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Compliance

NVIDIA NIM in Switzerland

Structured issue-spotting for deploying NVIDIA NIM in Switzerland, read against the rules for that jurisdiction.

Source
Rules engine over a generic brief — no anchored quote on this page
Verified
Evidence not verified
Confidence
Low

Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.

01What this reading assumes

Jurisdiction
Switzerland
Principal framework
The revised Federal Act on Data Protection (nFADP/revDSG), SR 235.1, in force since 1 September 2023, with the Data Protection Ordinance and the Ordinance on Data Protection Certification. The AI-relevant provisions are article 21 on automated individual decisions — a duty to inform, a right to state a point of view and a right to review by a natural person on request — article 22 on impact assessments, article 23 on prior consultation of the Commissioner where residual risk stays high, articles 16 and 17 on cross-border disclosure, article 24 on breach notification, article 9 on processors and article 7 on data protection by design. Sanctions are criminal fines of up to CHF 250,000 imposed on responsible private individuals, not administrative fines on companies, which changes who in an organisation has to have read the assessment.
Delivery assessed
Self-hosted
Data leaves the network
no
Vendor home jurisdiction
United States
Verified vendor positions
none — every vendor position below is a question, not an assurance
Rules evaluated
36
Rules fired
9

Assumptions about use

  • An internal deployment used by employees, not a public-facing product.
  • A person reads the output before acting on it — but that is not recorded, so the engine reports it as a gap rather than assuming it.
  • No significant automated decision is taken about a person by the system alone.

02Issues to work through

9 · 0 anchored

Cross Cutting

OUR RECOMMENDATIONSeverity HIGHsecurity-baseline

Self-hosting moves the security obligation to you

RECOMMENDATION

Keeping data on your own hardware answers the transfer question and creates an operations question. Patching, backups, key management, monitoring and incident response are now yours, and an unpatched inference server on the office network is a worse outcome than a well-run vendor.

Required checks
  • Name the person responsible for patching each component, and the cadence.
  • Confirm backups exist, are encrypted, and have been restored at least once.
  • Confirm there is an incident response path that includes this system.
Technical controls
  • Encrypt at rest and in transit, including between the application and the inference server.
  • Centralise authentication through the existing identity provider rather than local accounts.
  • Keep an audit log of who queried what, and protect it from the people it records.
  • Subscribe to security advisories for each component and track upgrade lag.
OUR RECOMMENDATIONSeverity MEDIUMauditability-practice

Being able to reconstruct a decision months later

RECOMMENDATION

The question that arrives after a complaint is what the system was shown and what it produced on a particular day. Models change, prompts change, and indexes are rebuilt, so the answer has to be recorded at the time. Without it, the only available response is that the output cannot be reproduced.

Required checks
  • Decide what is recorded per interaction: model and version, prompt template version, retrieved document ids, output, reviewer and outcome.
  • Set how long those records are kept, balanced against the retention duties that also apply to them.
Vendor questions
  • Does the vendor pin model versions, and how much notice is given before a model is retired or changed?
Technical controls
  • Version prompt templates in source control and log the version used.
  • Log the model identifier and version returned by the provider, not the one you requested.
OUR RECOMMENDATIONSeverity MEDIUMconfidentiality-duties

Confidentiality duties bind independently of data protection law

RECOMMENDATION

Material can be entirely free of personal data and still be the material a contract stops you disclosing. Client retainers, non-disclosure agreements, supplier contracts and common-law duties are the usual sources, and several of them require consent before a third party processes the material at all — which a model API call is.

Required checks
  • Review the confidentiality clauses in the contracts covering the material going into the system.
  • Identify any contract requiring notice or consent before a subcontractor processes the material.
  • Decide whether the deployment needs a confidentiality carve-out negotiated into new contracts.
Vendor questions
  • Will the vendor accept a confidentiality undertaking beyond its standard terms?
  • Which staff at the vendor can access customer content, under what controls?
Technical controls
  • Segregate the most sensitive corpora into an index that the general assistant cannot reach.

Human review required — take this to your counsel

OUR RECOMMENDATIONSeverity MEDIUMhuman-oversight-practice

We were not told whether a person reviews the output

RECOMMENDATION

Where output influences a decision about a person, the reviewer has to be able to disagree with it. That needs three things a rubber-stamp review lacks: enough information to judge, enough time to judge, and an override that is used often enough to be real. Design it before the volume makes it impossible.

Required checks
  • Name the role that reviews the output and what they see when they do.
  • Decide what evidence is retained about each review, so the practice can be shown to exist.
  • Set a threshold below which the system must not act without review.
Vendor questions
  • Does the product expose the retrieved context and the confidence behind a suggestion, or only the answer?
Technical controls
  • Show the reviewer the retrieved sources next to the suggestion, not the suggestion alone.
  • Record the reviewer’s decision, including overrides, as part of the audit trail.

Human review required — take this to your counsel

OUR RECOMMENDATIONSeverity MEDIUMlogging-practice

An AI deployment creates new copies of the data

RECOMMENDATION

Vector indexes, prompt logs, completion caches, evaluation datasets, fine-tuning checkpoints and backups are all copies of the source material in places the existing retention schedule does not mention. Deletion requests are the moment this is discovered, because deleting the source document does not delete its embedding.

Required checks
  • List every store the deployment creates and add each to the retention schedule.
  • Establish how a deletion request propagates to the index, the caches and the logs.
  • Establish how long backups keep material that has been deleted from the live system.
Vendor questions
  • What does the vendor retain, where, and for how long after we delete our copy?
Technical controls
  • Store the source document id with every embedding so deletion can cascade.
  • Set time-to-live on prompt and completion logs rather than relying on manual cleanup.
OUR RECOMMENDATIONSeverity MEDIUMmodel-access-control

Who can reach the model, the index and the weights

RECOMMENDATION

A self-hosted stack has three access surfaces that are easy to leave open: the inference endpoint, the vector index, and the weights on disk. Retrieval also carries an authorisation problem an ordinary application does not have — the index must not return a document to someone who could not open it in the source system.

Required checks
  • Confirm the inference endpoint is not reachable from outside the network and requires authentication.
  • Confirm retrieval filters by the requesting user’s permissions, not only by relevance.
  • Confirm who can read the model files and the index volume at the operating-system level.
Technical controls
  • Bind the inference server to a private interface and put an authenticating proxy in front of it.
  • Carry document-level access control into the index and enforce it at query time.
  • Encrypt the volume holding the weights and the index, and restrict it to the service account.
  • Rotate API keys and keep them out of client-side code and container images.
OUR RECOMMENDATIONSeverity MEDIUMprompt-handling

What ends up in a prompt, and where it goes next

RECOMMENDATION

Even with inference inside the network, prompts and retrieved context accumulate in logs, traces and caches, and system prompts can often be extracted from the output. The leak path is internal rather than external, but it is still a copy of the source material in a new place.

Required checks
  • Write down which categories of information may be entered into a prompt, and tell people.
  • Establish what the system prompt contains and whether disclosing it would matter.
  • Establish which shadow tools staff are already using; the policy has to name the permitted ones.
Vendor questions
  • Are prompts and completions retained, for how long, and can retention be set to zero?
  • Are prompts used for abuse monitoring, and if so who can read them and for how long?
Technical controls
  • Redact or block high-risk patterns before the prompt leaves the application.
  • Keep prompt and completion logs out of general-purpose observability tools.
  • Set an explicit retention period on prompt logs and enforce it.

Switzerland

RECOMMENDED PRACTICESeverity MEDIUMch-fdpic-ai-statement

No AI statute is not the same as no duties

ASSESSMENT

The Commissioner’s position is that the FADP is drafted in a technology-neutral manner and therefore applies to AI-supported data processing, and that the people affected should be granted the greatest possible protection. It expects transparency about the purpose, the functionality and the data sources of an AI system, and that a person knows whether they are dealing with a machine.

Required checks
  • Do not tell a stakeholder that Switzerland has no rules on AI — say it has no AI statute and describe what does apply.
  • Write down the purpose, the functionality and the data sources in terms a person outside the project could follow.
  • Make it evident to a user when they are interacting with a machine rather than a person.
Vendor questions
  • What can you tell us about the data your model was trained on, in enough detail for us to describe it?
LEGAL REQUIREMENTSeverity LOWch-ai-implementing-bill

The Swiss AI bill is still in preparation, and its shape is not knowable yet

ASSESSMENT

The federal communications office states that in Switzerland there is currently no specific legislation on AI. The Federal Council has decided to ratify the Council of Europe AI Convention and to legislate sector-specifically as far as possible, with a consultation draft due by the end of 2026, and no draft had been opened at the review date. Planning against a specific Swiss AI obligation is planning against nothing.

Required checks
  • Do not build to a hypothetical Swiss AI regime; build to the FADP and the sectoral rules that exist.
  • Watch for the consultation opening, because sector-specific legislation means the relevant rule may arrive in your own sector’s statute rather than in an AI act.

Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.


03What this reading does not know

3
  • Whether any of the data falls into a special or sensitive category.
  • Whether any material is covered by legal professional privilege.
  • Whether a person reviews the output before it is acted on.

04Instruments these issues point at

3

05Vendor documents being watched

4

06Ask about your own deployment

This page reads the rules against a generic organisation. Your size, industry, data and existing contracts change which of these issues matter and which fall away.

  1. 01What do we need to check before using NVIDIA NIM in Switzerland?