KVKK for AI Agents · concept · an Avalanche AI example wiki verified 2026-07-08 · live

Deployment Patterns — KVKK-Aware Agent Architecture

KVKK compliance for agents is mostly decided by architecture, not by policy documents. The Kurul's 2021 AI recommendations already point the same direction as good engineering: privacy by design, data minimization, purpose limitation, and human oversight.

Pattern 1 — Minimize what enters the context window

The agent's context is a processing event. The corporate-brain approach helps directly: agents read compiled knowledge pages (usually free of personal data) instead of raw documents and mailboxes (full of it). Compiling knowledge once, with personal data stripped or aggregated at compile time, beats filtering ad hoc on every query.

Pattern 2 — Decide where transcripts live before go-live

Agent conversations accumulate personal data fast (names, complaints, salary questions). Decide retention, storage location, and access before launch; apply the same purpose-limitation analysis as any other record system. "We keep everything forever for fine-tuning" fails Art. 4 on its face.

Pattern 3 — Assign the roles in writing

Customer = veri sorumlusu; LLM provider, host, and integrator = veri işleyen under processing agreements. For foreign LLM providers, pair the processing agreement with the Art. 9 mechanism — see Cross-Border Transfers After the 2024 Reform.

Pattern 4 — Choose LLM placement per data class, not per fashion

A hybrid split — resident models for the sensitive slice, cloud for the rest — is often the honest optimum.

Pattern 5 — Keep a decision trail

For each agent use case, record: purpose, lawful basis, data categories, transfer mechanism, retention. This is exactly the kind of knowledge a corporate brain page contract handles well — compliance documentation that stays verified instead of rotting in a drive.

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Educational reference maintained by Avalanche AI — not legal advice.