Govern AI-Assisted Contract Drafting with a Seven-Step Compliance Framework

Generative AI fits contract drafting the way Track Changes supports negotiation: once adopted, it becomes the default. The risk is that routine use develops into an uncontrolled process. Contract drafts carry privileged strategy, client-confidential information, regulated personal data, pricing, and business concessions that should never enter public AI tools or vendor logs. AI’s hazard is ordinary drafting failure at higher speed: invented terms, broken cross-references, mismatched definitions, and phantom statutes. This guide establishes a compliance-first workflow with clear boundaries, review gates, and vendor terms that match legal practice.

Start with Tool and Data Boundaries

A compliance-first contract workflow begins before anyone drafts a clause. The workflow begins by deciding which tools are approved for drafting, what information may enter them, and what must never leave the matter file. Ethics guidance has converged on a practical point: lawyers can use generative AI, but they remain responsible for competence, confidentiality, supervision, and the accuracy of work product. ABA Formal Opinion 512 frames generative AI as another technology lawyers must understand and supervise. Washington’s Advisory Opinion 2025-05 organizes the same idea into concrete duties, including competence, confidentiality, communication, supervision, and billing. New York City Bar Opinion 2024-5 similarly emphasizes that lawyers must understand tool limitations and protect client information.

For AI-assisted contract drafting, the easiest way to make those duties operational is a front-door rule set that everyone can follow under time pressure.

• Approved tools list: specify which tools are permitted for drafting, redlines, summaries, and clause comparison, and distinguish enterprise deployments from public consumer tools.
• Inputs policy: define what may be pasted into the tool, what must be anonymized first, and what is prohibited in all cases.
• Output handling: require that AI output be treated as unverified draft material until passing review gates.
• Role clarity: identify who may use AI for drafting, who reviews, and who signs off.

If a firm wants a simple boundary that mirrors what courts are now doing for their own internal work, look at how state court policies increasingly limit AI use to approved tools and warn against placing confidential or privileged information into non-secure systems. New York State’s Unified Court System interim policy is explicit about guardrails, approved tools, and restricting sensitive inputs. The New York UCS October 2025 announcement provides useful language compliance teams can adapt to transactional practice.

Build a Red List for Inputs

Contract-drafting teams move fast, and fast teams need a short list that prevents “accidental disclosure by convenience.” The goal is not to ban AI, but rather to stop the two most common mistakes: pasting sensitive deal facts into the wrong system, and assuming the model understands the deal context without being given structured instructions.

Here is a practical red list for contract-drafting inputs. The list is intentionally blunt.

• Privileged strategy: legal advice, negotiation posture, litigation risk, internal risk assessments, and draft language annotated with legal reasoning.
• Client confidential business terms: pricing, margins, proprietary product details, nonpublic forecasts, and unreleased roadmap information.
• Personal data beyond what is necessary: identifiers, HR records, health information, and sensitive category data, especially when the contract relates to employment or benefits.
• Third-party templates under restrictive licenses: if the firm does not have rights to reuse, do not upload into a tool that stores or trains on inputs.
• Authentication secrets: access tokens, credentials, API keys, or internal system identifiers.

Then provide a safe alternative for each red-list item. For example, if the drafting task requires facts, create a structured deal sheet and sanitize it first. If the task requires clause language, route drafting through an approved clause library or model forms repository, then ask the tool to work only within that approved material.

Draft from Approved Building Blocks

A compliance-first workflow treats the contract draft as assembly, not improvisation. That is not a style preference, the approach is a risk control. When AI is told to draft “a standard limitation of liability,” the system will generate plausible language that may not match the firm’s playbook, the client’s risk tolerance, or the governing law assumptions. The answer is to require drafting from controlled sources.

The most reliable pattern is “bounded drafting,” meaning the tool is instructed to draft only by adapting approved language. If the tool lacks enough information, the system must ask questions or flag gaps.

Sample bounded-drafting prompt a firm can adapt:

Draft a [clause name] for a [contract type] governed by [state]. Use only the clause library language pasted below and the deal facts in the Deal Sheet. Do not invent facts, defined terms, statutes, or cross-references. If information is missing, list questions before drafting. Output two options: a standard position and a client-favorable position, each with short drafting notes.

This prompt does two compliance-useful things: it narrows the model’s drafting discretion and creates a record of what the output was supposed to do. Those two features align with the recurring theme in ethics guidance: lawyers must understand tool limits and supervise outputs.

Treat AI Output as Unverified Draft

In transactional practice, the most expensive errors are the ones that look polished. Generative AI can produce text that reads like final language while quietly breaking the deal architecture. A compliance-first workflow prevents this by labeling AI output as unverified until clearing review gates that mirror real contract failure modes.

Use a short “four-check” standard that every reviewer must run.

• Defined-terms check: ensure every used term is defined, every definition is utilized as intended, and all terms match the deal sheet.
• Cross-reference check: sections, exhibits, schedules, and flow-down provisions point to real places and match numbering conventions.
• Factual-grounding check: the clause does not introduce dates, notice periods, deliverables, parties, or obligations that are absent from instructions.
• Risk-allocation check: liability caps, indemnities, termination, remedies, and dispute resolution align with the client’s position and the negotiating plan.

These checks are not theoretical. The checks translate the core warning repeated across AI ethics guidance into transactional terms: AI output can be wrong in ways that are hard to spot, so the lawyer must verify.

Force Review Gates before Sharing

Many AI failures happen at the moment of convenience. A junior drafter pastes a clause into a draft and sends the draft to a counterparty, or forwards a summary to a client, because the output reads clean and time is short. A compliance-first workflow treats external sharing as a gated event.

Set three gates that are easy to enforce:

• Gate one: no AI-drafted clause goes to a client or counterparty without human review, even if labeled “boilerplate.”
• Gate two: the reviewed draft must be saved as a version in the matter file with a reviewer name and date.
• Gate three: summaries and negotiation emails derived from AI must be confirmed against the draft, because summary errors create client expectation and dispute risk.

This is where supervision becomes operational. ABA Formal Opinion 512 emphasizes that lawyers remain responsible for work product and must supervise use of generative AI in practice, including by lawyers and nonlawyers. Washington’s Advisory Opinion 2025-05 emphasizes similar supervision duties.

Log Use without Overcollecting Prompts

A good compliance posture creates evidence that controls existed, while avoiding unnecessary retention of sensitive data. Contract teams sometimes solve the first part by logging everything, including full prompts and full outputs, and then discover they built a new category of sensitive record that must be secured, retained, searched, and produced. A compliance-first workflow aims for minimal, purposeful logging.

Start with a narrow log entry that answers accountability questions without storing deal secrets:

• Tool used: name and deployment type, such as enterprise tenant.
• Task type: clause drafting, issue spotting, summary, comparison.
• Document type: NDA, services agreement, SaaS, MSA, DPA, procurement template.
• Reviewer: name or role, plus date of review.
• Output location: matter file path or document-management reference.

Then define when prompts and outputs are retained, and why. Some organizations will retain prompt artifacts for quality control or defensibility. Others will minimize prompt retention to reduce the sensitivity of logs. Either approach can be made defensible if tied to a documented policy and implemented consistently, and if the firm understands its vendor’s retention and use of inputs. That emphasis on understanding how the technology works and how data is handled is a recurring theme in ethics guidance.

Lock Down Vendors and Retention

Contract drafting is one of the fastest ways to expose a vendor mismatch. Many tools are designed for general productivity, not professional confidentiality. A compliance-first workflow requires vendor terms that match the obligations lawyers carry, especially confidentiality.

Use a short “vendor minimums” checklist for any AI drafting tool:

• No training on client inputs: inputs and outputs are excluded from model training by default, or the vendor provides a clear opt-in mechanism.
• Retention controls: defined retention periods for prompts, outputs, and logs, with deletion commitments and administrative controls.
• Access controls: role-based access, audit logs, and secure administration for the firm.
• Security baseline: encryption in transit and at rest, vulnerability management, and incident-response obligations that fit a legal services environment.
• Subprocessors transparency: disclosure and controls for third parties handling data.

For teams that want a recognized way to structure these controls, NIST’s frameworks are practical because they translate governance into outcomes and tasks. The NIST AI Risk Management Framework provides a lifecycle-oriented way to manage AI risks, and NIST also published a Generative AI Profile as a companion resource. On the security side, NIST CSF 2.0 provides a cybersecurity-outcomes structure that compliance and security teams already understand.

Organizations that want a formal management-system approach sometimes look to standards such as ISO/IEC 42001, which sets requirements for an AI management system. Even if a firm does not pursue certification, the standard’s structure can help procurement and governance teams ask consistent questions.

Price the Work and Explain the Process

Contract drafting is also where billing confusion shows up first. A tool can compress drafting time, but the lawyer’s responsibility does not shrink with the clock. Ethics guidance repeatedly flags that fees must remain reasonable and lawyers cannot bill for time not actually spent, even if AI speeds up tasks. Washington’s advisory opinion explicitly includes billing in its duty set, and New York City Bar’s opinion discusses reasonableness and transparency expectations in the AI context.

Two practical rules avoid problems:

• Separate drafting from review: if AI accelerates drafting, make sure review time is real and documented, because review is where responsibility lives.
• Describe process, not novelty: if disclosure is required or chosen, describe controls, such as approved tools, restricted inputs, and lawyer review, rather than marketing language about innovation.

A Practical Workflow in Seven Steps

Below is the compliance-first contract-drafting workflow in a form a team can adopt as a standard operating procedure.

• Classify the matter: label the drafting request by sensitivity and confirm whether AI use is permitted under client instructions and firm policy.
• Select an approved tool: confirm the tool is on the approved list for that sensitivity level, and confirm the correct tenant or deployment is being used.
• Prepare a sanitized deal sheet: summarize facts needed for drafting in a structured format, and remove sensitive details that are unnecessary for the clause task.
• Draft within boundaries: instruct the tool to draft only from approved clause-library language and the deal sheet, and to flag missing information rather than inventing content.
• Run the four checks: defined terms, cross-references, factual grounding, and risk allocation.
• Save and log minimally: save the reviewed version to the matter file, and log tool use at a metadata level without overretaining prompt content.
• Share externally through a gate: require reviewer approval before sending any AI-assisted draft or summary to a client or counterparty.

The workflow above is intentionally boring. Boring is the point. In contracting, boring is how compliance scales.

Looking Ahead to Institutional Risk and Industry Standards

By January 2026, the adoption curve has shifted from “should we use AI” to “how do we control it.” Contracting is a natural place to build those controls because contracting is repetitive enough to standardize and risky enough to justify guardrails. World Commerce and Contracting’s 2025 report on AI adoption in contracting highlights both the growth in adoption and the persistent focus on security and trust concerns, which is exactly why a compliance-first workflow guide is more useful than another productivity post.

Meanwhile, public institutions are modeling the same posture: approved tools, restricted inputs, training, and human accountability. New York’s court system AI policy and California’s rule requiring courts to adopt generative AI use policies show how quickly “use it carefully” is turning into written governance. New York’s UCS policy and California Rule 10.430 are not law firm guidance, but they reflect the direction of travel for institutional risk management. Delaware’s interim policy on GenAI use by judicial officers and court personnel, effective October 2024, provides another model of how institutions are establishing clear boundaries for AI use while maintaining human accountability.

Sources

• American Bar Association Standing Committee on Ethics and Professional Responsibility: “ABA Issues First Ethics Guidance on a Lawyer’s Use of AI Tools” (July 29, 2024)
• California Courts: Rule 10.430, “Generative Artificial Intelligence Use Policies” (effective Sept. 1, 2025)
• Delaware Courts: “Interim Policy on the Use of GenAI by Judicial Officers and Court Personnel” (effective Oct. 21, 2024)
• International Organization for Standardization and International Electrotechnical Commission: ISO/IEC 42001:2023, “Artificial Intelligence Management Systems” (Dec. 2023)
• National Institute of Standards and Technology: NIST AI 100-1, “Artificial Intelligence Risk Management Framework (AI RMF 1.0)” (Jan. 2023)
• National Institute of Standards and Technology: NIST AI 600-1, “AI RMF Generative Artificial Intelligence Profile” (2024)
• National Institute of Standards and Technology: NIST CSWP 29, “The NIST Cybersecurity Framework (CSF) 2.0” (Feb. 26, 2024)
• New York City Bar Association Committee on Professional Ethics: “Formal Opinion 2024-5, Generative AI in the Practice of Law” (Aug. 7, 2024)
• New York State Unified Court System: “Interim Policy on the Use of Artificial Intelligence” (effective Oct. 2025)
• New York State Unified Court System: “Chief Administrative Judge Zayas Unveils Policy for AI Use Across All New York State Court System Functions and Devices” (Oct. 10, 2025)
• Washington State Bar Association Committee on Professional Ethics: “Advisory Opinion 2025-05, Artificial Intelligence-Enabled Tools in Law Practice” (Nov. 20, 2025)
• World Commerce and Contracting: “AI Adoption in Contracting” (2025)

This article was prepared for educational and informational purposes only. The article does not constitute legal advice and should not be relied upon as such. All cases, regulations, and sources cited are publicly available through court filings and reputable media outlets. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: When Machines Decide, What Are the Limits of Algorithmic Justice?