Healthcare
An assistant that keeps patient data inside
The model runs inside the hospital network, identifiers are masked, and only authorised staff can query the relevant records.
Special-category personal data never leaves the institution.
Overview
Why is data security a separate topic in AI?
In classic software, data comes in, rules run, a result comes out. In AI, text is sent to a model — and where that model runs, whether requests are logged and whether your data is used for training are each separate decisions. We build the architecture around those questions from day one: which data never reaches the model, which gets masked, where the model is hosted and who can access what. For KVKK, what matters is that these decisions were made up front rather than patched in later.
On-premise LLM deployment on your servers
Masking / anonymisation of personal data
Role-based access and authorisation
Audit logs of every request and response
Defined data retention and deletion periods
Technical documentation for privacy notices and registries
How it works
How we proceed
Scope
What the solution includes
On-premise LLM deployment
Open-source models run on your own servers, so no request ever leaves your network.
Data masking
Fields such as national ID, phone and IBAN are masked or pseudonymised before reaching the model.
Role-based access
Which role reaches which dataset and which assistant is managed centrally.
Audit logging
Who asked what, when, and which document they reached — all recorded and traceable.
Retention & deletion
Retention periods are defined for conversation and document records, with automatic deletion at expiry.
Compliance documentation
The technical and administrative measures taken are documented in a form your legal team can use.
Industry Use Cases
How is it used, and in which industries?
In data-sensitive industries, this layer is usually what makes AI possible at all. Examples:
Finance & Insurance
Confidentiality in customer-file analysis
While loan and claim files are analysed, fields such as IBAN and national ID are pseudonymised, with a full audit trail kept.
Internal audit and regulator questions can be answered with evidence.
Public Sector & Municipalities
Secure processing of citizen applications
Applications are classified and routed on in-house infrastructure, so personal data is never sent to a third-party service.
Service speeds up while data responsibility stays with the institution.
Human Resources
Protecting employee and candidate data
When CVs and personnel files are processed, fields prone to bias are filtered out and access is limited to HR roles.
Candidate data is protected and processed only as far as necessary.
Legal & Consulting
Confidentiality in client files
File and contract analysis runs in an isolated environment, and content is never used for a model provider’s training.
Professional secrecy is backed by technical guarantees, not just policy.
Education
Assistants working with student data
Student performance and attendance data is processed in-house, and the parent-facing assistant sees only their own student’s data.
Minors’ data is protected in line with regulation.
Typical Gains
0
Requests leaving the network on-premise
%100
Access and queries recorded
Rol bazlı
Access control for data and assistants
2–4 hafta
Time to design the security architecture
Figures show typical ranges from comparable projects; actual results depend on your data quality and processes.
Integration
It works alongside your existing systems
Kurum içi sunucu / GPUActive Directory & SSOTürkiye’de veri merkeziVPN & özel ağSIEM & log yönetimiYedekleme sistemleriRol yönetimi (RBAC)Şifreleme (at rest / in transit)
FAQ
Frequently Asked Questions
Let’s settle the security architecture before the project starts
When we settle where each piece of data goes up front, AI projects meet no compliance surprises later.