Data classification
Identify which information may be used, which requires additional safeguards and which remains out of scope.
Private AI
Private AI with documented data boundaries
Design AI around the confidentiality, access and governance requirements of your organisation — not around a product label.
“Private” is not meaningful until the data path is explained.
A private interface can still send content to an external model, retain prompts or expose information through broad permissions. The architecture must state where data travels, who can access it, what a provider may retain and how the organisation can audit or change the design.
Private AI
Controls that make privacy inspectable
The controls below are design commitments. The exact technical and commercial scope is confirmed in the written proposal.
Architecture choice
Compare hosted, tenant-controlled and private deployment options against the actual risk and operating capacity.
Location and retention
Document where prompts, retrieved content, outputs and logs are processed and how long each is retained.
Provider settings
Record whether data may be used for training, which contractual terms apply and who owns configuration and outputs.
Access control
Use minimum permissions and align identity, roles, source-level access and administrative change control.
Traceability
Define useful logs, review responsibilities, incident handling and the evidence required for audits.
A clear next step
Architecture before implementation
Each stage has an owner, an output and an approval point so that progress remains understandable.
Classify
Identify the workflow, information sensitivity, users, systems and regulatory or contractual constraints.
Decide
Compare architecture options and document the model, provider, data path, permissions, retention and ownership.
Verify
Test access boundaries, logging, human controls and rollback before approving wider use.
Illustrative deliverable — not a client result
A decision you can explain
- Data-flow and trust-boundary diagram
- Model, provider, location and retention decision
- Permission matrix and human approval points
- Logging, incident, rollback and ownership responsibilities
Direct answers
Questions Swiss organisations ask before changing IT or introducing AI
Clear answers help you compare providers, question assumptions and make a more informed decision.
How do you protect confidential information?
The design begins with minimum data access, explicit permissions and a documented choice of model, vendor, data location, retention, logging and training settings. Sensitive actions can require human approval, and every pilot includes a rollback path.
What is practical AI?
Practical AI starts with a specific business workflow, a measurable outcome and controlled access to data. We design a limited pilot, document the controls and only recommend expansion when the result is useful and supportable.
Can AI integrate with our current tools without replacing them?
Often, yes. An assistant or automation can connect to approved systems and fit into an existing workflow. We first check the available interfaces, permissions, data quality and fallback process before proposing an integration.
Start with the problem, not a technology purchase.
Book a 30-minute assessmentBook a 30-minute assessment
Book a 30-minute practical assessment
Tell us about one IT or workflow problem. You will leave with an initial view of the risks, feasible options and the most sensible next step. No technical preparation is required. Fields marked with an asterisk are required.
What happens next
- Describe one problem and suggest a convenient time.
- We use the context to focus the 30-minute conversation on IT, AI or both.
- The conversation ends with an initial view of risks, feasible options and the smallest sensible next step.
+41 22 552 08 10
info@dspsa.ch