assessments are scoped based on the number and complexity of AI systems under review. Typical engagements range from 3 to 6 weeks. Deliverables include a comprehensive risk register, regulatory exposure matrix, and prioritized mitigation plan. Initial consultations are complimentary and confidential.