Artificial intelligence is profoundly transforming how enterprises operate, analyze data, and make decisions. For financial executives tasked with driving innovation while controlling risk, AI presents both opportunities and challenges. Although the potential return on investment is clear, legal and regulatory risks are often less visible yet equally significant. The following seven legal considerations are designed to help financial leaders understand and mitigate potential risks when supporting or overseeing AI projects.

1. Data Privacy and Protection Are Critical

AI systems typically rely on vast amounts of data, which may include sensitive personal information, financial data, and business information. Compliance with data privacy laws is paramount, as the EU General Data Protection Regulation, the California Consumer Privacy Act, and other emerging state laws impose strict requirements on the collection, processing, storage, and sharing of personal data.

Enterprises should ensure that:

  • Data used for AI is collected and processed on a lawful basis, with appropriate consent obtained when necessary;
  • Data minimization principles are followed, using only the data necessary to achieve the purpose of the AI application;
  • Robust data security measures are established to prevent unauthorized access, disclosure, or misuse;
  • Data subject rights (such as the right to access, correct, or delete personal data) are respected and implemented in accordance with jurisdictional requirements.

Regardless of industry, violations of data privacy laws can lead to substantial fines, litigation, and reputational damage.

2. Address Bias, Fairness, and Discrimination Issues

AI systems may inadvertently perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes. This risk exists across industries, from recruitment and promotion to customer interactions and product recommendations.

To mitigate these risks, enterprises should:

  • Establish human oversight processes for high-impact AI decisions;
  • Regularly audit AI models to identify and address potential biases;
  • Use diverse and representative datasets for training and validation;
  • Implement fairness metrics and testing protocols to ensure equitable treatment of all individuals and groups;
  • Continuously monitor evolving legal standards related to discrimination and fairness, such as anti-discrimination laws and regulatory guidance.

3. Regulatory Environment and Compliance Uncertainty

The legal framework surrounding AI is evolving rapidly. In the United States, multiple federal agencies, including the Federal Trade Commission and the Equal Employment Opportunity Commission, have indicated they will apply existing laws to AI applications. State-specific AI laws in California and Utah have taken effect in the past year. Additionally, Colorado, Illinois, and potentially other states will implement AI-focused laws in 2026. Globally, the EU Artificial Intelligence Act will become fully applicable to most organizations in August 2026, establishing a tiered risk framework with extraterritorial effect.

Enterprises should:

  • Monitor emerging laws and regulatory guidance in key jurisdictions;
  • Designate cross-functional teams responsible for AI governance and compliance;
  • Document the purpose, risk classification, and safeguards for each AI use case;
  • Prepare for future disclosure obligations, such as impact assessments or risk ratings.

Proactive compliance management reduces enforcement risk and supports sustainable AI adoption across industries.

4. Intellectual Property and Licensing Strategies

AI projects raise unique issues related to data ownership and intellectual property rights in AI-generated works. Enterprises should ensure that their AI investments translate into sustainable competitive advantages rather than legal vulnerabilities.

Risk mitigation strategies include:

  • Obtaining licenses for all third-party datasets or pre-trained models;
  • Clearly defining intellectual property ownership in all agreements with employees, contractors, and suppliers;
  • Exploring trade secret, copyright, or patent protection for key AI assets;
  • Maintaining internal records of model development, versions, and authorship.

A robust IP strategy helps protect enterprise assets and minimizes the risk of costly disputes.

5. Contractual Risk Allocation

AI projects often involve collaboration with vendors, consultants, and technology partners. Well-drafted contracts are essential for allocating risk, defining responsibilities, and establishing clear expectations.

Enterprises should ensure contracts:

  • Clearly define the scope of work, deliverables, and performance standards, including service level agreements;
  • Address ownership, usage rights, and confidentiality obligations for data and outputs;
  • Allocate liability for errors, data breaches, or regulatory violations arising from AI use;
  • Include provisions for ongoing support, maintenance, and updates of AI systems;
  • Include representations and warranties regarding training data, performance, and compliance;
  • Include audit rights, performance standards, and termination triggers.

6. Transparency, Oversight, and Risk Management

AI is increasingly scrutinized by stakeholders, including investors, regulators, customers, and the public, who expect responsible and ethical use. For enterprises, this means ensuring AI risks are integrated into broader enterprise risk management practices. Key risks include misuse of AI tools due to lack of access controls or training, inability to explain or justify AI system decisions, and unmonitored model drift leading to inaccurate or unpredictable outputs.

Enterprises should:

  • Adopt an AI governance framework incorporating legal, risk, and technical perspectives;
  • Require model explainability for decision-making tools affecting individuals or operations;
  • Continuously track and audit AI system behavior, retraining when necessary;
  • Implement access controls, role-based responsibilities, and ethical use training.

7. Cybersecurity and Incident Response

AI systems may introduce new cybersecurity vulnerabilities, including risks related to data integrity, model manipulation, and adversarial attacks. Enterprises must prioritize cybersecurity to protect AI assets and maintain trust.

Best practices include:

  • Integrating AI systems into the enterprise's broader cybersecurity framework;
  • Conducting regular security assessments and penetration testing of AI applications;
  • Developing and testing incident response plans for AI-related threats;
  • Training employees on AI security risks and best practices.

A strong cybersecurity posture is essential for protecting sensitive data and maintaining regulatory compliance in any industry.

AI offers transformative potential for organizations across industries, but its implementation is fraught with legal complexities and risks. By proactively addressing data privacy, transparency, bias, regulatory compliance, intellectual property, contractual risks, risk management, and cybersecurity, financial professionals can mitigate legal risks and unlock the full value of AI. Approaching AI implementation strategically and with risk awareness not only protects the organization but also lays the foundation for its long-term success.