AI Governance: A Compliance Roadmap for Turkish Enterprises
How should Turkish enterprises build AI governance after the AI Action Plan, the parliamentary report, and the EU AI Act? A practical roadmap.
Short answer: AI governance in Türkiye is no longer a legal department topic — it has become an operational necessity. The Türkiye AI Action Plan (2026-2030), published in the Official Gazette on August 18, 2026, the Turkish Parliament's AI Research Commission report finalized in March 2026, and the EU AI Act transparency obligations that took effect on August 2, 2026 all point in the same direction: build your AI inventory, data processes, and model audit mechanisms now, without waiting for a law to pass. Organizations that do will be discussing competitive advantage when regulation arrives — not compliance costs.
August 2026: Regulatory pressure from three directions
For a long time, the enterprise AI conversation in Türkiye revolved around "which model should we use." As of August 2026, the question has changed. The AI Action Plan, put into effect by Presidential Circular No. 2026/9, formalized the country's roadmap through 2030 and assigned responsibilities to every public institution.
The legislative side is moving too. The final report of the Parliament's AI Research Commission, published in March 2026, proposes a framework law centered on the Council of Europe Framework Convention on AI and the creation of a "Türkiye AI Authority." Three separate AI bills are already pending in Parliament; the risk-based one foresees administrative fines of up to 7% of annual turnover.
The third front is Brussels. Every Turkish company selling products or services into the EU market is subject to the EU AI Act obligations that started applying on August 2, 2026. Pressure from these three sources is not a set of news items to track separately — it is a single picture that must be read together.
What does the Action Plan mean for companies?
The plan's publicly reported targets are ambitious: AI training for 5 million citizens within two years, 10,000 advanced specialists, at least $10 billion in cloud and AI infrastructure investment, and 1 gigawatt of installed data center capacity by 2030. Opening at least 2,000 public datasets in healthcare, agriculture, defense, and e-commerce is also on the list. The task of developing Turkish large language models has been assigned to TÜBİTAK, the T3 Foundation, Baykar, and HAVELSAN.
The critical point: this is a circular, not a law. Yet as Vircon Legal's analysis rightly notes, the plan will produce binding consequences through three channels: public procurement, sector-specific regulation, and eventual legislation. Vendors selling high-impact AI systems to the public sector should expect requirements for detailed assessments, public model cards, and technical documentation files. The five regulatory sandboxes planned for finance, healthcare, energy, mobility, and telecommunications are both an opportunity and a readiness test for organizations in regulated industries.
Public institutions positioning themselves as the "first user and reference customer" for domestic solutions opens a new market door for technology vendors. The ticket through that door is a documented, auditable AI operation.
The reality on the ground: The 7.5% figure
The distance between the targets and the current state is the real story. According to the AI statistics TurkStat (TÜİK) published for the first time in 2025, 7.5% of enterprises in Türkiye use AI. That figure was 2.7% in 2021 — roughly a threefold increase in four years, but still a narrow base. By contrast, among large enterprises with 250 or more employees, nearly one in four now uses AI.
This gap tells us two things. First, for large organizations AI is past the experimentation phase; it now runs in production, inside real business processes. Second, low adoption among SMEs is precisely the gap the Action Plan's training and incentive targets are aimed at — meaning it is the segment where the next adoption wave will grow fastest. We covered the broader trajectory of enterprise AI in Türkiye in our enterprise AI in Türkiye analysis; this article focuses on the governance layer of that transformation.
EU AI Act: Postponed obligations, unpostponed transparency
On the EU side there is an update worth reading carefully. With the Digital Omnibus amendment finalized on June 29, 2026, the high-risk system obligations were postponed: Annex III areas (recruitment, credit scoring, education, and similar) moved to December 2, 2027, and Annex I regulated products to August 2, 2028. So the reading that "we gained two years for high-risk compliance" is partly correct.
But some things were not postponed. As of August 2, 2026, the Commission's supervision and enforcement powers over general-purpose AI (GPAI) providers are active, with fines reaching €15 million or 3% of global turnover. The Article 50 transparency obligations also apply from the same date: customer-facing chatbots must disclose that they are AI, and deepfakes and AI-generated content must be labeled. Companies that fine-tune a GPAI model on their own data or offer one under a white label should not assume they fall outside this scope.
The KVKK groundwork is already in place
Even without an AI law in Türkiye, there is no vacuum on the data side. The Guide on Generative AI and the Protection of Personal Data, published by the KVKK on November 24, 2025, gives organizations a framework they can apply today: clarifying data controller and processor roles, privacy notices that go beyond "we use AI" to specify the system, purpose, and data types involved, privacy by design, impact assessments, and red team testing.
The guide's most overlooked message concerns record-keeping: an organization that cannot document which data went into which model, and for what purpose, also cannot respond to data subject rights requests. That is proof that governance starts not with legal documents but with logs.
Governance is not a document — it is an operational discipline
From a managed services perspective, we see a recurring pattern: in most organizations AI governance starts as a policy PDF and stays there. Yet what will actually answer an audit, a customer question, or a regulator is not a document but working mechanisms — and nearly all of them are extensions of classic IT operations disciplines.
Let's make that concrete. An AI inventory is an extension of asset management: you should be able to answer, from a single source, which team uses which model, with which data, in which business process — and "shadow AI," the AI edition of shadow IT, only becomes visible this way. Monitoring model behavior is observability practice; prompt and output logging is log management; restricting access to models and data is identity and access management. For teams building their own AI platform on open-source models, we covered how these layers fit into the architecture in our enterprise open-source AI stack guide.
Organizations that can make this translation find that compliance stops being a separate project and becomes a layer on top of the existing operating model. For those that cannot, every new regulation means a crisis project started from scratch.
The next 12 months: Five steps to take without waiting
Waiting for the law to pass is not a strategy, because what all three pending bills share is a risk-based approach and documentation obligations. The work to start now is clear. First, build your AI inventory — covering not only approved tools but also the services teams have adopted on their own. Second, map your use cases against the areas the Action Plan and EU regulation treat as high-impact (recruitment, credit, healthcare, biometrics) and prioritize accordingly.
Third, turn the KVKK guide into a checklist: privacy notices, impact assessments, and logging practice should be completed this year. Fourth, if you run customer-facing generative AI, apply the Article 50 transparency requirements — AI disclosure and content labeling — now, across every channel that touches the EU. Fifth, review your vendor contracts; for every AI service you consume, put model cards, data processing terms, and the allocation of responsibility in writing.
None of these five steps waits for legislation, and none is a wasted investment. Whatever form regulation finally takes, an AI operation with its inventory mapped, its data classified, and its logs in place wins in every scenario.