Artificial intelligence is no longer an experiment for U.S. companies – it is now operational. However, as AI becomes embedded in business models, regulators are paying attention. Compliance is now a board-level responsibility, whether it’s California’s CCPA/CPRA and HIPAA (for health care companies), SEC disclosures on cyber risk, or NIST’s AI Risk Management Framework (RMF) that establishes best practices for organizations across industries, compliance demands a board level call-out. Meanwhile, global regulation such as the EU AI Act and India’s DPDP Act is relevant to any U.S. company engaged in cross border operations or data flows.
The risks are great. One misstep, mishandling personal data, failing to disclose AI data risks, or violating sector-related security requirements, can expose a company to millions of dollars in fines, shareholder lawsuits, and reputational erosion. For CXOs, the challenge is not simply designing and implementing AI applications, they must embed compliance, trust, and accountability throughout the lifecycle of the AI.
This framework seeks to identify pragmatic steps for a CFO, CIO, CISO, and compliance executives to ensure their AI investment is protected while fulfilling regulatory expectations in the U.S. and beyond.
1
Identify the Types of Regulated Data
The first consideration is clarity. Organizations need to identify and map the types of data that AI systems utilize: personal identifiable information (PII) or protected health information (PHI), financial records, or sensitive corporate data. Organizations can delineate types of regulated data so they can establish the applicable laws, ascertain potential risks, and implement appropriate controls. Setting explicit inventory boundaries also helps prevent “shadow AI” use cases from appearing in production unnoticed.
2
Establish Lawful Basis & Consent Models
All AI applications will need to establish a lawful basis to police the use of regulated data. With GDPR lawful bases such as explicit consent, contractual necessity, or legitimate interest, with India’s DPDP Act, consent must be free, informed, and revocable. U.S. state-level legislation also requires transparency and opt out models in the CCPA and CPRA legislation. CFO and compliance officers need to ensure consent models are easy to manage for users, clear and transparent for users, and auditable. Establishing a clearly laid out lawful basis not only mitigates regulatory risk, it builds customer confidence.
3
Implement Data Minimization & Retention Policies
AI is a data creature but compliance is a discipline. Organizations simply need to minimize collection to what is necessary to meet the use case, and document and support retention schedules to avoid indefinite storage. Automated deletion workflows, anonymization workflows and purging, should be included in these processes. Minimizing data reduces organization exposure to risk, and demonstrates to regulators compliance with principles outlined in legislation such as GDPR, DPDP, and the EU AI Act.
4
Develop Privacy by Design & DPIAs
Compliance cannot be an afterthought. Privacy by design includes data protection in the underlying architecture of AI systems at the start. Conducting Data Protection Impact Assessments (DPIAs) requires identifying risks and putting in place the mitigations, before going live. The EU AI Act experts have clarified that high risk AI systems must be subject to conformity assessments, making DPIAs and documenting risk critically important. This reduces compliance to a checklist approach and moves from reactive to proactive and improved compliance as a guardrail.
5
Leverage Access Controls & Least Privilege
Sensitive AI applications must strictly adhere to least privilege: users and systems should only have access to data for their role, and nothing else. Role based access control (RBAC), multi factor authentication and monitoring are all preventative controls to reduce insider risk and maximize compliance. Where access can be audited, clear records mean access can be reviewed and justified during audits or certifications.
6
Compliance With Security Regulations
Many industries operate within strict security regulations such as HIPAA for healthcare or PCI DSS for payments. AI applications must abide not only by these frameworks, but also implement encryption, secure APIs and perform vulnerability scanning and penetration testing. For instance, NIST’s AI RMF is seen as a lifecycle process and encourages the mapping of threats throughout the lifecycle, and some sort of triaging of security failures as compliance failures instead of just technical ones.
7
Governance of Vendors and Cross-Border Data Transfers
Enterprises seldom develop AI systems or projects in isolation of third-party vendors, and it is the third party vendors, along with the cloud provider, who are the dominant aspects of the ecosystem. Vendor risk assessments, contractual protections, and due diligence, with one or more of them dependent upon the size of the vendor, are critical steps. When enterprises transfer data across borders, ensuring compliance with, for example, GDPR’s Standard Contractual Clauses (SCCs), and DPDP’s transfer rules in India, or even the nature of protective transfer rules in the U.S.-EU Data Privacy Framework is also part of the governance setting. When considering vendors, CFOs should put governance into the financial model as well as a compliance component to their decisions.
8
Orchestrating User Rights
New regulatory environments are creating expectations for consumer/user rights such as access, rectification, erasure and portability of their information. AI applications must give users the ability to exercise those rights at scale (using automated workflows). Compliance leaders should ensure fulfilling user requests do not hurt business processes while their customers enjoy transparency and control over their own information. Also, the EU AI Act is looking to expand this audience facing obligation, which makes these capabilities best left to a decision or strategy.
9
Logger, Auditability and Traceability
All AI systems should be able to log for data, trace usage, model decisions and user interactions. This provides accountability and supports auditing both regulatory and internal governance. Explainability tools and tracing dashboards can also help organizations demonstrate compliance when challenged by a regulator, partner, or potential customer, while showing that they are practicing ethical AI.
10
Develop Incident Response & Breach Notification Policies
All systems, no matter how secure, can face some risk. Having a strong incident response plan means that whenever a breach does occur, the organization is able to contain it quickly, assess it before communicating with anyone affected about what happened. GDPR laws mandate an organization notify you of a data breach that has put your data at risk within 72-hours, similar provisions exist in DPDP. Organizations should also conduct exercises using their response playbook of processes and assigned roles, and have after action meeting plans, and have cyber vendor incident roles pre established. If an incident occurs, rapid and transparent response limits exposure and reduces trust erosion, from customers or other partners.
Final Thought:
Ensuring compliance with different regulations regarding the use of AI applications is no longer a choice, rather it is board level responsibility. By methodically addressing data scope, consent, minimization, privacy, access, security, vendor management, user rights, auditability, and incident response, companies can develop innovative but nevertheless supported AI systems. With new laws such as EU AI Act, the DPDP Act, and the NIST RMF influencing global expectations, being compliant before the laws are enacted is the best way to derive business value from AI, while mitigating legal, financial, and reputational risk.
