Compliance teams that use generative AI to draft policies are trying to solve a specific, familiar problem, such as policies drifting out of alignment with the rules they implement. Every regulatory change can touch a dozen policies and procedures, and each one has to be found, revised, checked against the rule, and re-approved.
Generative AI produces a first draft and a proposed citation map in minutes, leaving the compliance officer to review and decide rather than to build from scratch. This article walks through how that works, using Ask Kaia as an example.
Why Policy Work Is a Natural Fit for Generative AI
Policy management is suited to generative AI for three reasons:
- The work is document-centric, and language models handle documents well.
- The lifecycle is procedural, since every policy follows a defined path of drafting, review, approval, attestation, and retirement.
- The volume is relentless, because regulatory change arrives continuously and each change can ripple across many documents.
Drafting a policy is largely a matter of translating a regulatory requirement into an institution's own procedures and language, then keeping that translation current as the rule evolves. That is exactly the kind of grounded, repeatable task where generative AI compliance tools perform well, provided they work from the right source material.
The Generative AI Policy Drafting Workflow, Step by Step
A disciplined workflow for using generative AI in policy drafting follows a short, repeatable sequence, as follows:
Ground the Model in Your Policies and the Regulation
The tool must work from your policy inventory and the current regulatory text. Platforms that use retrieval-augmented generation pull the relevant policy and rule into the model's context before it writes, so the output is anchored to real source documents.
Draft or Revise with Tracked Changes
With the source material in hand, the tool drafts a new policy or proposes revisions to an existing one. A reviewer needs to see exactly what the model proposes to add, cut, or reword when making revisions. This keeps the compliance officer in control.
Map Each Requirement to Its Citation
A good tool produces a citation alongside the draft, so a reviewer can click from a policy sentence to the underlying regulation.
How AI Maps Policy Language to Regulatory Citations
Citation mapping is the capability that distinguishes a tool as compliance-grade. The model links a policy statement to the specific regulatory source behind it. Done well, the map runs from a policy to its governing rule, and from a new or amended rule to the policies it affects. That is where regulatory change management and policy drafting meet. When a rule changes, a citation-aware system can flag every policy that references the affected provision and queue it for revision. The map becomes the connective tissue between the regulatory feed and the policy library.
Keeping a Human in the Loop
Every draft, revision, and citation map should pass a qualified reviewer before it becomes policy, and the approval checkpoint should be a configured requirement rather than an optional courtesy.
In practice, that means three review disciplines:
- A reviewer confirms that each proposed change is accurate and appropriate for the institution.
- A named owner approves the final policy and is accountable for it.
- An attestation trail records who approved what and when.
Good policy management software supports these checkpoints, keeping the AI-drafted content inside the same system of record that stores approvals and attestations.
Governance and Examiner Expectations
The OCC's revised model risk management guidance, issued as Bulletin 2026-13 in April 2026, replaced the 2011 framework known as SR 11-7 and explicitly places generative and agentic AI outside its current scope. The agencies have said a request for information on AI and model risk is coming.
That does not leave the work ungoverned. Examiners still expect documented intended use, validation of outputs proportional to risk, a named human accountable for every policy an AI touches, and an audit trail complete enough to reconstruct how a policy changed and who approved it.
In the 2025 Global Compliance Risk Benchmarking Survey by White & Case, 62% of compliance functions reported using AI in some capacity, with document summarization (88%) and document review (85%) as the most common use cases, both close cousins of policy drafting.
A Worked Example: Ask Kaia's Policy Agents
Ask Kaia offers a concrete look at how these steps become software. In February 2026, we added a set of AI compliance agents, each of which carries a defined policy task from start to finish and records an auditable output.
The table below maps the relevant agents to what they do, what they produce, and where the human checkpoint sits.
| Ask Kaia agent | What it does | Output | Human checkpoint |
|---|---|---|---|
| Policy & Procedure Agent | Drafts and maintains a policy from your business profile and the relevant regulation | Draft policy grounded in source rules | Compliance officer reviews and approves the draft |
| Policy Revision Agent | Analyzes an uploaded policy against current requirements | Revised draft with tracked changes | Reviewer accepts or rejects each change |
| Federal Register Tracker | Monitors regulatory updates and maps them to affected policies | List of impacted policies with a change summary | Analyst confirms scope and sets priority |
| Regulatory Impact Analyzer | Assesses how a regulatory change affects the institution | Impact assessment tied to the rule | Compliance team validates and assigns tasks |
Frequently Asked Questions
Can generative AI write a compliance policy?
Generative AI can produce a solid first draft of a policy when it is grounded in your existing policies and the relevant regulation. It works by translating a regulatory requirement into your institution's procedures and language, then mapping each statement back to its citation. The draft still needs review and approval by a qualified compliance professional before it becomes policy.
How does AI map policy language to regulatory citations?
A citation-aware tool links each substantive policy statement to the specific rule behind it, such as a section of the Code of Federal Regulations or an interagency guidance document. It also works in reverse, flagging which policies are affected when a rule changes. This two-way mapping connects the regulatory feed to the policy library, which is the step that keeps policies from drifting out of alignment with current rules.
What does an examiner expect when policies are AI-drafted?
Examiners expect documented intended use, validation of outputs proportional to risk, a named person accountable for each policy, and an audit trail that shows how a policy changed and who approved it.
How much time does AI policy drafting save?
Time savings vary by institution and policy complexity, and the gains come mainly from producing first drafts, revisions, and citation maps that a person then reviews rather than builds.
See how AI compliance agents fit inside a GRC system of record, and to read the OCC's revised model risk management guidance so the oversight questions are settled before an examiner raises them.
Learn how Ask Kaia can assist your organization’s compliance team in gaining clarity on regulatory changes.
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- 1Why Policy Work Is a Natural Fit for Generative AI
- 2The Generative AI Policy Drafting Workflow, Step by Step
- 3How AI Maps Policy Language to Regulatory Citations
- 4Keeping a Human in the Loop
- 5Governance and Examiner Expectations
- 6A Worked Example: Ask Kaia's Policy Agents
- 7Frequently Asked Questions