AI Governance & Legal Ops: From Policy To Product-level Controls

Most organisations say they have an AI policy and are aligned with AI governance and regulations. However, regulators have reflected and uncovered that the organizations are far from the actual scenario.

AI governance has entered its enforcement phase. Across jurisdictions, oversight bodies are moving beyond ethical principles and high-level policy statements. Instead of demanding demonstrable control over how AI systems, particularly General-Purpose AI, are sourced, deployed, monitored, and updated.

In this environment, governance is no longer judged by intent, but by the presence of product-level controls, auditable documentation, and enforceable supplier obligations. This shift fundamentally redefines the role of in-house legal from policy advisor to operational owner of AI risk.

In 2025, the European Commission published a General-Purpose AI Code of Practice to help providers of general-purpose models comply with the GPAI provisions of the AI Act. The Code, developed in collaboration with over 1,000 stakeholders, outlines transparency and documentation standards, including model documentation templates, and provides a voluntary yet widely recognised mechanism for demonstrating compliance, thereby underscoring the regulatory emphasis on auditable evidence for GPAI deployment.

What is General-Purpose AI, and why is it so important right now for the shift?

General-Purpose AI (GPAI) refers to AI models that are trained on broad datasets and designed to perform a wide range of tasks across domains, rather than being built for a single, narrowly defined use case. These models can be adapted or fine-tuned for diverse downstream applications, such as content generation, summarisation, decision support, or analytics, and are often supplied by third-party providers via APIs or platforms.

Under the EU AI Act, GPAI is treated as a distinct category with specific transparency, documentation, and risk-management obligations due to its scale, adaptability, and potential systemic impact. Because GPAI models are reusable, scalable, and often supplied by third-party providers, they introduce cross-cutting legal, operational, and governance risks that span intellectual property, data protection, consumer protection, and systemic safety considerations across jurisdictions.

The European Commission's guidance and public-summary templates for GPAI underscore this shift. Firms deploying or relying on third-party AI models are expected to maintain compliance-grade documentation, supplier assurance, and auditable controls across the model lifecycle. In this environment, a purely advisory legal function is no longer sufficient. AI governance must shift from policy to product-level controls, and in-house legal is increasingly expected to own that transition.

This brings us to the question. How in-house legal moves from advisory to owning model-risk frameworks, documentation, and supplier audits for General-Purpose AI? As General-Purpose AI models become embedded across products, operations, and customer-facing systems, regulators worldwide are making one expectation clear: organisations must demonstrate how AI risks are governed in practice, not merely that policies exist.

The Microsoft report highlights the rapid diffusion of advanced, multi-use AI systems globally. One in six people worldwide uses AI tools in everyday work and decision-making. This level of adoption is indicative of widespread enterprise reliance on general-purpose AI models, typically supplied by third-party providers and reused across multiple downstream applications, often without direct visibility into training provenance, model updates, or lifecycle controls. The scale and speed of this reliance fundamentally shift what in-house legal must govern, from policy articulation to ownership of model-level risk, documentation, and supplier assurance.

The Strategic Imperative

Until recently, legal teams advised on contracts and compliance while product, data, and security teams handled technical control designs. The arrival of competent, externally supplied GPAI changes that balance.

GPAI introduces opaque training provenance, frequent model updates, and emergent behaviors that create legal exposure across intellectual property, data protection, consumer protection, and corporate disclosure regimes. In short, model risk is an enterprise risk, and it requires a single operational owner with the authority to create and enforce auditable controls.

Why Advisory Legal Models Break Down for GPAI?

General-Purpose AI differs fundamentally from traditional software and earlier generations of machine learning. These models are trained on vast, often opaque datasets, updated frequently by vendors and capable of emergent behaviour that cannot be fully predicted at deployment. As a result, GPAI introduces legal exposure across intellectual property, data protection, consumer protection, employment, financial regulation, and corporate disclosure regimes, often simultaneously.

Historically, legal teams advised on compliance while product, data, and security teams designed and operated technical controls. GPAI collapses this separation. Model behaviour, training provenance, and update cadence now directly affect legal risk, yet they are controlled externally by vendors. In this context, legal advice without operational authority leaves organisations exposed.

Regulators are no longer satisfied with after-the-fact explanations or aspirational governance statements. They expect evidence of continuous oversight: documented risk assessments, enforceable supplier obligations, incident-response mechanisms, and auditable records that persist over time. Meeting these expectations requires legal to move upstream and become the operational owner of AI governance.

Why Legal Must Own Model-Risk Governance?

AI governance regimes increasingly attach obligations to AI systems and their supply chains, not merely to internal processes. The EU's GPAI framework, for example, requires lifecycle documentation, training-content summaries, and systemic-risk controls that must be legally durable and defensible.

Technical frameworks such as NIST's AI Risk Management Framework provide valuable guidance on identifying and mitigating risks, but they do not resolve questions of liability allocation, evidentiary sufficiency, or contractual enforcement. Procurement teams can onboard vendors, and IT teams can test performance, but neither function can define what constitutes legally admissible evidence or negotiate binding audit and remediation rights.

Legal is uniquely positioned to translate regulatory expectations into enforceable obligations, define documentation standards that withstand scrutiny, and retain the records regulators and courts will demand. For GPAI, legal ownership is not about replacing technical teams; it is about creating a control plane that makes technical governance legally effective.

The Structural Gaps Driving the Shift

Most organisations encounter similar challenges as they attempt to scale GPAI:

• Fragmented accountability - Model development, deployment, and procurement are siloed, leaving no single owner of end-to-end governance.
• Documentation shortfalls - Many GPAI providers disclose limited information on training data, evaluations, or safety mitigations, despite regulatory expectations.
• Contracts misaligned with AI risk -Traditional SaaS templates rarely address provenance, retraining notifications, explainability, or model-specific audit rights.
• Unclear risk tiering - Legal teams often lack operational frameworks to classify AI systems by legal and regulatory risk.
• Limited enforcement mechanisms - Without tailored indemnities, SLAs, and audit triggers, organisations have little leverage when third-party models cause harm.

These gaps make AI deployments difficult to defend and force legal teams into reactive, crisis-driven roles.

From Advisory to Ownership: A Legal-Led, Compliance-by-Design Framework

The transition from advisor to owner mirrors the evolution of privacy engineering. Just as privacy-by-design embedded legal requirements into system architecture, AI governance now requires compliance-by-design, embedding legal controls directly into how models are selected, contracted, deployed, and monitored.

A practical framework for in-house legal includes the following components.

• Owning Model-Risk Classification

Legal should define and own a risk-tiering taxonomy that maps AI use cases to regulatory exposure, consumer impact, and systemic reach. Jurisdictional overlays such as EU high-risk classifications or sector-specific obligations in finance and healthcare determine documentation depth, audit frequency, and contractual rigor.

• Standardising AI-Native Contract Controls

Legal teams must replace generic cloud templates with AI-specific clauses, covering training-data summaries, model cards, update and retraining notifications, audit and access rights, incident reporting timelines, and tailored indemnities for IP, privacy, and regulatory harms. These clauses should be mandatory for all new and renewing GPAI vendors.

• Converting Technical Artefacts Into Legal Evidence

Model cards, lineage statements, evaluation metrics, and red-team reports are no longer internal engineering artefacts; they are regulatory evidence. Legal should define minimum content standards, require vendor attestations, and maintain these materials in a version-controlled, searchable repository that supports audits and investigations.

• Leading Supplier Assurance and Audits

For high-risk models, legal should require independent third-party audits or contractually triggered reviews following material model updates. Audit cadence, remediation obligations, and escalation mechanisms must be aligned with legal risk tiers and enforceable through contract.

• Chairing Cross-Functional Governance

An AI Governance Committee, bringing together legal, product, security, privacy, procurement, and compliance, should operate under legal process ownership. Legal maintains policy, templates, and evidence standards; technical teams implement and validate controls within that framework.

• Measuring and Proving Control

Key performance indicators such as documentation completeness, audit coverage, incident response times, and remediation rates enable board reporting and regulatory readiness. Evidence trails must be maintained continuously, not assembled after issues arise.

• Building AI Legal Operations Capability

To scale ownership, legal teams must develop operational capacity. This often includes training counsel on AI concepts and establishing a dedicated "AI Legal Ops" function to manage intake, classification, vendor reviews, and audit coordination.

Global Regulatory Convergence Reinforces Legal Ownership

Regulatory and standards bodies are converging on common expectations for AI governance. EU GPAI templates, NIST's AI RMF, Singapore's Model AI Governance Framework, and emerging ISO AI management standards collectively establish a global baseline: transparency, traceability, and enforceable governance across the AI lifecycle.

The practical implication is unavoidable. Organisations that cannot produce model-level documentation and supplier assurance will struggle to defend GPAI deployments across jurisdictions, regardless of where the model was developed.

The Business Case for Legal Ownership

Legal-led AI governance delivers measurable value. Financial institutions have passed regulatory inspections by producing complete model-lineage documentation. Healthcare providers have enforced contractual remediation when vendor updates degraded performance. Consumer-facing platforms have reduced privacy complaints and reputational risk through pre-deployment attestations and tiered controls.

Beyond risk reduction, these frameworks accelerate innovation by reducing procurement friction, clarifying approval pathways, and enabling responsible scaling of AI across products and regions.

Conclusion

The question facing in-house legal is no longer whether to engage with AI governance, but how quickly it can move from advisor to owner. General-Purpose AI demands governance that is operational, auditable, and enforceable across the model lifecycle.

Organisations that empower legal to own model-risk frameworks, documentation standards, and supplier assurance will be best positioned to meet regulator expectations, reduce liability, and scale AI responsibly. This shift does not inhibit innovation; it provides the legal and operational foundation that makes innovation sustainable.

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.