Is Your Legal Function Ready for AI at Scale, or Exposed by the Absence of Governance?

What happens when artificial intelligence is embedded into legal workflows faster than governance frameworks can mature, particularly in a profession where errors directly translate into regulatory exposure, privilege erosion, and judicial sanctions?

According to the 2024 Thomson Reuters state of the legal market report, more than 70% of large law firms and corporate legal departments globally are already piloting or deploying generative AI, while fewer than one-third have established formal, enterprise-wide governance structures that define accountability, acceptable use, and risk ownership. This growing disconnect between adoption velocity and institutional control is emerging as one of the most material operational risks facing the legal sector.

For law firms and in-house legal teams, AI governance is a practical necessity. It is an operational imperative that sits at the intersection of professional responsibility, regulatory compliance, data protection, and reputational stewardship. An AI Governance Charter is the mechanism through which legal organisations convert abstract principles into enforceable, auditable, and defensible operating rules.

Why AI Governance in Legal Functions Requires a Higher Bar

Legal functions operate within a uniquely constrained risk environment compared to other enterprise domains adopting AI. Attorney-client privilege, statutory confidentiality obligations, evidentiary standards, and court-mandated disclosure requirements significantly amplify the consequences of model error, data leakage, or unverified outputs. Unlike adjacent corporate functions, legal teams cannot rely on probabilistic reasoning without robust validation and documented professional oversight.

This distinction has been reinforced by enforcement and judicial action. During 2023 and 2024, multiple US federal courts sanctioned attorneys for submitting filings containing fabricated case law generated by large language models, prompting formal judicial scrutiny of AI use in legal practice. In response, several Am Law 100 firms, including Baker McKenzie, Latham & Watkins, and Jones Day, issued internal directives restricting the use of public generative AI tools for client work and mandating senior-level review for AI-assisted outputs.

At the same time, global enterprises such as JPMorgan Chase, Siemens, and Unilever have publicly confirmed the controlled deployment of AI within their legal departments for contract analytics, discovery prioritisation, and regulatory monitoring. The signal is clear. AI adoption is accelerating, but only where governance frameworks provide confidence, traceability, and accountability.

The Strategic Role of an AI Governance Charter

An AI Governance Charter is not an ethics statement, nor is it a technology policy owned solely by IT. Its function is to establish a legally enforceable control framework that defines who may deploy AI, for which legal activities, under what constraints, and with which escalation and accountability mechanisms.

For legal organisations, the charter must explicitly address professional conduct obligations, data sovereignty and confidentiality, model risk management, regulatory alignment, and auditability. These elements must be articulated at the level of the operating process rather than aspirational values, ensuring that governance is embedded into daily legal work rather than applied retrospectively.

Enterprise standards such as Microsoft’s Responsible AI Standard and OpenAI’s enterprise governance guidance provide structural reference points, while regulatory instruments, including GDPR, the EU AI Act, and advice from the American Bar Association and the UK Solicitors Regulation Authority, define the external compliance perimeter within which legal AI must operate.

Core Pillars of a Legal AI Governance Charter

A credible AI Governance Charter for legal functions must move decisively beyond high-level principles and translate governance intent into concrete, enforceable controls. In practice, this requires a tightly structured framework that aligns professional responsibility, regulatory compliance, technology risk management, and operational accountability into a single, defensible system.

Governance, ownership, and decision accountability

Effective AI governance begins with explicit ownership at the highest level of the legal organisation. Leading firms and corporate legal departments are establishing formal AI governance committees chaired by the Managing Partner or General Counsel, with representation from compliance, information security, data protection, and technology leadership. Salesforce’s legal department has publicly described its AI review board model, which centralises decision-making authority and ensures that legal leadership remains accountable for AI-enabled outcomes.

Permitted use cases and controlled prohibitions

Mature governance charters establish risk-tiered classifications of AI use cases. Lower-risk applications such as document classification, clause extraction, and discovery prioritisation are governed differently from higher-risk activities involving legal reasoning, drafting pleadings, or regulatory interpretation. Goldman Sachs has publicly stated that AI deployments within its legal and compliance functions are limited to augmentation and efficiency use cases, with explicit prohibitions on unsupervised legal advice generation.

Data governance and protection of privilege

Data handling is the most critical pillar of legal AI governance. Charters must define precisely which data may be used for prompting, training, or fine-tuning models and explicitly prohibit the use of client-confidential or privileged information in consumer-grade or externally hosted systems. Firms such as Clifford Chance and Allen and Overy have invested in private AI environments built on Microsoft Azure OpenAI Service to ensure data residency, encryption, access logging, and non-retention guarantees.

Model risk management and validation discipline

Legal AI systems must be governed as regulated decision-support tools. This requires documented validation protocols, accuracy testing, bias assessment, and version control. Corporate legal teams at organisations such as Shell and Pfizer require periodic performance reviews of AI tools, with defined error thresholds and mandatory human review for outputs that influence legal interpretation or contractual obligations.

Auditability and defensibility by design

Finally, governance frameworks must anticipate scrutiny. Legal organisations must be able to demonstrate how AI tools were used, what data they accessed, how outputs were reviewed, and who approved final decisions. Platforms from Thomson Reuters, Relativity, and Ironclad embed audit trails and traceability into AI-enabled workflows, supporting defensibility before courts and regulators.

Operationalising the Charter Across Legal Operations

An AI Governance Charter delivers value only when embedded in daily legal operations. Leading organisations integrate governance requirements into engagement letters, internal training programmes, vendor procurement standards, and enterprise risk reporting. Siemens, for example, incorporates AI governance criteria into third-party legal technology contracts to ensure alignment with its enterprise risk framework.

Firms such as Dentons and Linklaters have introduced mandatory AI training for lawyers that focuses on governance obligations, professional accountability, and risk awareness rather than technical model mechanics.

Conclusion: Governance as a Strategic Differentiator

Organisations that treat AI governance as a compliance afterthought will constrain adoption under the weight of unmanaged risk and regulatory scrutiny. By contrast, those that establish rigorous, enforceable AI Governance Charters will enable confident, scalable, and defensible AI deployment across legal operations.

For law firms and in-house legal teams, the question is no longer whether AI governance is required, but whether leadership is prepared to institutionalise it with the same discipline applied to financial controls and regulatory compliance. In an AI-driven legal landscape, governance is no longer a constraint on innovation. It is the condition that enables sustainable innovation.