AI is a toolbox I use openly, within clear bounds.
My engagements involve working with your data. I also use AI tools when they improve the quality, speed or reliability of my work.
Document
AI & Data Charter
version
1.01
This content has been translated using AI and meticulously reviewed by me. In case of discrepancy, the French text shall prevail.
EFFECTIVE FROM
September 2026
author
Pierre-Yves Niederhauser
This charter sets out how I use these tools effectively, rationally, securely and ethically, how I protect your data, and where I place the safeguards. It forms an integral part of my terms of engagement.
Designed to comply with the Swiss Federal Act on Data Protection (FADP), this charter proactively incorporates the transparency and risk-management principles of the EU AI Act, ensuring compatibility for clients operating in Europe.
I.
DATA
Four levels under Swiss law.
Every document I create, collect, or receive is assigned a classification level.
– definitions & examples
Information that is publicly available, or intended to become so.
Website, annual report, commercial brochure, press release, public job posting, widely distributed product documentation.
Information circulated within an organization, with no intention of being shared externally.
Internal procedures, organization charts, meeting minutes, application documentation, tracking tables, unpublished operational information.
Information whose disclosure is restricted to authorised persons.
Customer data, employee data, budgets, contracts, margins, incidents, unannounced projects, non-public decisions of management or the board, application maps, supplier files.
Information whose disclosure could cause serious harm.
Trade secrets, credentials, access keys, cryptographic secrets, unpatched vulnerabilities, inside information, critical security information.
– application rules
If your organisation uses a different scale, or none at all, I set the classification at the start of the engagement and submit it for your approval.
An uplift rule applies: a document classified ‘internal’ at your end is treated as ‘confidential’ at mine. Information that circulates freely within your organisation cannot circulate freely at a third party. The other levels remain unchanged.
I store your public, internal, and confidential documents exclusively on Swiss territory, either with providers subject solely to Swiss law, or on my own infrastructure with full-disk encryption.
– special provision
The ‘secret’ level entails substantial operational constraints: no document leaves your infrastructure, access-rights management remains your sole responsibility, and communications slow down considerably. Costs and timelines rise accordingly. As a matter of principle, this level is applied sparingly, only where the nature of the information requires it. In practice, information your organisation routinely shares by email does not qualify, whatever its subject.
Your secret documents therefore remain within your perimeter: they do not reach my infrastructure, even encrypted. You are responsible for providing me with whatever access mechanism you consider appropriate (physical or virtual workstation, secure remote access, or any other arrangement under your control). My notes, extracts and outputs relating to the matter remain within that same perimeter.
– data breach handling
In the event of a suspected breach involving data at any level other than ‘public’, unauthorised access, or any other security incident, I undertake to notify you immediately, within 24 hours at the latest. I take initial containment measures right away to limit the impact, and cooperate fully with you on incident analysis and on any required notifications to the authorities (Federal Data Protection and Information Commissioner, FDPIC).
II.
PrincipLe
Your data is not fed to every AI under the sun.
I do not use an AI tool simply because it is convenient, fast, or fashionable.
– The Finding
The major AI services — OpenAI, Anthropic, Google, and their Chinese equivalents — rely on a unilateral contractual commitment. No audit is possible, there is no leverage under Swiss law, and the word of an American or Chinese company does not stand up to a political requisition.
rEcentLY
On June 6, 2025, the United States Supreme Court authorized the DOGE team — the Department of Government Efficiency, launched by Elon Musk — to access the personal files of the Social Security Administration: Social Security numbers, banking data, and medical records of millions of Americans1. The decision overrode the protections of the 1974 Privacy Act that lower courts had upheld.
If fifty-year-old access protections can give way within a few months, a private contractual commitment carries no more weight.
– MY RULE
No client data crosses that boundary. The promotion rule set out in Chapter I ( what is “internal” on your side becomes “confidential” on mine ) takes care of this automatically.
- An international platform only receives from me content that is already public, or materials that contain no client data.
- Under no circumstances do I allow your data to be used to train a model.
- I strictly comply with the A/B/C/D processing rules described in Chapter III.
- I keep each tool within the scope defined for the engagement.
Control over data flows takes precedence over model power. What must remain on your side remains on your side
Note
-
1.Supreme Court of the United States, SSA v. AFSCME, No. 24A1063, June 6, 2025 — unsigned emergency order staying the preliminary injunction issued by the U.S. District Court for the District of Maryland, by a 6–3 vote. The details of the data concerned and the challenge under the Privacy Act of 1974 are set out in the dissenting opinion of Justice Jackson, joined by Justice Sotomayor.
See also Le Temps, La Cour suprême américaine autorise Doge à accéder aux données de sécurité sociale des Américains (The U.S. Supreme Court authorizes DOGE to access Americans’ Social Security data), June 7, 2025.
III.
Models
The more protection your data needs,
the closer the AI stays to you.
I distinguish four AI processing models.
A
International AI platforms
Market-leading AI services, often referred to as “frontier models”: OpenAI, Anthropic, Google, Microsoft, or equivalent providers.
Intended Use
Public data or ITpulse internal data
B
Swiss AI service
Provider subject to Swiss law, data centre in Switzerland, your data is never used to train an AI model and is not retained beyond processing.
Intended Use
Client internal or confidential data
C
AI under my control
Open model running on a private server in Switzerland managed by a Swiss entity, or on my own local physical infrastructure with encrypted storage.
Intended Use
Confidential data with no third-party AI
D
AI in your own environment
Model running in your own environment, without transfer to an external AI service.
Intended Use
Secret data or specific constraints
Your classification describes the status of the data within your organization. My processing rules describe what I am authorized to do with it as a third party.
By default, I raise the level of protection when data leaves your perimeter: data that normally circulates within your organization must not circulate freely within mine.
| client Classification Level | ITpulse Processing level | Permitted AI Models |
|---|---|---|
| Public | Public | A · B · C · D |
| Internal | Confidential | B · C · D |
| Confidential | Confidential | B · C · D |
| Secret | Secret | D uniquement |
Default permitted models. Depending on the nature of the engagement, your contractual commitments, the presence of sensitive personal data, or the context of the matter, the most restrictive option may apply.
IV.
Accountability
AI can assist me; I stand behind what I deliver.
AI may help me analyze, rephrase, structure, compare, synthesize, or produce variants. It never decides what is right, acceptable, or deliverable.
- I review, correct, and take responsibility for the deliverables I provide to you. The use of an AI tool does not reduce that responsibility.
- At the start of the engagement, I inform you of the intended AI uses and the categories of data concerned; I notify you of any changes along the way.
- I maintain a register of the tools used, the tasks concerned, and the categories of data processed, and I include it as an appendix to each deliverable.
- When a deliverable includes AI-generated output that has not been reworked, such as a machine translation or a raw summary, I identify it as such.
V.
Continuity
I build on the method, never on your data.
An engagement produces two things: a method that can be reused elsewhere, and content that must not be.
– The Principle
I may reuse an approach, an analysis structure, a presentation model, a visual identity, or generation scripts. I do not reuse one client’s content to serve another.
My materials are designed to separate form from content: templates, visual components, and generation scripts on one side; engagement-specific data on the other. When I produce a presentation, client data is injected at document generation time, from sources dedicated to the engagement.
This architecture avoids copied-and-pasted presentations, remnants from old files, unintended traces of other clients, and questionable reuse of materials.
– AFTER THE ENGAGEMENT
At the end of the engagement, the working sources you have provided to me ( files, exports, temporary access, interim documents ) are deleted, unless you request otherwise or a specific need has been agreed.
Final deliverables classified as public, internal, or confidential are retained for ten years on my infrastructure when they have evidential or accounting value. For the others, this reference period applies by default to ensure traceability, unless a shorter period is agreed in accordance with the minimization principle. These documents remain subject to their initial confidentiality level. Secret-level deliverables are an exception: they remain entirely within your perimeter, in accordance with Section I.
LEGAL BASIS
In Switzerland, Article 958f of the Code of Obligations sets a ten-year retention period for accounting records and business correspondence. The period runs from the end of the financial year. The retention period for deliverables is aligned with this benchmark.
This retention is strictly controlled:
- I am the only person with access to it.
- I use it only in the context of the engagements you entrust to me.
- I do not reuse it for other clients.
- I do not export it to any AI tool.
- I delete it at your request, unless a contrary legal obligation applies.
Capitalizing on experience does not mean accumulating material for unrestricted reuse. The method improves; your data remains yours.
VI.
FAQ
The questions I am asked.
What are the four confidentiality levels you apply?
Public. Information already accessible to everyone, or intended to be.
Website, annual report, commercial brochure, press release, public job posting, widely distributed product documentation.
Internal. Information that circulates within your organization, but is not intended to leave it.
Internal procedures, organization charts, meeting minutes, application documentation, tracking tables, non-public operational information. At ITpulse, data classified as internal by the client is treated as confidential.
Confidential. Information restricted to authorized persons.
Client data, employee data, budgets, contracts, margins, incidents, unannounced projects, application maps, supplier files, trade secrets. These data exclude international AI platforms.
Secret. Information whose disclosure could have a major impact and which, as a matter of principle, should not leave your environment.
Active M&A file, strategic litigation, information subject to professional secrecy, credentials, access keys, non-public board decisions, critical security information. These data are processed only under Model D. Where needed, I can assist you in setting up the required infrastructure.
What is personal data?
Personal data is information relating to an identified or identifiable natural person. This may be direct ( such as a name, a named email address, or a telephone number ) or indirect, for example a role, an activity history, an IP address, or a combination of elements that makes it possible to recognize a person. The Swiss FADP is intended to protect natural persons whose personal data is processed.
Personal data is not a separate confidentiality level. It is a cross-cutting dimension: it may appear in public, internal, confidential, or secret data.
What is sensitive personal data?
Sensitive personal data is personal data that relates to particularly protected aspects: health, the intimate sphere, racial or ethnic origin, religious, philosophical, political or trade-union opinions or activities, genetic data, identifying biometric data, criminal and administrative proceedings or sanctions, and social assistance measures. These categories follow the logic of Article 5 FADP.
Medical certificate, sick leave certificate, disciplinary HR file, debt-enforcement register extract, biometric information, trade-union membership, information relating to social assistance.
The presence of sensitive personal data may require a more restrictive processing model than the one determined solely by the business confidentiality level.
Can data be both public and personal?
Yes. A biography published on your website, a team photo, or a named professional email address may be public while still being personal data. The fact that it is public makes certain uses easier, but does not remove the obligations related to data protection.
Can data be confidential without being personal?
Yes. A commercial margin, an application architecture, a migration strategy, a supplier contract, or a purchase price may be highly confidential without directly relating to a natural person. Confidentiality then protects your organization, your business, your partners, or your competitive position.
What happens if a document combines several levels?
The strictest rule applies. If a document contains internal information, personal data, and secret elements at the same time, it is processed according to the highest level required.
In practice: what is useful is extracted or isolated before any AI processing.
Is “hosted in Switzerland” enough?
No. Hosting in Switzerland is an important criterion, but it is not sufficient on its own. One must also look at who operates the service, under which jurisdiction, with which subcontractors, what administrative access exists, what support arrangements apply, what retention is in place, and what contractual guarantees are provided.
Infrastructure located in Switzerland but controlled by an entity subject to extraterritorial obligations may not be suitable for certain confidentiality levels.
Can Microsoft, Google, AWS, or other major cloud providers be used?
Yes, but not for everything. They may be used under Model A, for public data. They are not my default choice for internal, confidential, or secret data, even when the hosting region is in Switzerland or Europe.
The question is not only “where is the data?” It is also: who operates, who administers, who provides support, who retains it, who can be compelled to access it, and who holds the keys?
What do you retain after an engagement?
I distinguish between source materials and deliverables.
Working sources ( files provided, exports, interim documents, temporary access ) are deleted at the close of the engagement, unless you request otherwise or a specific need has been agreed.
Final deliverables may be retained for ten years, within a limited framework: access restricted to me alone, use connected to our engagements, no reuse for other clients, no feeding into AI tools, and deletion at your request unless a contrary legal obligation applies.
Why retain the deliverables?
Because a deliverable documents the work performed. Retaining it makes it possible to pick up the thread in a later engagement, avoid asking you again for what has already been established, and remain accountable for what was delivered.
This does not authorise me to reuse your data for other clients.
What do we do in case of doubt?
In case of doubt, I choose the higher level of protection. Caution takes precedence over ease of use. AI is used only if the processing model is compatible with the data actually being handled.
LET’s TALK
30 minutes, a good conversation,
an independent view to take away.
PHONE
+41 (0)79 733 03 94
BASed in
Château-d’Oex & Fribourg, Switzerland