Enterprise data, KVKK & security: manage risks early
A frequent risk in AI projects is processing personal or sensitive business data beyond its purpose. This guide outlines a concise roadmap for enterprise data and security within KVKK and data-minimisation principles.
Section 1
Why minimisation comes first
Model and feature choices must shrink and bound the dataset to its purpose. Otherwise compliance and security surface area grow; with LLMs that risk multiplies.
Section 2
CRM and the “golden record”
Reporting and automation quality usually depend on consistent CRM/ERP data. Deduping, identity resolution and report definitions are as critical as integration.
Section 3
Web applications and security
Authentication, session management and data classification are architectural choices in enterprise web apps — they are not “fixed” by a penetration test alone.
Related expert articles
- Enterprise AI, KVKK & data minimisation
- Web analytics, cookies & consent — KVKK context
- CRM data quality: golden record & consistent reporting
- AI-assisted web application: architecture & security
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Frequently asked questions
What matters under KVKK for enterprise AI?
Purpose limitation, legal basis, data inventory, subprocessors, and technical/organisational measures; automated decision-making and profiling may need extra assessment.
Which data should not be sent to an LLM?
Raw identity, health, finance or trade-secret data should usually be masked or not sent; policy and technical controls must be defined together.
Who owns cookies and analytics?
Legal framing is owned by legal/compliance; technical implementation (CMP, tag firing, log retention) should run on one inventory aligned with product and engineering.