By proactively addressing data privacy, intellectual property and cybersecurity, finance professionals can mitigate legal exposure and unlock the full value of AI.
Artificial intelligence is revolutionizing how organizations operate, analyze data and make decisions. For finance executives tasked with driving innovation and controlling risk, AI presents both an opportunity and a challenge. While the potential return on investment is clear, the legal and regulatory risks are often less visible but equally significant. Here are seven key legal considerations to help finance leaders understand and mitigate risk when supporting or overseeing AI initiatives.
1. Data privacy and protection are paramount
AI systems often rely on large volumes of data, including sensitive personal, financial and business information. Compliance with data privacy laws is critical, as regulations such as the European Union's 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.
Organizations should ensure that:
- Data used for AI is collected and processed lawfully, with appropriate consent where required.
- Data minimization principles are followed, using only the data necessary for the intended AI application.
- Robust data security measures are in place to prevent unauthorized access, breaches or misuse.
- Depending on jurisdiction, data subject rights — such as the right to access, correct or delete personal data — are respected and operationalized.
Non-compliance with data privacy laws can result in significant fines, litigation and reputational harm, regardless of industry.
2. Consider bias, fairness and discrimination
AI systems can inadvertently perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes. This risk is present in any sector, from hiring and promotions to customer engagement and product recommendations.
To mitigate these risks, organizations should:
- Establish procedures for human oversight of high-impact AI decisions.
- Conduct regular audits of 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.
- Stay informed about evolving legal standards related to discrimination and fairness, such as anti-discrimination laws and guidance from regulatory agencies.
3. Regulatory landscape and compliance uncertainty
The legal framework surrounding AI is evolving rapidly. In the U.S., multiple federal agencies, including the Federal Trade Commission and Equal Employment Opportunity Commission, have signaled they will apply existing laws to AI use cases. AI-specific state laws, including in California and Utah, have taken effect in the last year. Further, Colorado, Illinois and potentially other states will have AI-focused laws going in effect in 2026. Globally, the EU AI Act, which will be fully applicable for most organizations in August 2026, will impose a tiered risk-based framework for AI systems with extraterritorial reach.
Organizations should:
- Monitor emerging laws and regulatory guidance across key jurisdictions.
- Designate a cross-functional team to oversee AI governance and compliance.
- Document the purpose, risk classification and safeguards for each AI use case.
- Plan for future disclosure obligations (e.g., impact assessments or risk ratings).
Proactive compliance management reduces the risk of enforcement actions and supports sustainable AI adoption across all sectors.
4. Intellectual property and licensing strategies
AI projects involve unique intellectual property questions related to data ownership and IP rights in AI-generated works. Organizations should ensure that their investments in AI translate into sustainable competitive advantages, not legal vulnerabilities.
Risk mitigation strategies include:
- Secure licenses for all third-party datasets or pre-trained models.
- Address IP ownership in all agreements with employees, contractors and vendors.
- Explore trade secrets, copyright or patent protections for key AI assets.
- Maintain internal records of model development, versions and authorship.
A robust IP strategy helps safeguard organizational 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 to allocate risk, define responsibilities and establish clear expectations.
Organizations should ensure that contracts:
- Clearly define the scope of work, deliverables and performance standards (including service level agreements) for AI solutions.
- Address data and output ownership, usage rights and confidentiality obligations.
- Allocate liability for errors, data breaches or regulatory violations arising from AI use.
- Include provisions for ongoing support, maintenance and updates to AI systems.
- Contain representations and warranties on 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 — investors, regulators, customers and the public — who expect responsible and ethical use. For organizations, this means ensuring that AI risk is incorporated into broader enterprise risk management practices. Key risks include misuse of AI tools due to a lack of access controls or training, the inability to explain or justify decisions made by AI systems and unchecked model drift leading to inaccurate or unpredictable outputs.
Organizations should:
- Adopt an AI governance framework that includes legal, risk and technical input.
- Require model explainability for decision-making tools that affect individuals or operations.
- Track and audit AI system behavior over time, with retraining as needed.
- Implement access controls, role-based responsibilities and training on ethical use.
7. Cybersecurity and incident response
AI systems can introduce new cybersecurity vulnerabilities, including risks related to data integrity, model manipulation and adversarial attacks. Organizations must prioritize cybersecurity to protect AI assets and maintain trust.
Best practices include:
- Integrating AI systems into the organization's broader cybersecurity framework.
- Conducting regular security assessments and penetration testing of AI applications.
- Developing and testing incident response plans specific to AI-related threats.
- Training staff on AI security risks and best practices.
A strong cybersecurity posture is essential to safeguard sensitive data and maintain regulatory compliance in any industry.
AI offers transformative potential for organizations across all sectors, but its implementation is fraught with legal complexities and risks. By proactively addressing data privacy, transparency, bias, regulatory compliance, intellectual property, contractual risk, risk management and cybersecurity, finance professionals can mitigate legal exposure and unlock the full value of AI. A strategic, risk-aware approach to AI implementation not only protects the organization but also positions it for long-term success.
Originally published by CFO.com
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
