Navigating AI Risk: Legal Exposure, Governance, and Emerging Trends

The accelerated adoption of artificial intelligence (AI) across business functions is reshaping corporate risk profiles and expanding legal exposure. While AI presents opportunities for efficiency and innovation, its deployment raises material concerns under existing employment, privacy, consumer protection, and professional responsibility frameworks.

Key Legal Risks

One of the most significant risks associated with AI systems is algorithmic discrimination, particularly in employment and workforce management. AI tools used for recruiting, screening, and performance evaluation may give rise to disparate impact claims under Title VII of the Civil Rights Act of 1964, the Americans with Disabilities Act (ADA), and the Age Discrimination in Employment Act (ADEA) where outputs reflect biased training data or insufficient human oversight. These risks are increasingly being tested in U.S. courts, including in Mobley v. Workday, Inc. (N.D. Cal. 2023), which challenges the use of AI-driven hiring tools under federal anti-discrimination laws.

A second area of exposure arises from data privacy and biometric processing. AI systems often rely on large datasets that may include personal or sensitive information, triggering obligations under statutes such as the California Consumer Privacy Act (CCPA/CPRA) and state biometric privacy laws. In parallel, regulators have emphasized that opaque AI practices may constitute unfair or deceptive conduct under Section 5 of the Federal Trade Commission Act.

Finally, the increasing use of generative AI has introduced risks tied to accuracy, reliability, and professional accountability. U.S. courts have sanctioned attorneys for filing submissions containing fabricated or “hallucinated” legal citations, underscoring that reliance on AI does not excuse failures to meet duties of competence and candor.

Governance and Risk Mitigation

In response, companies are moving toward structured AI governance models rather than ad hoc adoption. Leading practices include the establishment of cross-functional AI governance committees, formal AI use policies, and documented risk assessments aligned with the NIST AI Risk Management Framework. Embedding human review at critical decision points remains essential, particularly where AI outputs may affect individuals’ rights or legal interests.

From a legal perspective, organizations should treat AI as an extension of existing compliance obligations—not a separate or unregulated tool. Internal controls, auditability, and transparency are increasingly viewed by regulators as baseline expectations.

Emerging Regulatory Trends

At the U.S. state level, AI-specific regulation is beginning to crystallize. The Colorado Artificial Intelligence Act(C.R.S. § 6-1-1701 et seq.), effective in 2026, will require developers and deployers of “high-risk” AI systems to implement risk management programs and conduct impact assessments. In parallel, enforcement activity by agencies such as the Equal Employment Opportunity Commission continues to signal heightened scrutiny of AI-enabled employment decisions.

Latin America & Caribbean Perspective

Across Latin America and the Caribbean, AI regulation is developing primarily through data protection and constitutional rights frameworks rather than AI-specific statutes. Brazil’s Lei Geral de Proteção de Dados (LGPD)expressly grants individuals the right to request review of automated decisions that affect their interests, creating early precedent for algorithmic accountability. Similar principles are emerging in proposed reforms and regulatory guidance in jurisdictions such as Mexico, Chile, and Colombia, reflecting a regional emphasis on transparency, explainability, and human intervention.

For multinational companies operating across the region, this convergence of privacy law, labor protections, and constitutional norms underscores the importance of harmonized AI governance strategies that can withstand scrutiny across multiple legal systems.