China AI Compliance Checklist and Algorithm Filing Tracker

The compliance set for offering AI services in China: an applicability matrix across the CAC instruments, a security assessment checklist, a content labeling specification, and an algorithm filing tracker.

Who this is for: The compliance owner for AI products available to users in mainland China.

Download the kit (Markdown) ↓4 artifacts. Every table also copies as CSV.

1. China AI regulatory applicability matrix

Spreadsheet

Which CAC instrument applies to which product or feature.

Template

Product / feature	Generative AI Measures	Deep Synthesis Provisions	Algorithm Recommendation Provisions	Data / PIPL	Security assessment required?
<feature>	Y / N	Y / N	Y / N	Y / N	Y / N

Worked example

Product / feature	Generative AI Measures	Deep Synthesis	Algorithm Recommendation	Data / PIPL	Security assessment?
Text assistant (public)	Y	N	N	Y	Y (public-facing generative service)
Image generation	Y	Y	N	Y	Y
Product recommendation feed	N	N	Y	Y	filing, not full assessment
Internal analytics (no public output)	N	N	N	Y	N

Acceptance criteria

✓Every China-available feature is assessed against each instrument.
✓The trigger for a security assessment versus a filing is recorded per feature.
✓PIPL and data-export obligations are tracked alongside the AI-specific instruments.

2. CAC security assessment checklist

Spreadsheet

The areas a self-assessment (and any filed assessment) must cover for a public generative service.

Template

Area	Requirement	Status	Evidence
Training data	lawful sources; IP respected; no unlawful content; personal data handled per PIPL
Data annotation	annotation rules; staff training; quality checks
Content safety	filtering for prohibited content categories; refusal behaviour
Output testing	pre-release testing against a content-safety test set; documented pass rate
Model transparency	disclosure of service provider; complaint channel
Real-name and minors	user identity verification; minors protection measures
Incident handling	takedown, model tuning, and reporting process for unlawful content

Worked example

Area	Status	Evidence
Training data	Complete	provenance records; content filter on ingestion
Data annotation	Complete	annotation SOP; annotator training log
Content safety	Complete	multi-category filter; refusal tests
Output testing	Complete	2,000-prompt safety set; pass rate documented
Model transparency	Complete	provider disclosure + complaint form in-product
Real-name / minors	In progress	identity check via partner; minors mode pending
Incident handling	Complete	takedown + retrain + report runbook

Acceptance criteria

✓Every area has a status and attached evidence.
✓Output testing has a documented test set and a pass rate, not a claim.
✓A content-incident handling process exists and has been exercised.

3. Content labeling implementation specification

Spreadsheet

What must be labeled as AI-generated, how, and in which formats, per the labeling rules.

Template

Content type	Explicit label (visible/audible)	Implicit label (metadata)	Placement / format	Implemented?
Generated text
Generated images
Generated audio
Generated video

Worked example

Content type	Explicit label	Implicit label	Placement / format	Implemented?
Generated text	"AI-generated" notice near the output	provider + generated flag in response metadata	prepended line; not removable in the UI	Yes
Generated images	corner watermark + caption	C2PA-style metadata; provider ID	bottom-left, min 5% width	Yes
Generated audio	spoken disclosure at start	metadata tag	first 2 seconds	Partial
Generated video	on-screen label first + persistent corner mark	metadata tag	first 3 seconds + corner throughout	Partial

Acceptance criteria

✓Both explicit (visible/audible) and implicit (metadata) labels are specified per content type.
✓Label placement and format meet the size and duration expectations in the rules.
✓Partial items have an owner and a date.

4. Algorithm filing tracker

Spreadsheet

Filing status, registration numbers, and renewal dates for each algorithm subject to filing.

Template

Algorithm / service	Filing type	Submitted	Registration number	Approved	Renewal / update due	Owner
<name>	initial / change	YYYY-MM-DD		YYYY-MM-DD	YYYY-MM-DD	<name>

Worked example

Algorithm / service	Filing type	Submitted	Registration number	Approved	Renewal / update due	Owner
Text assistant (generative)	initial	2026-05-10	(redacted)	2026-07-02	on material change	China Compliance
Recommendation feed	initial	2026-04-01	(redacted)	2026-05-20	annual review 2027-05	China Compliance

Acceptance criteria

✓Every algorithm subject to filing has an entry with its current status.
✓Material changes to a filed algorithm trigger a change filing, tracked here.
✓Renewal and review dates are on the China compliance calendar.

Governance controls this kit produces evidence for

Completing the artifacts above gives you a head start on the evidence requirements for these controls.

CMP-001

The applicability matrix is multi-jurisdiction mapping for the China instruments.

CMP-006

The labeling specification is the AI content watermarking and labeling compliance record.

DGC-001

The training-data area of the CAC checklist maps to training data provenance.

SAF-004

Output safety testing against a content-safety set is AI reliability testing evidence.

CMP-002

The compliance calendar for CAC guidance updates is part of standards and regulatory monitoring.

This kit backs one playbook. Read the full guidance for the reasoning behind each artifact.

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