AI Governance, Compliance & Risk Management Legal Services

The Strategic Imperative for AI Governance

Artificial intelligence (AI) is no longer just an experimental tool; it is integrated into core business processes, decision-making, and customer interactions. Without a comprehensive AI governance framework, each new model or deployment can introduce undefined liability, regulatory risks, and reputational damage. We at Phillips Murrah help corporate boards, general counsel, and C-suite executives stay ahead in this rapidly changing environment. Our attorneys combine extensive experience in cybersecurity, data privacy, intellectual property, and complex corporate issues with practical knowledge of AI technologies. We collaborate with your leadership team to develop AI governance structures that meet legal requirements, uphold fiduciary duties, and align with your long-term business goals. Our expertise enables you to maintain a defensible legal posture against emerging AI litigation and regulatory actions.

Strategic Solutions for AI Risk and Accountability

Executives and in-house counsel increasingly ask the same question: “Who is liable when our AI gets it wrong?” That uncertainty itself creates a significant risk. Instead of treating AI Governance Law as just a compliance requirement, Phillips Murrah helps you turn AI governance into a strategic advantage. Our AI risk management attorneys craft customized AI governance frameworks that clarify accountability, align with emerging regulations, and provide robust documentation for regulators, investors, or customers who question how your company manages AI. 

By proactively identifying and reducing AI risks, we enable you to move faster and with greater confidence when deploying AI throughout your organization. Our strategic AI Governance and AI Risk Management services include:

• Establishing Corporate Accountability – Establishing corporate accountability in AI governance involves clearly identifying who is responsible for AI-related decisions, outcomes, and compliance within the organization. This generally includes board-level oversight, executive ownership (e.g., GC, CTO, CISO), and defined roles for key stakeholders such as legal, IT, compliance, and operations. A well-organized accountability model helps the company demonstrate that it exercised due care and met its fiduciary duties regarding AI, which is essential when regulators, investors, or courts inquire about who was “in charge” of these systems.
• Algorithmic Bias & Ethics Compliance – Algorithmic bias and ethics compliance focus on preventing AI systems from generating discriminatory, unfair, or unethical outcomes. This involves identifying high-risk applications (such as hiring, lending, pricing, and customer targeting), testing models for disparate impact or biased results, and applying corrective actions when bias is found. Effective bias and ethics measures help ensure AI systems comply with anti-discrimination laws, internal conduct codes, and emerging AI governance standards, thereby reducing legal risks and safeguarding the company’s reputation with customers, employees, and regulators.
•  Regulatory Compliance Frameworks – Regulatory compliance frameworks for AI translate a complex, evolving set of legal obligations into clear, operational rules for designing, deploying, and monitoring AI systems. This includes federal and state privacy laws, sector-specific regulations (such as in healthcare or financial services), and guidance from agencies and standards organizations. By integrating these requirements into policies, controls, and documentation practices, the company can demonstrate that its AI governance program is well-structured and defensible, which is essential during investigations, audits, or enforcement actions.
•  Data Governance for AI Models – Data governance for AI models includes how data is gathered, labeled, stored, accessed, and used during the AI lifecycle. Since AI systems are only as reliable as the data that trains and feeds them, companies need to manage data quality, trace data sources, and safeguard sensitive or regulated information like personal, health, or financial data. Good data governance minimizes the risk of privacy breaches, data leaks, and misuse of confidential information, while also boosting the reliability, auditability, and legal defensibility of AI results.
•  Third-Party AI Risk Management – Third-party AI risk management addresses issues arising when a company relies on external AI vendors, platforms, or tools to run its business operations. It generally includes vendor due diligence (covering technical, security, and legal aspects), contract clauses that assign responsibility and indemnity, and continuous oversight of vendor performance and compliance. Since regulators and courts often hold the deploying company responsible regardless of who created the model, solid third-party risk management ensures that external AI providers meet the company’s governance, security, and compliance standards, rather than becoming an unseen source of liability.

AI Governance FAQs

Who is legally responsible when a company’s deployed AI system causes a costly error or violates a regulation? In most cases, the legal responsibility rests with the company that deploys or relies on the AI system, not the technology itself. Courts, regulators, and counterparties will target the entity that made the decision to use AI in a specific way. This means directors, officers, and General Counsel must ensure that AI-driven processes comply with applicable laws and that oversight mechanisms are in place. Phillips Murrah helps clients develop governance structures, contracts, and internal controls to clearly assign responsibility and demonstrate that the company exercised proper care when deploying AI.

How does establishing a legal governance framework enable innovation rather than restricting or slowing it down? A well-crafted AI governance framework sets clear “rules of engagement” for innovation. When engineers, data scientists, and product teams understand the legal boundaries—what data can be used, when human review is needed, and how to document decisions—they can develop and implement AI solutions more efficiently and with fewer internal obstacles. Instead of reacting to issues after they occur, your organization operates within pre-approved guardrails that minimize rework, delays, and regulatory surprises. Phillips Murrah designs AI governance frameworks that are legally sound yet easy for business teams to follow, so your teams can focus on creating value while maintaining a defensible compliance stance.

What is the very first step our legal department should take to establish an effective AI governance program? The first step is to conduct an AI Risk Audit. Before developing policies or forming committees, your organization needs a clear inventory of where AI is currently used (or planned), what data it depends on, which decisions it influences, and what legal, ethical, and operational risks those uses present. Phillips Murrah’s AI risk management attorneys lead legal-focused AI Risk Audits that (1) map existing and upcoming AI systems and vendors; (2) identify high-risk applications and potential regulatory touchpoints; and (3) recommend prioritized actions for governance, policy, and oversight. This initial AI Risk Audit creates the foundation for your AI governance framework, providing a roadmap for policy development, board and executive oversight, training, and ongoing monitoring. From there, Phillips Murrah becomes your strategic partner in AI Governance Law—advising your leadership whenever new AI initiatives, questions, or risks arise.