A product receives the highest tier whose criteria it fully meets. The common criteria (C) apply to every tier.
All tiers
The product and the vendor's website state the NOCIP tier and say where AI processing takes place. Users can find this statement before first use (e.g. product page, settings, documentation).
- Evidence
- Addresses (URLs) and screenshots of the statements.
- Audit
- Open the statements on the website and in the product.
- Compare them with the architecture found in the audit.
- Passed if
- Statements present, accurate and matching the tier.
The documentation lists every model used for the product's AI functions: name, version, licence, origin and where it runs (customer, vendor or third-party AI, with country).
- Evidence
- Model inventory (table in the self-assessment).
- Audit
- Compare the inventory with installed model files, configuration and observed network traffic.
- Passed if
- Inventory complete and consistent.
C-03No training on customer data
Customer content is not used by the vendor or any third party to train or fine-tune models unless the customer explicitly opts in. Opt-in is off by default. Fine-tuning that takes place only on the customer's own systems is allowed.
- Evidence
- The vendor's terms and, if used, the third-party AI's terms; default settings.
- Audit
- Review the terms.
- Check the product's default settings.
- Passed if
- No training without explicit opt-in; opt-in off by default.
C-04No content in telemetry and logs
Telemetry, usage statistics, crash reports and remote logs contain no document content, prompts, AI output or personal data taken from processed content.
- Evidence
- Description of all telemetry and diagnostic data sent.
- Audit
- Process test documents containing canary values.
- Capture all outgoing traffic, including crash reports (trigger a crash where possible).
- Search the capture for canary values.
- Passed if
- No canary value outside the transfers the tier allows.
The vendor reports changes to the AI architecture (new model or model provider, new processing location, new external connection, changed router or pseudonymisation) to NOCIP within 30 days. A change that leads to a lower tier is reported before it goes live.
- Evidence
- Signed label agreement.
- Audit
- Check the label agreement.
- At spot checks and renewal: compare the architecture with the last audit report.
- Passed if
- Agreement signed; no unreported changes.
The vendor lists every outgoing network connection the product makes (destination, purpose, data sent). Processing the test documents creates no connections outside this list.
- Evidence
- Connection list (table in the self-assessment).
- Audit
- Run the product behind the audit proxy with packet capture.
- Process the test corpus.
- Compare observed connections and DNS queries with the list.
- Passed if
- No connection that is not on the list.
C-07Data processing agreement
If the vendor processes customer data (e.g. SaaS, remote support), it offers a data processing agreement under GDPR Art. 28 that names all sub-processors with access to AI input or output. Does not apply if the vendor receives no customer data.
- Evidence
- Data processing agreement template, list of sub-processors.
- Audit
- Review the agreement and the list.
- Compare the list with the model inventory and the connection list.
- Passed if
- Agreement available; list complete. Or: the vendor receives no customer data.
C-08User-triggered reports
If the product sends examples to the vendor at the user's request (e.g. to improve recognition), then only like this: (1) the user triggers each report, never automatically and never as a default. (2) The product first replaces personal data with invented data of the same format, without a mapping table. (3) A preview shows exactly what is sent, and the user can change it; words the user keeps in the original are confirmed once more before sending. (4) The vendor uses reports only to improve the product and does not pass them on. Does not apply if the product sends no reports.
Threshold: Replaced: ≥ 99 % of structured identifiers (IBAN, e-mail, phone, ID and tax numbers), ≥ 98 % of person names and ≥ 95 % of all canary values – as B-03.
- Evidence
- Description of the report function, screenshots of the preview, purpose stated in the terms.
- Audit
- Call the report function with texts of the test corpus.
- Capture the reports sent, search them for canary values and compute the rates per group.
- Check that nothing is sent without the user triggering it and that the preview matches what is sent.
- Review the terms on how reports are used.
- Passed if
- All four conditions met and all three thresholds reached – or the product sends no reports.
Silver
S-01Vendor-operated AI
Variant S-aAI that processes personal data runs at the customer's site or on vendor-operated infrastructure in the EEA. No third-party AI sees personal data. A cloud provider's model services count as third-party AI.
- Evidence
- Architecture description, hosting contracts, locations.
- Audit
- Review hosting contracts and locations.
- Obtain proof that the vendor runs the models itself (configuration, deployment, model files).
- Process the test corpus through the service; check the vendor servers' outgoing traffic where accessible, otherwise the vendor's logs.
- Passed if
- Models demonstrably run at the vendor in the EEA; no third-party AI receives canary values.
If a third-party AI is used, a router at the customer's site or on vendor-operated infrastructure checks every request. Only requests without personal data go to the third-party AI.
- Evidence
- Description of the router (method, location, categories).
- Audit
- Trace the router in the architecture.
- Check that no request reaches the third-party AI bypassing the router.
- Passed if
- Every request to the third-party AI passes the router.
S-03Router accuracy
Variant S-bThe router reliably keeps requests containing personal data away from the third-party AI.
Threshold: Of the test requests containing personal data, at most 0.5 % reach the third-party AI (≥ 99.5 % kept back); for requests containing structured identifiers (IBAN, e-mail, phone, ID and tax numbers), at most 0.1 % (≥ 99.9 %).
- Evidence
- The vendor's own measurements (optional).
- Audit
- Send at least 1,000 test requests per language (mixed, with and without personal data).
- Count which requests containing canary values reach the third-party AI.
- Compute the rates separately for structured identifiers and other data.
- Passed if
- Both thresholds met, in German and in English.
S-04Router fails closed
Variant S-bIf the router is uncertain, fails or cannot be reached, the request stays local or with the vendor.
- Evidence
- Description of the failure behaviour.
- Audit
- Deliberately disturb the router (stop it, force a timeout, return an invalid result).
- Check whether any request reaches the third-party AI.
- Passed if
- Under no disturbance does a request reach the third-party AI.
Bronze
B-01Pseudonymisation before transfer
Every request to a third-party AI passes a pseudonymisation step at the customer's site or on vendor-operated infrastructure.
- Evidence
- Architecture description of the pseudonymisation step.
- Audit
- Trace the pseudonymisation step in the architecture.
- Check that no request reaches the third-party AI bypassing it.
- Passed if
- Every request to the third-party AI is pseudonymised first.
At least the following are covered: names of natural persons, addresses, dates of birth, phone numbers, e-mail addresses, IBAN and account numbers, ID card, passport, tax, social security and health insurance numbers, file and case reference numbers, vehicle registration numbers, IP addresses. The vendor lists any further categories.
- Evidence
- List of categories covered.
- Audit
- Review the list.
- Process canary values of each category in the test corpus (see B-03).
- Passed if
- All minimum categories covered.
The pseudonymisation step reliably removes personal data. A canary value counts as leaked if it, or an obvious variant of its spelling, appears in plain text in the outgoing request.
Threshold: Removed: ≥ 99 % of structured identifiers (IBAN, e-mail, phone, ID and tax numbers), ≥ 98 % of person names and ≥ 95 % of all canary values overall.
- Evidence
- The vendor's own measurements (optional).
- Audit
- Process at least 200 test documents per language.
- Capture the outgoing requests to the third-party AI.
- Determine for each canary value whether it appears in plain text; compute the rates per group.
- Passed if
- All three thresholds met, in German and in English.
The mapping table (placeholder ↔ real value) never leaves the customer or the vendor. Placeholders in AI output are replaced back at the customer's site or at the vendor.
- Evidence
- Description of where the mapping table is stored and when it is deleted.
- Audit
- Search outgoing requests for mappings (placeholder next to canary value).
- Passed if
- No mapping in outgoing traffic.
B-05Pseudonymisation fails closed
If pseudonymisation fails or cannot be reached, nothing is sent to the third-party AI.
- Evidence
- Description of the failure behaviour.
- Audit
- Deliberately disturb the pseudonymisation (stop it, force a timeout, return an invalid result).
- Check whether any request reaches the third-party AI.
- Passed if
- Under no disturbance does a request reach the third-party AI.
Users can see what was sent to the third-party AI (preview before sending, or a log).
- Evidence
- Screenshots of the preview or log.
- Audit
- Open the preview or log in the product and compare it with the capture.
- Passed if
- Preview or log available and complete.
B-07Contract with the third-party AI
The contract with the third-party AI provider excludes training on the data, limits retention to at most 30 days, and provides for processing in the EEA or a valid basis for transfers to third countries.
Threshold: Retention ≤ 30 days.
- Evidence
- Contract or terms of the third-party AI, selected region.
- Audit
- Review the contract and configuration (region, retention).
- Passed if
- No training, retention ≤ 30 days, valid transfer basis.
The product and its marketing do not call pseudonymised data anonymous or anonymised.
- Evidence
- Addresses of product and marketing texts.
- Audit
- Review product and marketing texts.
- Passed if
- Nothing described as anonymous.