Introduction:
Artificial intelligence didn’t enter the workplace quietly—it arrived quickly and is now everywhere. Tools that generate text, classify documents, summarize meetings, or even screen applicants have become part of day-to-day operations. Most organizations didn’t develop these tools, they adopted them. And adoption is still happening at a pace that leaves little time for thorough assessment.
Just as companies begin adjusting to one version of a tool, a new one appears—faster, more capable, and often less transparent. It’s not a one-time transition; it’s a moving target. Managing AI inside an organization is starting to feel a bit like raising a child—you figure out how to handle the newborn phase, and just when you start getting comfortable, it’s time to deal with toddler mood swings. For legal departments, this often means being asked to govern something that’s both evolving and largely outside their technical control.
Amid all of this, legal departments are being pulled in to help make sense of it. And not just to review contracts or respond to incidents, but more and more, they’re being asked to build something foundational around what “responsible use” means in practice.
The uncertainty has led to a wide range of responses. Some attorneys advise against adopting AI tools at all, arguing the risk outweighs the benefit. Others lean in, aiming to shape internal norms and get ahead of regulation. Most sit somewhere in the middle: practical, cautious, and trying to make defensible decisions without perfect information. They understand that waiting too long could mean falling behind, but rushing in could mean facing risks they don’t fully understand.
This article is for people who are in that middle ground. The starting point does not necessarily have to be the technology itself; it can be what the technology is being used for. Legal teams don’t need to know how an AI model was trained to ask good questions about its use. A better approach would be to look at the business task (e.g., drafting, hiring, or analyzing customer feedback) and then think through the legal and regulatory issues tied to that kind of work.
Impact, not Architecture:
Traditionally, when new technology enters the workplace, the instinct, especially in legal and compliance roles, is to understand how it works in order to judge what it can do. With AI, that instinct quickly runs into limits. The pace of change, lack of transparency, and proprietary nature of many systems make it difficult (often impossible) to fully learn how a model functions under the hood. But from a legal perspective, what matters just as much is what the system can’t do, where it fails, and how those failures affect people, decisions, or obligations. You may not always be able to open the black box, but you can ask what the tool is being used for, what’s at stake when it gets things wrong, and whether the right safeguards are in place. That’s a shift in approach, and one that makes it possible to govern AI in a structured way, even without deep technical access. The following scenario shows what this looks like in practice.
It’s 9:30 a.m. on a Monday when the supply-chain director pings Legal: “Ops wants to integrate a new AI predictor onto our demand-planning dashboard. It reads two years of sales data plus some market indexes and spits out daily order forecasts. We think it can cut stock-outs by 2 percent and trim holding costs by 1 percent.” The product manager fills out the one-page AI intake form with purpose, data sources, people affected, and what decisions the output will drive, and shoots it over. Legal scans the form and slots the use case into the playbook: this is Forecasting, Tier 3. Why Tier 3? Because the numbers will steer procurement and show up in financial reports. Tier 3 pulls three extra checkpoints: Privacy impact assessment – already on file from the privacy team; Bias test on the training data – still missing; Human oversight – buyers must OK any purchase order the model suggests (already built into the workflow). Our internal script checks the repository. Two boxes are green; the bias report is red, so deployment is paused. The data-science crew runs a quick parity test on historic procurement gaps, uploads the results, and the script clears the block.
Forty-eight hours after the first ping, the plug-in is live. All artifacts sit in version control, linked to ISO 42001 clauses 6.1 and 8.2 and to EU AI Act Article 9—for anyone who needs the paper trail later.
Tiered Oversight:
The scenario above may sound familiar, maybe even aspirational. Either way, the logic behind it is simple: instead of trying to govern AI tools by what they are (a chatbot, a predictor, a classifier), start with what they are used for. This shift matters. AI models change constantly. Today it’s GPT-4, tomorrow it’s something faster with a new name. If your governance process is tied to the technical details of each tool, you will be chasing the documentation forever. But if you anchor your review to the business task, such as drafting emails, scoring leads, forecasting demand, you can build a stable, repeatable way to assess legal risk.
That’s where the use-case taxonomy comes in. The goal is to sort each proposed AI application by the job it’s doing, then apply a fixed risk tier based on that function. Legal teams don’t always need to know how the model was trained. They just need to know what the model’s output is used to decide. Once that’s clear, the rest of the review falls into place. For example, drafting or generation tools produce memos, emails, or presentations and raise issues around intellectual property ownership, defamation, and confidentiality. Triage or routing systems assign priorities or categorize tickets, where the legal exposure centers on fairness, discrimination, and procedural integrity. Forecasting tools, like those that generate sales estimates or demand curves, bring financial accuracy and consumer protection into focus. Personalization systems, which modify content or offers to individual users, can implicate profiling rules and privacy rights. Finally, safety-critical systems, specifically those issuing movement commands or controlling thresholds, may trigger product liability and safety regulation concerns. By categorizing AI tools this way, legal teams can concentrate on known risk domains rather than trying to interpret evolving technical architectures.
Once you understand what the tool is being used for, the next step is to decide how closely it needs to be reviewed. That’s where a tiering system comes in. The goal isn’t to block innovation or slow down the business, it is to calibrate legal oversight based on how much risk the tool creates. A tool that drafts internal notes is not reviewed the same way as a tool that scores job applicants. Instead of reviewing every AI system in the same way, the tiering model scales legal involvement depending on how the tool affects people, decisions, and outcomes.
Tier 1 may include tools with low impact and reversible outcomes, for example, generating internal memos or drafting non-binding content. These typically require only a basic intake form and registration. Tier 2 may cover moderate-impact tools that affect individuals or internal operations, such as classifying support tickets or summarizing meetings with named participants. These may call for a privacy review and prompt logging. Tier 3 may apply to tools with higher stakes—those that drive external outcomes or influence financial or employment decisions. Examples include forecasting demand, scoring sales leads, or screening resumes. These use cases require more structured oversight, such as a data protection impact assessment (DPIA), bias testing, and documented human sign-off. Finally, Tier 4 may be reserved for use cases that are either prohibited under current law or carry significant regulatory burden, such as emotion detection in hiring or systems that control physical machinery. These are either blocked outright or escalated for executive-level review. The tiering system allows legal teams to apply consistent controls based on risk, regardless of the technical architecture of the model used.
Importantly, this structure doesn’t need to be perfect to be useful. It’s designed to give legal teams a clear starting point. Over time, the tiers can be refined based on experience, regulatory changes, and business feedback. What matters most is having a shared language across teams—so that Legal, Product, and Procurement all know what a “Tier 2” tool means and what it requires.
Control Mapping:
Once a risk-based review structure is in place, the next step is to map it to external expectations. While ISO/IEC 42001 and the EU AI Act are widely referenced, they are not the only frameworks in play. Depending on the industry, jurisdiction, or use case, a company may be more concerned with sector-specific guidance (e.g., health data regulation, financial conduct standards), national AI policies, or even internal codes of ethics developed through industry consortia.
The legal value of a review process doesn’t come from aligning with any single standard, but from being able to demonstrate that the process exists, that it is applied consistently, and that it serves a clear risk-management function. This is where a use-case-driven framework proves adaptable. The same intake, tiering, and documentation steps can support different reporting structures depending on the context. For example, a financial services company may choose to map its controls to supervisory expectations from a central bank or financial conduct authority. A healthcare provider may align with guidance from a national health data regulator or sectoral ethics board. Multinationals may need to reference multiple regimes, such as mapping Tier 3 controls to ISO clauses in one region and to EU AI Act obligations in another.
In practice, this means each internal control step can be tied to more than one external reference point, depending on the context. For instance, risk identification and triage may correspond to ISO/IEC 42001 Clause 6.1 in one jurisdiction, OECD AI Principle 1 in cross-border settings, or Article 9 of the EU AI Act in European operations. Data governance may need to satisfy ISO 42001 Clause 8.3 for general controls, HIPAA in the United States for health-related systems, or GDPR Articles 5 and 6 when processing personal data in the EU. Human oversight, a recurring concern across sectors, can be grounded in ISO 42001 Clause 9.1, Article 14 of the EU AI Act, or industry-specific governance codes. Logging and recordkeeping may meet internal audit requirements or fulfill obligations under Article 12 of the EU AI Act. And ongoing performance monitoring can draw on ISO 42001 Clause 10, Article 61 of the EU AI Act, or the procedures already in place through internal risk committees. The specific standards may vary, but the underlying controls such as risk assessment, oversight, documentation, and monitoring remain stable across frameworks.
Conclusion:
The goal is to build enough traceability so that, for any AI tool in use, you can point to the review process, the tier assignment, the documentation produced, and how that maps to both your own policy and the external rulebook. This doesn’t require technical access to the model itself. If legal owns the intake process, the tiering decisions, and the checklist of required controls, that’s already enough to show a structured approach. And it becomes much easier to demonstrate compliance when regulators ask, “What’s your process for managing AI risk?”
