Overview
AI offers transformative benefits for financial services and healthcare, but regulated sectors bring heightened legal and ethical duties. Counsel and compliance officers must reconcile innovation with data protection, patient and consumer safety, and sector-specific supervisory expectations. This article outlines a practical governance framework for organisations in Türkiye considering or operating AI in these regulated spaces.
Regulatory landscape and core obligations
Multiple obligations typically intersect when AI is used in finance or healthcare:
- Data protection duties under the Turkish Personal Data Protection Law (KVKK), including lawful basis, purpose limitation and security measures;
- Sectoral regulation administered by banking and health supervisory authorities that may impose licensing, reporting and consumer-protection obligations;
- Consumer protection and liability doctrines addressing defective products or negligent services.
Organisations must therefore adopt a compliance stance that addresses both cross-cutting privacy and sector-specific safety concerns.
AI-specific governance elements
Design a governance program around four pillars:
- Risk classification and impact assessment – perform AI-specific impact assessments that address privacy, safety, fairness, and systemic risk. Document rationale for classification (low, medium, high risk) and mitigation measures.
- Data governance – ensure lawful collection and processing under KVKK; implement robust anonymisation or pseudonymisation; maintain provenance records for training data.
- Operational controls – testing, validation, continuous monitoring and human oversight for critical decision points (credit decisions, diagnoses, treatment recommendations).
- Vendor and third-party risk – contractually require transparency, model explainability, breach notification, audit rights and service levels from AI providers.
Practical compliance measures
Counsel should advise clients to implement the following concrete measures:
- Maintain a documented AI register describing purpose, data flows, processing steps and high-level model architecture for each deployed system.
- Require pre-deployment validation and user-acceptance testing, with test cases representative of real-world inputs and edge cases.
- Adopt continuous monitoring metrics: accuracy drift, false positive/negative rates, disparate impact indicators and system uptime.
- Ensure clear human-in-the-loop protocols and escalation thresholds for automated decisions with significant legal or health consequences.
- Integrate incident response plans aligned to regulatory notification timelines under KVKK and sectoral rules.
Vendor contracts: key clauses
When engaging third-party AI vendors, prioritise contractual clarity on:
- Data ownership and permitted uses of training and derivative datasets;
- Security standards, encryption and breach notification obligations;
- Audit and inspection rights, and the vendor’s obligations to support regulatory inquiries;
- Model update/change control, rollback and revalidation procedures;
- Indemnities for data breaches, IP infringement, and regulatory fines to the extent permitted by law.
Sector-specific considerations
Finance: credit-scoring models and algorithmic trading present clear supervisory interest. Banks and fintechs should document explainability, consumer disclosure, and fairness testing. Maintain records to support regulatory examinations and consumer dispute resolution.
Healthcare: AI used in diagnostics or treatment advice must be integrated with clinical governance. Patient safety, informed consent and medical-device classification may apply. Engage clinicians in validation and ensure audit trails for clinical decisions supported by AI.
Implementation roadmap
- Conduct a portfolio review of AI systems and prioritise high-risk models for immediate assessment.
- Establish a cross-functional AI governance committee including legal, compliance, technical and business leads.
- Roll out standard contractual templates and an AI vendor onboarding checklist.
- Train staff on human oversight duties and incident reporting procedures.
- Plan periodic audits and update risk assessments with new operational data.
Conclusion
Deploying AI in Türkiye’s financial and healthcare sectors requires disciplined governance that aligns technical controls with legal duties under KVKK and sectoral rules. By institutionalising impact assessments, data governance and vendor controls, organisations can reduce legal risk while enabling innovation. Av. Burak Şahin and Şahin Hukuk advise on tailored compliance programs and contractual protections for regulated AI use-cases.
This article is provided for general legal information and analytical purposes. Specific matters should be assessed under the current law and their own facts.
