AI is a powerful new area where personal data is processed. Training a model, feeding customer data to a chatbot, or building automated decision systems all fall within KVKK. The good news: existing KVKK principles provide a solid framework for governing AI. A new technology does not require a new body of law; it requires applying the same principles to a new context.
Below you will find the personal-data risks of using AI, how to map those risks to KVKK controls, and an end-to-end, auditable governance roadmap.
The Personal-Data Risks of AI
- Training data: Training a model with personal data is a processing activity; it needs a legal basis and purpose. Because training data can become "embedded" in the model, how you will honour erasure and rectification rights should be considered from the outset.
- Automated decisions and profiling: Producing automated outcomes about a person (credit, hiring, pricing) triggers the right to object. A person must be able to object to decisions made solely by automated processing that produce adverse effects on them.
- Transparency: "Black box" decisions can conflict with the principles of notice and accountability. You should be able to explain, at least in understandable terms, "how" a decision was reached.
- Data minimisation: The "more data the better" approach violates purpose limitation. Collecting data beyond the purpose is indefensible, even for model performance.
- Vendor/AI provider: Using a third-party AI service means a data processor and a probable cross-border transfer.
- Input (prompt) leakage: Employees pasting personal or confidential data into an external AI tool can unwittingly create a transfer and a disclosure.
From Risk to Control: A Mapping
| Risk | KVKK control |
|---|---|
| Automated decision | Notice + right to object (Art. 11) + human oversight |
| Transparency | Plain notice + explainability of decision logic |
| AI provider | Processor contract + transfer safeguard |
| Input leakage | Usage policy + employee training + data masking |
| High risk | Data protection impact assessment (DPIA) |
The Roadmap
- Build an AI inventory: Which systems run on which personal data? List unofficial tools too, including shadow AI use.
- Determine legal bases: A solid ground for each AI processing. Choose consciously between explicit consent and legitimate interest.
- Assess high-risk uses: Document risks and measures with a DPIA.
- Add human oversight to automated decisions: An objection and review mechanism. Human oversight must be meaningful enough to actually change the decision, not a rubber stamp.
- Ensure transparency: Explain AI use and its logic in the privacy notice.
- Vet providers: Contracts, security and transfer safeguards. Clarify whether the provider uses your data to train its own models.
- Monitor and update: Models and law change; the process must stay alive.
Global Context: The EU AI Act
For companies serving the EU, the EU AI Act brings additional risk-based obligations. The regulation broadly sorts AI systems into four risk tiers:
- Unacceptable risk: Prohibited practices (for example, manipulative or social-scoring systems).
- High risk: Systems used in areas such as hiring, credit and education; subject to strict obligations.
- Limited risk: Systems with transparency duties (for example, being told you are talking to a chatbot).
- Minimal risk: Most applications, which can be used freely.
Handling the KVKK + GDPR + AI Act trio in a single governance framework avoids double/triple work. A DPIA often overlaps significantly with the risk assessment the AI Act requires.
Example Scenario: AI Screening in Hiring
A company uses an AI tool that automatically screens applications. Under KVKK:
- Candidates must be informed that the process is AI-assisted,
- A right to object (Art. 11) to fully automated adverse outcomes must be granted,
- Meaningful human oversight must be added to the process,
- A processor contract must be signed with the tool's provider,
- If the provider is foreign-based, a transfer safeguard must be in place.
Because this use is high-risk, a DPIA is also expected. It is also important, both ethically and legally, to check that the model has not learned discriminatory bias from historical data.
Example Scenario: A Customer-Support Chatbot
An e-commerce company deploys an external AI chatbot that answers customer questions. The chatbot has access to order history and contact details. Here the priorities are: telling the person they are talking to an AI (transparency), processing data only as far as the purpose requires (minimisation), a processor contract with the provider, and a cross-border transfer safeguard. Masking and a retention period should be defined so the free text entered into the chatbot does not collect more data than necessary.
Frequently Asked Questions
Do I need a separate law to use AI?
No. Existing KVKK principles apply to AI. For the EU market, the EU AI Act may additionally apply; but most core obligations already overlap with KVKK.
Is using a tool like ChatGPT a transfer?
Sending personal data to a foreign-based AI service is generally a cross-border transfer; an appropriate safeguard is required. Govern employees' entry of confidential data into such tools with a usage policy.
Are automated decisions entirely prohibited?
Not prohibited; but they must be balanced with transparency, the right to object and human oversight. These balances matter even more for decisions that are adverse to the person and based solely on automated processing.
When should I run a DPIA?
When high-risk processing is involved, and preferably before the system goes live. A DPIA is a proactive tool that lets you spot risk early and take measures; it is not a formality done after the fact.
This content is for general information only and does not constitute legal advice. With JUS. you can inventory all your processing activities, including AI, and bring them under governance — request a demo.