How Can AI Help with Regulatory Compliance Review?

AI for regulatory compliance is the use of AI to check a contract or policy against the regulations that govern it: it flags language that falls short of those regulations, ranks the compliance gaps by exposure, and drafts compliant replacement wording a lawyer approves or rejects. It does not decide whether a term is defensible, confirm that a regulator will accept it, or take the reviewing lawyer off the file. The gain for in-house legal teams is a shorter first pass: a regulatory compliance review that starts with the flagged exceptions and the regulatory obligation each one touches.

The work that changes is the first pass. A lawyer who would otherwise read sixty pages of third-party paper against one data processing obligation reads the eight clauses the screen marked, overrules the two it got wrong, records what was checked, and moves to the next file. The judgment stays where it has always been.

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Key takeaways

An AI regulatory compliance review performs five jobs on a legal document: it identifies which regulatory obligations apply, screens the text against them, ranks the compliance gaps by exposure, drafts replacement language, and records what was checked. Lawyers keep every judgment call, and the time saving shows up as a shorter first pass, not a shorter review.

1. What it screens: third-party paper, a company’s own templates and internal policies, against the statutes, regulations and industry standards the company is subject to.
2. What it cannot do: confirm that wording is legally sufficient, predict how a regulator will read it, or catch an obligation nobody configured it to look for.
3. Where the time goes: the screen is what gets faster. The judgment that follows the screen does not.
4. What makes it defensible: a record of which obligations the document was screened against, what the screen returned, and which findings the reviewing lawyer accepted or overruled.
5. What breaks it: an unconfigured document type, which returns a clean result that should not have been clean.

What a regulatory compliance review checks

A regulatory compliance review is a documented check that an organization’s agreements, policies and records meet the laws, regulations and industry standards that apply to it. For a legal team the unit of work is the document: a vendor contract that has to carry the data processing terms GDPR requires, a services agreement that has to survive a sector regulator’s audit, a policy that has to match what the company actually does.

The three questions a compliance review answers

1. Which regulations apply to this document, given what the company does and where it operates.
2. What each of those regulations requires the document to say.
3. Whether the document says it, in language that would hold up if a regulator asked.

Scaling from one document to many

Answering those three for one new vendor agreement is a document review. Answering them across every agreement a company has already signed is closer to a contract compliance audit, which works from a sample and a remediation plan instead of a single file, and which is usually triggered by a regulatory change or a diligence request, not by a counterparty sending paper.

Which regulations apply to a given document

Which regulations bind a given document depends on what the company does and who it sells to. A company shipping goods across borders works against export controls, customs classification and sanctions screening, the territory where international trade lawyers spend most of their compliance time. Four groupings cover most of what reaches a commercial legal team.

1. Data protection: the General Data Protection Regulation (GDPR) and the state privacy statutes, plus whatever enterprise customers have layered on top in their own data processing addenda.
2. Trade and sanctions: export controls, classification and restricted-party screening, which attach to the goods and the counterparty, not to the clause.
3. Intellectual property: disclosure obligations and recordal deadlines, which turn compliance work into docket management as much as drafting.
4. Sector regulation: the rules a specific regulator enforces, from financial services conduct rules through to healthcare privacy and anti-kickback requirements.

Compliance work in intellectual property (IP) law and in a regulated industry both run on deadlines a sector specialist knows and a commercial lawyer often does not, so the screen there is a triage step before a specialist read rather than a substitute for one.

How AI runs a regulatory compliance review, step by step

AI runs a regulatory compliance review in five steps: it identifies which regulatory obligations apply to the document, screens every clause against them, ranks the gaps it finds by exposure, drafts the replacement language, and writes down what was checked and by whom. Each step produces something a lawyer can accept, reject or overrule, which is what keeps the review defensible.

Step 1: Identify which regulatory obligations apply

The screen is only as good as the obligation set it is pointed at. In practice a legal team configures that set once per document type: a data processing addendum is screened against GDPR and the customer’s own security schedule, a healthcare vendor agreement against the privacy and anti-kickback rules that attach to the service being bought. The trigger for revisiting the set is a new jurisdiction, a new product line or a new regulator, not a calendar date.

1. The document type, because a reseller agreement and a data processing addendum do not share an obligation set.
2. The jurisdictions in play, which are the counterparty’s as well as the company’s.
3. The internal position on each obligation, meaning the clause the company has already approved, not the regulation restated.

Step 2: Screen every clause against the obligation

The screen reads each clause and reports whether the language an obligation requires is present, partially present or absent. This is the step that replaces the slow read, and it is worth running on paper a lawyer believes is already compliant: a Spellbook Labs review of 3,019 material-contract exhibits filed with the Securities and Exchange Commission found that 60% of SEC-filed contracts contain drafting errors, so the baseline is not clean to begin with.

1. Present: the required language is there. A reviewer confirms it is the right version of it.
2. Partial: the clause gestures at the obligation without meeting it. This is the result that needs a lawyer first, because it reads as a hit.
3. Absent: nothing in the document addresses the obligation, which is the easiest finding to act on and the easiest to miss by hand.

Step 3: Rank the gaps by exposure

Ranking is what makes a findings list usable. A list in document order still has to be read end to end, which is the read the screen was supposed to remove. Ranking works from what the gap would cost, not from where it sits: a missing breach notification deadline in a regulated-data contract outranks a stale address for notices. Teams that already run AI contract risk scoring on commercial terms can point the same scoring at regulatory findings, with the thresholds set by the team instead of the tool.

1. Likelihood the gap is tested, which is a function of the counterparty and the regulator, not of the drafting.
2. Cost if it is tested, including penalties, remediation and the contracts that would have to be reopened.
3. The escalation threshold, agreed in advance: which score sends a document to a specialist instead of back to the business.

Step 4: Draft the replacement language

For each gap the tool proposes wording. The better versions propose from a source the team controls, the clause the company has approved for that obligation, or the way the same obligation has been drafted in comparable agreements, instead of language generated from nothing. The lawyer accepts, edits or rejects each suggestion.

1. From the playbook: the approved clause, which is the default and the fastest to clear.
2. From comparable agreements: useful when the obligation is new and no approved position exists yet.
3. From a market benchmark: it shows what counterparties in the same position have accepted, which tells a lawyer where there is room to negotiate.
4. From nothing at all: language with no source behind it, which is the version to decline on anything regulated.

Step 5: Record what was checked and by whom

The output of the review is not only the marked-up document. It is the record of how the document was cleared, and teams that treat this step as administration discover its value the first time a regulator or an acquirer asks.

1. The obligation set the document was screened against, with its version.
2. The findings the screen returned, at the ranking it gave them.
3. The findings the reviewing lawyer accepted, and the ones overruled with the reason.
4. The name of the lawyer who signed off, and when.

What AI can and cannot verify in a compliance review

AI can verify whether a document contains the language a regulation requires. It cannot verify that the language is legally sufficient, that a regulator or counterparty will accept it, or that the obligation it matched is the one that actually governs the deal. The distinction matters because the two failures look identical on screen: a clause the model missed and a clause the model invented both read as a clean result.

Where the screen is least reliable

The last row of that table, on citations and cross-references, is the one with published evidence behind it. Stanford researchers testing large language models on legal questions found that legal mistakes are pervasive, including confidently stated citations to authority that does not exist. A compliance screen that cites a regulation by section number is making exactly the kind of claim that study found unreliable, so the section number is something to open, not something to trust.

Four failure modes worth watching

1. The silent miss: an obligation applies, nobody configured it, and the document comes back clean.
2. The invented hit: the screen reports a requirement, or a citation for one, that the regulation does not contain.
3. The stale position: the configured clause was right when it was written and the rule has moved since.
4. The orphaned obligation: a regulation applies to no configured document type at all, which is a different gap than a missed clause inside a type that is configured.

How AI helps legal teams keep up with regulatory change

AI helps legal teams keep up with regulatory change by monitoring the official publication feeds, matching each new or amended rule to the clauses already in the contract set, and surfacing the agreements that now fall short. The monitoring is only as current as the feed behind it, so the practical question for any tool is which sources it reads and how often.

The three feeds behind regulatory monitoring

For United States federal regulation there are three feeds worth naming, because a vendor that cannot name them is monitoring something else. New and proposed rules publish daily through the Federal Register API, which exposes each document on the day it appears.

The currently codified text is a separate source and a separate question. A rule as enacted and a rule as amended four times since are different texts, and a tool screening a contract against current law should be reading eCFR, the electronic Code of Federal Regulations, instead of a cached copy of the rule as originally published. Screening against a superseded version is the quiet failure here, because the output looks the same either way.

Proposed rules and the comment dockets attached to them are the third source, and the one that buys a legal team time. A change caught during the comment period on Regulations.gov is a remediation a team can plan ahead of time instead of running under deadline.

What real-time monitoring actually means

“Real time” is a claim worth testing, not accepting. These feeds publish on a schedule and a monitoring tool reads them on a schedule, so a change is typically visible within a day rather than within a second. What matters more than the latency is whether the tool can tell which of the agreements already signed are affected, which requires it to have read the contract set and not only the regulation. Teams doing this properly do contract compliance tracking continuously instead of reviewing at renewal.

1. Which sources does it read, named individually, and how often does it poll them.
2. Does it map a change to clauses, or only to practice areas and newsletters.
3. Can it list the affected agreements by name, with the clause that now falls short.
4. What triggers a re-screen, which should be both periodic and event-driven, because a rule change does not wait for a quarterly cycle.
5. Who maintains the mapping, because a feed changes format and a clause mapping rots quietly when nobody owns it.

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How much review time AI returns

AI can reduce compliance review time, and the reported savings are more modest than the category implies: Cyber GC reports 25-30% time savings across review, drafting, and final cross-checks. The reduction lands on the first pass: the screen is what gets faster, not the judgment that follows it. Thomson Reuters’ 2025 Future of Professionals survey found that respondents expect AI to save them about five hours a week over the next year, up from four hours predicted the year before, which is the realistic order of magnitude for a team screening contracts against regulatory requirements.

Reading the published figures

The 25-30% and the five hours a week both need reading carefully. The first is a customer’s own reported figure for its own workload, not an independent measurement, and the second is an expectation: Thomson Reuters asked professionals what they expect AI to save them, and that is the number they gave. A team that has never measured its current review time has no baseline to compare either figure against.

What to measure instead of time saved

1. Time from receipt to first marked-up pass, per document, which is the metric a screen actually moves.
2. Findings overruled per document, which shows where the obligation set is configured badly.
3. Coverage by document type, because a type where the screen saves nothing is usually a type nobody configured.
4. Documents cleared per reviewer per week, which is the business-facing number and the one that moves last.
5. Escalations to a specialist, which should fall as the obligation sets mature, not stay flat.
6. Time to close an escalation, once a finding reaches a specialist, which runs on a different clock than the first pass.

How to document an AI-assisted compliance review

Document an AI-assisted compliance review the way you would document a manual one: record which obligations the document was screened against, what the screen returned, which findings the reviewing lawyer accepted or overruled, and who signed off. ABA Formal Opinion 512 frames the duty in terms of competence and supervision, which means the record has to show a lawyer exercised judgment, not that a tool ran.

Which opinion actually binds you

The opinion is published in full as ABA Formal Opinion 512, and its practical effect here is that the lawyer of record cannot point at a tool’s output as the reason a document was cleared. The opinion frames competence and supervision as duties the lawyer carries personally, which is why the record exists in the first place: it is the evidence that a named person did the carrying.

State bars have issued their own opinions on generative AI with their own emphases, and the ABA position does not automatically bind a state that has written its own. Florida Bar Ethics Opinion 24-1, approved January 19, 2024, was among the first comprehensive state-bar opinions on generative AI and covers confidentiality, oversight, fees and advertising, so a team operating in more than one jurisdiction checks the opinions that actually bind it rather than assuming one travels. Adopting a tool for this work also brings duties about the tool itself, from supervision through to client confidentiality, and those are the subject of AI legal compliance instead of the review.

What the record has to show

Step 5 above lists what a record contains. What matters at the point somebody asks for it is what the record proves.

1. That a lawyer was in the loop, which is what the competence and supervision framing turns on.
2. That the scope was deliberate, meaning somebody chose the obligation set instead of accepting a default.
3. That disagreement was resolved, so an overruled finding reads as a decision, not an omission.
4. That the file can be reconstructed, including which version of the document the screen actually read.
5. That the record predates the question, because a file assembled after a regulator asks reads differently than one that already existed.

How regulators are scrutinizing AI in legal and regulated work

Regulators are scrutinizing how AI is marketed and used in legal and regulated work, which sets part of the standard an AI-assisted compliance review has to meet. The Federal Trade Commission announced Operation AI Comply in September 2024, a sweep whose first targets included a company marketing a robot lawyer service, and the Food and Drug Administration has proposed a framework for assessing the credibility of AI models used in drug and biological product submissions.

What the two actions mean for a legal team

The Federal Trade Commission (FTC) sweep is about claims: a tool that says it performs a legal task has to perform it, and a buyer who repeats the vendor’s claim to a client takes on the exposure that comes with it. The Food and Drug Administration (FDA) framework is the other side of the same question, a regulator setting out how it expects AI model credibility to be established when a sponsor relies on a model in a submission. A legal team in a regulated industry can read that as a signal about documentation: the question a regulator is likely to ask is not whether AI was used but whether its use was fit for the decision it supported.

Governing the tool is a separate exercise

Neither the FTC sweep nor the FDA framework is the same question as whether the AI system a legal team runs is itself compliant with the rules that govern AI systems. That is a separate body of obligations, covering risk classification, human oversight and transparency duties, and it is the subject of legally compliant AI use. The compliance review described here is the operational task.

How Spellbook supports regulatory compliance review

Spellbook screens contracts for regulatory compliance inside Microsoft Word, where the document already is. The Review feature reads an agreement against the Playbooks a legal team has configured, flags the clauses that depart from them, and proposes replacement language as native tracked changes the lawyer accepts or rejects. That is an AI regulatory compliance review in the place the review already happens.

Playbooks and the obligation set

Playbooks is where the obligation set from step 1 lives. A team encodes its primary and fallback positions for each obligation it cares about, and the Review feature applies them on every document instead of relying on whichever reviewer happens to have that obligation front of mind on the day. Spellbook’s Customer Success team builds custom Playbooks for a team in one to two weeks, which matters because an unconfigured document type is the main reason a screen returns nothing useful, and because the teams that get value from a screen are the ones whose positions were written down before it ran.

Drafting and the market check

For the drafting step, the Draft feature proposes replacement wording in the document itself, and the Compare to Market feature answers the adjacent question of what counterparties actually accept, drawing on 270+ clause types across 20M+ real market contracts plus 2,300+ out-of-the-box benchmark standards. A lawyer deciding whether to hold a position or trade it gets the market picture next to the obligation instead of in a separate tool. For an obligation that reaches past one document, Spellbook’s Associate feature re-screens the clause across the contract set a legal team has in the platform, which is how a team answers which signed agreements a given regulatory change now touches, instead of checking one document at a time.

Where Spellbook stops

Spellbook does not make the compliance call. It is a commercial-contract specialist rather than a regulatory research platform, so it does not tell a team which regulations apply to its business, and it does not replace the judgment the record has to show. The output is a shorter first pass and a defensible set of findings, with the lawyer still deciding what each finding means.

Security and getting started

Spellbook operates on zero data retention, which means its large language model providers do not learn from, train on or store customer data. Spellbook operates under SOC 2 Type II and starts with a free trial, not an implementation project. Try Spellbook free to see how a configured review runs against your own paper, inside Word, on a document your team has already cleared by hand.

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Frequently asked questions

Does an AI compliance review remove the need for legal reviewers?

No. An AI compliance review changes what a reviewer reads first, not whether a reviewer is needed. The screen reports whether required language is present; a lawyer decides whether what is present is sufficient, whether an absent obligation actually applies, and what to do about a gap. The two categories of error that matter most, a missed obligation and an invented one, both present as a clean result, which is why a human reads the output.

Can AI summarize a new regulation and flag where a contract set falls short?

Yes, and the second half is the harder half. Summarizing a published rule is well within what current tools do reliably. Matching that rule to the clauses in a set of signed agreements requires the tool to have read the agreements, to know which obligation each clause was meant to satisfy, and to be configured with the team’s position on it. A tool that can summarize but has not read the contract set produces a reading list, not a remediation list.

How current is AI-assisted regulatory change monitoring?

As current as the feed it reads, which for United States federal regulation means daily rather than instant. Proposed and final rules publish to the Federal Register on a publication schedule, the codified text updates in the electronic Code of Federal Regulations, and a monitoring tool polls those sources on its own schedule. A team that needs to know about a change the day it publishes should ask a vendor which sources it reads and how often.

What should a legal team check before adopting an AI compliance review tool?

Five things are worth establishing before a trial: which obligation sets can be configured and by whom, where the suggested language comes from, what record the tool produces per document, how it handles the data in the documents it reads, and what it does when it is unsure. Comparing products on those five is more informative than comparing feature lists, and the current field of AI tools for compliance lawyers differs more on the first and third than on anything else.

Who owns an AI-assisted compliance review inside the company?

The reviewing lawyer owns the clearance, and in most teams legal operations owns the configuration. Splitting it that way keeps the obligation set maintained by the people who see every document type while leaving the judgment with the lawyer whose name goes on the sign-off. Compliance, procurement and the business owner for the contract are the three groups that need to see the findings, which is a reason to agree the escalation threshold before the first screen.