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China Algorithm Registry: Understanding Filing Requirements for AI Systems

China's algorithm filing requirements are easy to misunderstand because teams often collapse several regimes into one phrase. The Provisions on the Administration of Algorithmic Recommendation for Internet Information Services, the Deep Synthesis Provisions, the Interim Measures for the Management of Generative Artificial Intelligence Services, and related personal-data obligations can each matter depending on what the system does. Filing duties are especially relevant where an algorithm has public opinion properties or social mobilization capability, or where a public-facing generative AI service falls into a separate approval or assessment workflow. Keeptrusts does not file with the Cyberspace Administration of China, perform a security assessment, or determine whether a product needs registration. What it can do is support the part of the problem that engineering teams control directly: keep route inventory explicit, constrain provider handling, and preserve evidence when a filed or reviewable route changes over time.

Use this page when​

  • You are assessing how China's filing regimes affect live AI systems.
  • You need an engineering model for route inventory and change evidence around public-facing or review-sensitive AI services.
  • You want to avoid overstating what gateway controls can prove about regulatory filing status.

Primary audience​

  • Primary: Product counsel, compliance teams, platform engineers
  • Secondary: security teams, public-policy teams, service owners

The problem​

The biggest implementation mistake is treating filing as a one-time legal event. A team secures internal approval for a public-facing AI workflow, then continues to change prompts, provider order, output handling, and safety controls as the product evolves. Months later, the service looks materially different from the version that was originally documented.

That is not just a documentation problem. Filing-sensitive routes need stable inventory and durable change evidence. If engineering cannot show which providers were eligible, what output constraints applied, or when the route's operating model changed, the business loses the ability to explain the service coherently. In a China context, that creates governance risk even before you get to the formal legal analysis.

The second problem is overusing one global route. A public recommendation, a ranking service, a synthetic media workflow, and an internal drafting assistant should not share the same governance assumptions. Filing-sensitive systems need sharper separation because the product description, output behavior, and user impact are central to how the service is assessed.

The solution​

Use Keeptrusts to make the filing-relevant runtime characteristics stable and observable.

That starts with route isolation. Public-facing recommendation or generation routes should have their own provider set and their own control chain. data-routing-policy is useful because it makes provider handling explicit and fail-closed if the approved set changes. audit-logger and export jobs matter because they let engineering and compliance reconstruct what the route did at a specific point in time. If the product also needs DLP or prompt-injection controls, add them, but do not confuse those protections with the filing process itself.

This is where careful language matters. Keeptrusts helps with runtime governance around a potentially filed system. It does not turn an unfiled service into a compliant one. The right outcome is a cleaner handoff between product, legal, and engineering.

Implementation​

For a China-sensitive public AI route, keep the runtime footprint narrow and evidence-friendly.

pack:
  name: china-public-algorithm-route
  version: 1.0.0
  enabled: true

providers:
  targets:
    - id: china-reviewed
      provider: openai
      model: gpt-5.4-mini-mini
      secret_key_ref:
        env: OPENAI_API_KEY
      data_policy:
        zero_data_retention: true
        training_opt_out: true
        retention_days: 0
        sanitized: true
        allow_internet_egress: false

policies:
  chain:
    - dlp-filter
    - data-routing-policy
    - audit-logger

policy:
  dlp-filter:
    detect_patterns:
      - '(?i)\bnational\s*id\b'
      - '(?i)\bphone\s*number\b'
    blocked_terms:
      - internal ranking signal
      - unreleased moderation rule
    action: redact
    fuzzy_matching: true
    max_distance: 1
    sensitivity_level: high

  data-routing-policy:
    require_zero_data_retention: true
    require_no_training: true
    max_retention_days: 0
    sanitize_before_provider: true
    allow_internet_egress: false
    on_no_compliant_provider: block
    log_provider_selection: true

  audit-logger:
    retention_days: 180

And when you need a filing or change-review package, create an evidence export rather than relying on ad hoc screenshots:

kt export-jobs create \
  --from "2026-05-01T00:00:00Z" \
  --to "2026-05-31T23:59:59Z" \
  --format json \
  --filter-policy "china-public-algorithm-route"

That export is not the registry filing. It is the engineering evidence that shows how the route actually operated during the review window. That is useful because product changes are often what make a once-clear filing story become blurred.

Results and impact​

The practical result is better coordination. Product counsel can evaluate filing duties against a defined route instead of a moving target. Engineering can show which provider and data-handling rules were active. Change review becomes more disciplined because route modifications are recorded as configuration changes instead of disappearing into application code.

This also reduces overclaiming. Teams stop saying the gateway solved the filing requirement and start using it for what it is good at: route inventory, fail-closed provider control, and reproducible evidence.

Key takeaways​

  • China's algorithm filing duties should be analyzed route by route, not as a generic platform label.
  • A filing-sensitive AI service needs a stable runtime inventory, not just a launch memo.
  • data-routing-policy and export jobs help make route changes reviewable over time.
  • DLP and similar policies are useful supporting controls, but they are not the filing itself.
  • Keeptrusts is strongest here as an evidence and enforcement layer around a legally reviewed service design.

Next steps​