AI Governance for Business: 5 Key Considerations

AI governance is the set of policies, processes, and accountability structures a business uses to manage the legal, ethical, and operational risks that come with using artificial intelligence, including data privacy, discrimination, and regulatory compliance.

1. AI Governance Is a Business Issue, Not Just a Technical One

Paradigmatically – leaders must first understand that AI governance is not an issue for the IT department or a dev team. Instead, AI governance is the domain of business and its operation. AI systems can impact employment decisions, customer interactions, intellectual property rights, privacy obligations, cybersecurity requirements, contractual commitments, and regulatory compliance. As a result, legal departments, compliance teams, risk managers, human resources professionals, procurement leaders, and executive leadership all have a role to play.

Questions companies should consider include:

• Who owns AI governance within the organization?
• What approvals are required before deploying an AI system?
• How are AI-related risks escalated to management or the board?
• Who is responsible if an AI system produces harmful, discriminatory, or inaccurate outcomes?

Without defined accountability structures, organizations may find themselves exposed to significant legal and operational risks.

An experienced AI governance attorney can help organizations establish governance frameworks that align with regulatory requirements, industry standards, and corporate risk tolerance.

2. Know Where AI Is Being Used Across Your Organization

The foundation of effective AI governance is visibility. Before an organization can manage AI risks, it must understand where AI is being used and how it affects business operations.

An incomplete index of AI usage is undocumented risk; in other words, you cannot govern what you cannot see. AI is increasingly embedded, and automatically enabled, in software tools across the tech stack. Business units may purchase AI-enabled software without legal review. Vendors may incorporate AI functionality into existing products with little visibility into how data is processed or how decisions are generated. Existing products that didn’t have AI might suddenly have AI capabilities (Slack is an easy example).

As a result, more AI products or features may be used than is known to a business. Worse yet, shadow AI usage – that is employees using unsanctioned AI – has been rising dramatically. Some businesses face much less risk than others depending on the market, the business unit or department using the technology, and the nature of data moving through the tool. Nonetheless, answering the question of risk necessitates a complete inventory of the tools with AI features, whether they are enabled, and how data moves between

Companies can, and should, perform AI audits to build a snapshot measure of AI usage and its risks. An AI audit can help identify these systems, assess risk exposure, and establish appropriate controls. Organizations that fail to maintain visibility into their AI ecosystem may struggle to comply with emerging regulations, respond to regulatory inquiries, or manage litigation risks.

3. Data Governance Is the Foundation of AI Governance

AI tools are only as good as the data used to train the model, along with the supplemental data (i.e. context) that the model has access to. Unfortunately, digital hygiene and data governance are already difficult challenges for individual persons and corporations alike. Nonetheless, poor-quality data can lead to inaccurate outputs, flawed business decisions, and discriminatory outcomes. In addition, the use of personal information, confidential business data, or proprietary content may create privacy, intellectual property, and cybersecurity concerns. For this reason, AI governance and data governance are closely intertwined.

Organizations should evaluate several key questions:

• Where does the data originate?
• Is the data accurate and reliable?
• Does the organization have the right to use the data?
• Are privacy obligations being satisfied?
• Can decisions and outputs be traced back to their underlying data sources?
• Are retention and deletion policies being followed?

Data governance controls should address data quality, access management, data lineage, security protections, and compliance obligations. These considerations become especially important when organizations deploy generative AI systems. Employees may inadvertently upload confidential information into third-party platforms, creating potential legal and contractual risks. Similarly, organizations may rely on AI-generated outputs without understanding the limitations or biases embedded within the underlying training data.

Vendors may be producing liability for the business that is making use of the vendor. For instance, under U.S. employment discrimination law, employers generally remain responsible for their hiring processes, even when they use third-party tools. Courts and regulators (including the EEOC) have repeatedly taken the position that an employer cannot avoid anti-discrimination obligations simply by outsourcing screening to software. If an AI tool disproportionately excludes protected groups, the employer may still face liability. In the now-unfolding litigation in Mobley v. Workday, Workday’s applicant screening tool allegedly engaged in several forms of hiring discrimination. This framework raises the possibility that both the entity deploying the AI software and, in some circumstances, the developer of the software may be held liable for discriminatory outcomes produced by the algorithm.

A comprehensive AI compliance program should include policies governing data usage, data security, privacy protections, and vendor management to understand exposure risks.

Legal counsel can assist organizations in evaluating whether current data governance practices adequately support AI deployment and regulatory compliance obligations.

4. Don't Overlook State AI Laws and Regulations

State law itself varies. For instance, some states have reporting requirements such that users intaking with an AI companion must provide clear, conspicuous notice that the user is not interacting with a human. In Connecticut, a newly passed bill requires provenance watermarks in AI generated audio, images, or video. That may affect how some marketing departments produce digital assets. At Russell, we track each state’s current and upcoming regulations to keep an up-to-date map of legal and operational requirements for companies across the United States. Understanding where these laws are headed allows us to better advise clients, identify emerging litigation trends, and stay at the forefront of AI accountability and governance.

5. AI Governance Is an Ongoing Process, Not a One-Time Project

Many organizations approach AI governance as a compliance exercise: create a policy, approve a few tools, and move on. In practice, however, AI governance is an ongoing operational function that must evolve alongside the technology itself.

AI systems change rapidly. Vendors continuously release new features, models receive updates, regulations develop, and employee use cases expand. A governance framework that is effective today may be inadequate six months from now. Organizations that fail to revisit their AI controls may find themselves exposed to risks that did not exist when their policies were originally drafted.

Businesses should establish mechanisms for continuous oversight, including:

• Regular reviews of AI systems and use cases.
• Periodic AI risk assessments and audits.
• Ongoing employee training regarding acceptable AI use.
• Monitoring changes in applicable laws and regulatory guidance.
• Reviewing vendor AI practices and contractual protections.
• Testing governance controls to ensure they remain effective.

AI governance should be treated similarly to cybersecurity, privacy, or compliance programs: as a continuous process of assessment, monitoring, and improvement rather than a one-time implementation.

Organizations that build governance into their normal business operations are often better positioned to adapt to regulatory changes, manage emerging risks, and take advantage of new AI opportunities with confidence.

AI Governance Is a Business Necessity, Not Just a Compliance Checkbox

Artificial intelligence presents significant opportunities for organizations to improve efficiency, enhance decision-making, and create new products and services. At the same time, it introduces legal, operational, reputational, and regulatory risks that cannot be managed through technology alone.

Effective AI governance begins with recognizing that AI is a business issue requiring cross-functional oversight. Organizations must understand where AI is being used, ensure their data governance practices are fit for purpose, remain attentive to evolving state and federal requirements, and establish processes for ongoing monitoring and accountability.

Companies that proactively develop AI governance programs today will be better prepared to navigate emerging regulations, respond to stakeholder expectations, and deploy AI responsibly. As AI adoption accelerates, governance is increasingly becoming not just a compliance requirement, but a business necessity.

Need Help Navigating AI Governance?

AI governance isn't something to figure out alone, and the risks of getting it wrong (regulatory exposure, discrimination claims, data mishandling) are significant. At Russell, we work with law firms and businesses to build practical, defensible AI governance frameworks tailored to their industry and risk profile. If you're not sure where your organization stands, contact us for a consultation.