DPA Generator for CN

CN-specific obligations covered

The output is anchored on the regulations that apply to AI deployments in CN. The top frameworks cited:

• Personal Information Protection Law (PIPL 2021)national_law · In force

  Personal information processors must establish a lawful basis for processing, obtain separate consent for sensitive personal information, conduct personal information protection impact assessments for high-risk processing, and provide opt-out mechanisms for automated decision-making affecting individual rights.

• Interim Measures for the Management of Generative AI Services (CAC, 2023)administrative_regulation · In force

  Generative AI service providers must ensure training data is lawfully sourced, implement content filtering to prevent prohibited outputs, label AI-generated content, and obtain a CAC security assessment before launching public services.

• Provisions on the Management of Algorithmic Recommendations (CAC, 2022)administrative_regulation · In force

  Algorithm recommendation service providers must disclose their use of algorithms to users, provide opt-out options for personalised recommendations, refrain from dynamic pricing that discriminates against existing customers, and prominently label algorithmically distributed content.

• Provisions on the Administration of Deep Synthesis Internet Information Services (CAC, 2022)administrative_regulation · In force

  Deep synthesis service providers must implement real-name registration for users, label all synthetic content with a visible AI-generated marker, prohibit creation of content that impersonates real persons without consent, and retain synthetic content logs for sixty days.

How the DPA Generator approaches this

You describe your organisation, the AI vendor (Processor), the AI service being procured, and the categories of personal data the service will process. The tool maps your jurisdiction + industry + risk appetite into a structured, schedule-based DPA ready to redline with your legal team.

The DPA follows the format working contract lawyers recognise — main clauses for the contractual body, schedules for the GDPR Art. 28(3) particulars (subject matter / duration / nature / purpose / data categories / data subject categories), schedules for the operational details (TOMs, sub-processors, transfer mechanism), and annexes for the AI-specific protections + negotiation positions + consolidated review-required notes. Risk-appetite-driven defaults (security standard, audit notice, breach window, liability cap, model-update notice) are internally consistent across clauses, schedules, and the negotiation checklist. This is an AI-assisted drafting aid intended to accelerate review by qualified data-protection counsel.

What you get

• ✓ Schedule-based GDPR Art.28(3) format that working contract lawyers recognise — main clauses + 4 schedules + 3 annexes + signature blocks, not a flat clause-list.
• ✓ Sub-Processor schedule pre-populated with typical sub-processors for the named vendor (e.g. Microsoft Azure for OpenAI services), with explicit ⚠️ verify callout — saves the customer and counsel hours of inferring the chain.
• ✓ Inline ⚖️ qualified-legal-review callouts at known risk points (security standard, breach notification window, audit cost allocation, IP ownership, training-data restrictions, AI-error liability cap), consolidated into Annex C with the specific counsel competence required for each.
• ✓ Risk-appetite-driven defaults are internally consistent — security standard, audit notice, breach window, liability cap, and model-update notice all reference the same risk-appetite-driven values across clauses, schedules, and the negotiation checklist.

Ready to generate?

$39 · one-time — answer a 6-question intake (including jurisdiction = CN), and download your tailored document immediately.

Also available framed for your sector → see industry-specific pages