CCPA CPRA AI Compliance Checklist: Ten Items Before the April 2027 ADMT Deadline

California's ADMT clock is running. The California Privacy Protection Agency's regulations were approved on 23 September 2025 and took effect on 1 January 2026, with automated decisionmaking technology obligations attaching from 1 April 2027, initial risk assessments due by 31 December 2027, and the first CPPA submission on 1 April 2028.

This checklist follows the order a CPPA review tends to take: scope and identity records first, then pre-use notice and opt-out, then access, appeal, security, and breach readiness. Each item pairs the action with the evidence it produces, because the regulations test what happened to a specific consumer's decision rather than what a programme document intended.

1. Decide which AI uses are ADMT for significant decisions

Inventory every AI call and mark the ones where computation replaces or substantially replaces human decisionmaking about financial or lending services, housing, education enrollment or opportunity, employment or independent contracting, or healthcare. Evidence is a scoping record naming each system, the decision it drives, and the reasoning, since advertising and most internal productivity uses fall outside.

2. Bind identity to every AI request

Route AI traffic so a named user, agent, or consumer context, and not a shared service key, is attached to each prompt. Evidence is a per-request record showing which identity sent which prompt to which model, which is what every downstream ADMT right depends on.

3. Bind the pre-use notice version to the request

Record which notice and which policy version were in force at the moment of each ADMT decision. Evidence is a per-request policy-state stamp, which is what proves a published notice actually governed the decisions a consumer is asking about.

4. Enforce opt-out at the boundary

Refuse or reroute requests from consumers who have opted out of ADMT for significant decisions, before the prompt reaches the model. Evidence is the refusal record. An opt-out honored only inside application code leaves nothing an auditor can distinguish from an opt-out that was ignored.

5. Classify the personal information in the prompt

Evaluate the classification of the context window before it leaves, separating ordinary personal information from the sensitive personal information categories the CPRA created. Evidence is a per-request classification decision recorded at the boundary.

6. Produce a per-decision record for every ADMT output

Write a record binding the input prompt to the output and the applied policy for each significant decision. Evidence is decision lineage, the operational backing for the consumer's right to information about the logic, and it mirrors GDPR Article 22 handling.

7. Link the appeal chain to the original decision

Flag decisions that must support appeal and link the original decision record, the consumer's appeal, and the human reviewer's outcome into one chain. Evidence is that chain, retrievable by consumer and timestamp, which is the piece that breaks when decisions live in application logs and appeals live in a ticketing system.

8. Replace static API keys with per-role, per-route policy

Remove shared credentials granting permanent full model access to any caller, which is where CCPA section 1798.100(e) reasonable security meets the cybersecurity audit phasing in from 1 April 2028 by revenue tier. Evidence is an access policy plus the enforcement log showing which roles reached which models.

9. Keep the record independent of the application

Ensure the compliance log is written by a system separate from the one making the AI call, so it survives selective logging, suppression, and crash-loss. Evidence is a signed, tamper-evident log outside application custody, as argued in You Own the AI Liability, Not the Vendor.

10. Pre-build the breach-scoping path

Confirm you can reconstruct which prompts carried which personal information to which endpoint, because CCPA section 1798.150 gives consumers a private right of action with statutory damages of $100 to $750 per consumer per incident. Evidence is a query against the independent request log that produces the affected class. My honest take: that per-consumer math will move California AI logging budgets faster than any CPPA enforcement action.

How the checklist maps to the controls

Items 1 through 10 resolve to a handful of enforcement points at the request boundary, laid out in the CCPA controls mapping. The audit-evidence view covers what each artifact looks like when the CPPA asks. Together they turn this list into a running system with evidence attached.

DeepInspect

Nine of these ten items produce evidence at one place: the boundary between your users or agents and the LLM APIs they call. DeepInspect sits inline at that boundary. It binds identity to each request, reads opt-out state, classifies the prompt, stamps the policy version in force, flags decisions for appeal, and writes a signed per-decision record before the response returns. The checklist becomes a query rather than a scramble.

For a business counting down to 1 April 2027, items 4 and 7 are the two that a consumer complaint will reach first. Book a technical deep dive at deepinspect.ai.