How to Use Functions to Automate Compliance Checks

Every compliance failure in a large content estate starts the same way: a legal disclaimer that never made it onto a regulated product page, an unredacted PII field that shipped to a public dataset, or a claim that violated advertising rules in one market but not another. In a legacy DXP, the safety net is a human. Someone in legal or compliance is expected to catch the problem during a review window, and when the volume of content outpaces the reviewers, things slip. The stakes are not theoretical: GDPR fines, EU AI Act obligations, and regulator scrutiny all attach to what actually publishes, not to what your policy document says should happen.

Sanity, the Content Operating System for the enterprise, reframes this. Instead of treating compliance as a manual gate at the end of the pipeline, Sanity Functions let you encode your rules as code that runs automatically on content events, an intelligent backend that enforces policy before content ever reaches production. This guide shows how to use Functions to automate compliance checks, where they fit alongside Roles & Permissions, Content Releases, and Audit logs, and how the automated approach compares to the review-heavy workflows enterprises run on their existing DXP.

Why manual compliance review breaks at enterprise scale

The core problem is arithmetic. A compliance team of five can meaningfully review a few hundred content changes a week. An enterprise with dozens of markets, thousands of SKUs, and a content marketing engine produces far more than that, and the gap is where risk lives. When reviewers are the bottleneck, one of two bad things happens: either the pipeline slows to the pace of the slowest reviewer, or people start waving content through to hit deadlines. Neither outcome is defensible in front of a regulator.

Legacy DXPs like Adobe Experience Manager and Sitecore address this with workflow engines, structured approval chains that route content to named approvers before it can go live. That depth is real, and enterprises have built serious governance on it. But an approval chain still assumes a human evaluates each item. It scales people, not throughput. If your German market needs a mandatory energy-efficiency disclosure on every appliance page, a workflow can require that a human confirms it is present, but it cannot itself know whether the disclosure is actually there.

This is the reframe the article turns on. Compliance rules that are deterministic, meaning a machine can evaluate them, should not consume human attention at all. Human review should be reserved for judgment calls: tone, brand risk, ambiguous claims. Everything mechanical (required fields present, PII patterns absent, disclaimers attached, market-specific rules satisfied) belongs in code that runs on every change, every time, without a reviewer in the loop. That is precisely the job Sanity Functions are built to do.

What Functions are and where they run in the pipeline

Functions are server-side code that Sanity runs in response to content events. When a document is created or updated, a Function can fire, inspect the document, and act on what it finds. Because they execute inside Sanity rather than as a separate service you have to host, monitor, and secure, there is no webhook relay to maintain, no queue to babysit, and no additional piece of infrastructure that can drift out of sync with your content model.

For compliance, the placement matters. A Function sees the content at the moment it changes, which means you can evaluate a rule before content is promoted to a published state rather than after it is already live and doing damage. Pair that with Content Releases and you get a genuinely powerful pattern: a batch of changes staged as a release can be run through a compliance Function as a unit, so a whole market launch is checked in one pass rather than page by page after the fact.

The practical shape of a compliance Function is straightforward. It reads the fields it cares about, applies a rule, and then does something with the result. That something might be setting a validation flag on the document, writing an entry that surfaces in a compliance dashboard, blocking promotion, or notifying a channel. Because Functions run with the App SDK and can query the Content Lake with GROQ, a single Function can also check a document against related content, for example confirming that a product variant inherits the same regulatory disclaimer as its parent. The rule lives in one place, versioned with your codebase, and applies uniformly across every market and brand in the estate.

Encoding real compliance rules: PII, disclaimers, and market rules

Start with the rules that are unambiguous, because those return the fastest payoff. A PII check is a good first Function: scan free-text fields for patterns that look like national identifiers, card numbers, or email addresses that should never appear in customer-facing copy, and flag or block the document when they do. This single check closes off one of the most common and most expensive failure modes, personal data leaking into a public dataset, without asking a human to eyeball every paragraph.

Required-disclosure checks are the next tier. Regulated industries carry mandatory language: financial products need risk warnings, health products need approved claims, and many markets require specific consumer-rights or energy-efficiency disclosures. A Function can assert that the relevant disclaimer field is populated and, using GROQ, that it matches an approved value from a reference document rather than free text someone edited. If it does not match, the document does not pass. That turns a policy PDF into an enforced rule.

Market-specific logic is where Functions earn their keep in a multi-market enterprise. The same product page has different obligations in the EU, the US, and the UK. Rather than trusting each market team to remember every local rule, you encode the ruleset once and let the Function branch on the market or locale field. Combined with Studio Workspaces, which let you model multi-brand and multi-market content in one place, a single library of compliance Functions can govern the entire estate. When a regulation changes, you update one Function and every affected document is re-evaluated, instead of retraining every regional editor and hoping the message lands.

Keeping AI-generated content inside the compliance loop

AI has quietly become a compliance problem, not because the technology is dangerous in the abstract, but because it removes the last human who used to see the content before it shipped. When a marketer drafts a page, they read what they wrote. When an AI agent generates a hundred product descriptions overnight, no one reads all hundred. The volume that makes generative AI attractive is exactly what defeats manual review, and the EU AI Act plus existing advertising and consumer-protection rules still apply to every word regardless of who or what produced it.

This is where the governance framing matters more than the AI framing. The question is not whether to use AI to produce content; enterprises already are. The question is whether AI-generated content is subject to the same automated checks as everything else. If your PII, disclaimer, and market-rule Functions run on every document change, they run on AI output too, with no special case required. The machine that generated the content and the machine that governs it operate on the same content events.

Sanity treats this as a first-class concern. Because Functions execute on every write and Audit logs record who or what changed a document and when, an enterprise can demonstrate to a regulator that AI-generated content passed the same compliance gates as human-authored content, with a defensible trail. Legacy DXPs increasingly bolt AI features on to an existing platform; here the governance layer and the content layer are the same system, so there is no gap between where content is created and where it is checked.

Building the audit trail regulators actually ask for

Passing a compliance check is only half the obligation. The other half is proving, months later, that the check happened. When a regulator or an internal audit asks who approved a claim, when a disclaimer was added, or whether a flagged document was actually remediated, the answer cannot be a shrug or a reconstructed guess from memory. Enterprises that cannot produce that evidence carry the full weight of a violation even when their intentions were sound.

Audit logs give you the immutable record of what changed, who changed it, and when, across the content in your datasets. Functions add the enforcement layer on top: rather than logging only that a field changed, you can have a Function write a structured compliance result back into the Content Lake, a record that says this document was evaluated against this ruleset on this date and passed or failed. Because that result is itself content, it is queryable with GROQ, so a compliance dashboard becomes a saved query rather than a bespoke reporting project.

Roles & Permissions and SSO close the loop by ensuring the audit trail is attributable to real, authenticated identities tied to your identity provider, and that only the right people can override a failed check. The combination (Functions enforcing, Audit logs recording, Roles & Permissions attributing) is what turns compliance from a hopeful policy into a demonstrable, defensible system. That posture sits on top of Sanity's underlying compliance foundation, including SOC 2 Type II, GDPR alignment, regional hosting and data residency, and a published sub-processor list.

A practical rollout: from one high-risk rule to full coverage

Do not try to encode every rule at once. The fastest way to stall a compliance-automation program is to attempt a complete rulebook translation before anything ships. Instead, rank your rules by two factors: how much financial or regulatory risk attaches to a failure, and how deterministic the rule is. The first Function you build should sit in the top-right of that matrix, high risk and fully mechanical, so that a single deployment removes a real liability and gives the program its first visible win.

With that Function running, use Content Releases as your safe rollout mechanism. Stage the change, run the Function across a representative release, and review what it flags before you enforce blocking. This surfaces the inevitable false positives (a legitimate value the rule was too strict about) and lets you tune the rule against real content rather than assumptions. Only once a rule is trusted should you promote it from flag to block, so that the pipeline never grinds to a halt over an untuned check.

From there, coverage grows rule by rule, and the compliance team's role shifts. Instead of reading every document, they curate the ruleset, review the exceptions the Functions surface, and handle the genuine judgment calls. This is the difference between scaling people and scaling output: the legacy DXP model asks you to add reviewers as content volume grows, while an automated model lets a stable team govern a growing estate. The Partner network can accelerate the initial buildout for enterprises that want a ruleset encoded quickly, but the operating model that remains is lean and durable.