# AI Regulation in Türkiye: Current State and Expectations (2026 Guide) > Source: https://sukruyusufkaya.com/en/blog/turkiye-yapay-zeka-regulasyonu > Updated: 2026-07-10T05:31:17.262Z > Type: blog > Category: yapay-zeka **TLDR:** What is AI regulation in Türkiye and what is its current state? KVKK, sectoral rules, the National AI Strategy, a possible AI law, and EU AI Act alignment in this guide. AI regulation in Türkiye is the whole of the legal framework governing the development and use of AI systems in Türkiye. As of today there is no single, comprehensive AI-specific law in Türkiye; compliance is run through existing horizontal legislation — chiefly Law No. 6698, the KVKK (Personal Data Protection Law) — sectoral regulations, and the National AI Strategy. This does not mean "there are no rules"; on the contrary, many existing rules surrounding AI — data protection, consumer law, competition, sectoral supervision — are binding today. With the lens of a management consultant and compliance engineer, this pillar guide covers the current state of AI regulation in Türkiye, its legal ground, the National AI Strategy, the path toward a possible AI law, the impact of EU AI Act alignment on Türkiye, what organizations should do today, sectoral expectations, a global comparison (EU, US, China), and a compliance strategy under uncertainty. The goal is to turn the excuse "regulation is not clear yet" from a reason for inaction into a defensible compliance posture. This guide is for information only and does not constitute legal advice. Legislative provisions (KVKK articles, EU AI Act risk levels) are presented as a general framework; organizations should consult a qualified legal expert and data-protection advisor for their specific situation. Regulations change; base your decisions on current official sources. ## Why Is AI Regulation in Türkiye So Critical Now? AI is no longer a future technology but today's operational reality: banks use it in credit decisions, hospitals in image analysis, retailers in personalization, public bodies in service delivery. This ubiquity turns the regulation question from theoretical into an urgent management issue. As a technology's use spreads, so do its risks — discrimination, privacy breaches, faulty automated decisions, lack of transparency — and the law responds to these risks sooner or later. Türkiye's position here is particularly notable. It is one of the fastest societies in the world at adopting AI tools; generative AI use is spreading rapidly at both individual and corporate levels. High adoption means a high need for regulation: the more organizations use AI, the more the clarity of rules protecting consumers, employees, and citizens matters. That is why AI regulation in Türkiye is not a topic to be deferred but today's agenda. The second critical reason is that regulatory uncertainty is itself a risk. Many executives fall into the fallacy of "there is no clear AI law, so we are free." Yet existing legislation — especially the KVKK — applies to AI today, and violations can lead to administrative fines. Uncertainty does not mean the absence of rules; it means the rules are scattered and open to interpretation. In this environment, what protects organizations is not waiting for regulation to clarify but taking a proactive compliance posture aligned with existing obligations and international best practice. To see AI and its enterprise impact in a broad frame, the what is AI guide is a good start. The third reason is exports and international trade. With the EU AI Act, the European Union enacted the world's most comprehensive AI regulation. For Turkish organizations offering products or services to the European market, EU AI Act alignment becomes effectively binding even though they are located in Türkiye. This is a strong external pull toward the European standard, much like the "Brussels effect" GDPR created in data protection. Therefore, thinking of AI regulation in Türkiye as limited to local legislation alone would be incomplete; economic integration with Europe raises the compliance bar. The fourth, strategic reason is trust. One of the biggest causes of failure in AI projects is not technical inadequacy but a lack of stakeholder trust: a consumer unsure how their data is used, a citizen who cannot see the rationale of an automated decision, a manager who cannot tell where responsibility lies. A solid compliance and governance framework builds this trust and clears the path for AI to create value. In this sense, regulation is not an obstacle but a precondition for sustainable AI value. ## What Is the Current Legal Ground of AI Regulation in Türkiye? Although Türkiye has no comprehensive law that names AI directly, AI does not operate in a vacuum. On the contrary, many horizontal (cross-sector) and vertical (sector-specific) regulations already in force apply to AI applications. The current legal ground of AI regulation in Türkiye consists of reflecting this existing legislation onto AI. At the head of the horizontal legislation is Law No. 6698, the KVKK; it covers every AI system that processes personal data. Alongside it, the Turkish Penal Code (e.g., unlawful acquisition of data, privacy), the Code of Obligations (contractual and tort liability), the Commercial Code, intellectual and industrial property law (copyright of generative AI outputs), Consumer Protection Law No. 6502, and Competition Law No. 4054 come into play. None of these say "AI," but all apply to AI applications. The most important thing this table shows is this: AI regulation in Türkiye lives not in a single document but in a mosaic of legislation. Instead of relaxing because "there is no AI law," an organization must map which parts of this mosaic each AI system it uses touches. A customer chatbot processing personal data touches the KVKK; a model making credit decisions touches both the KVKK and the BRSA; a generative AI tool that can produce fake content touches both criminal law and intellectual property. Compliance begins with seeing these contact points. One consequence of this scattered ground is interpretive uncertainty: because there are no AI-specific provisions, how existing rules apply to the new technology is often shaped by case law, board decisions, and expert interpretation. This uncertainty makes a cautious compliance approach — taking the most likely strict interpretation as the basis — the wise choice. For responsible AI principles at the enterprise level, the what is responsible AI and, for the governance framework, the what is AI governance guides form a foundation. ## Where Does the KVKK Stand in AI Regulation? If there is a single backbone to AI regulation in Türkiye today, it is the KVKK. The reason is simple: the overwhelming majority of AI systems process personal data — customer records, employee data, health information, behavioral traces. The moment an AI system uses personal data, all the principles and obligations of Law No. 6698 come into play. That is why, in practice, "AI compliance" largely means "KVKK compliance." The KVKK's core principles that reflect directly onto AI are as follows. **Lawfulness and fairness:** every processing activity must have a legal basis — explicit consent, performance of a contract, legal obligation, or legitimate interest. **Purpose limitation:** data must be used for the purpose it was collected for; using data collected for one purpose later for AI training requires a separate assessment. **Data minimization:** only necessary data should be processed. **Accuracy and currency, storage limitation, and data security** are the other core principles. ### Why Are Automated Decision-Making and Profiling Especially Important? In the AI context, the KVKK's most sensitive area is automated decision-making and profiling. When an individual's credit, hiring, insurance premium, or access to a service is determined entirely by an automated system, that decision has significant effects on the individual. In such decisions, transparency, explainability, and the ability to object gain special importance. The individual being able to understand the logic of an automated decision about them and to object to it is both a legal and an ethical requirement. To produce the rationale of automated decisions, the model must be explainable; the what is explainable AI guide helps here. ### What Is the Relationship Between Training Data and Anonymization? AI models are trained on data; if this training data contains personal data, its lawful acquisition is a critical compliance topic. Organizations often say "we will train the model with the data we have"; but if that data's collection purpose was not model training, a risk of purpose-incompatible use arises under the KVKK. One of the strongest tools to manage this risk is data anonymization: when data is rendered unlinkable to a specific person, it falls outside the KVKK's scope. However, anonymization is technically hard and, done wrong, carries re-identification risk. We cover these topics in what is personal data and what is data anonymization; for the conceptual framework of the KVKK, the what is the KVKK guide is the core resource. Placing KVKK compliance at the center of AI architecture is both a legal necessity and a strategic advantage: an AI system designed to be KVKK-compliant is also largely ready for stricter future regulations (a possible AI law, EU AI Act). We deepen this integrated approach in the what is KVKK-compliant AI guide. In short, the KVKK is both today's ground of AI regulation in Türkiye and a rehearsal for tomorrow's compliance. A common and dangerous fallacy is to think that because there is no separate AI law, AI use is unsupervised. In reality, the KVKK, consumer, competition, and sectoral legislation are binding today. If an AI system unlawfully processes personal data, the presence of "AI" does not mitigate the penalty; KVKK sanctions apply as they are. Deferring compliance until a law is enacted means ignoring today's risk. ## How Do Sectoral Regulations Bind AI? (BRSA, Health, and Others) Alongside horizontal legislation, certain sectors operate under the rules of their own regulatory authorities, and these rules reflect directly onto AI applications. The vertical dimension of AI regulation in Türkiye is exactly these sectoral regulations. The same AI technique is subject to very different obligations in banking versus retail, because the sector's risk profile and supervision intensity differ. ### Banking and Finance: The BRSA Framework Banking is one of the most heavily supervised sectors in Türkiye, and the BRSA (Banking Regulation and Supervision Agency) has detailed regulations on banks' information systems, risk management, and outsourcing. When a bank uses AI in credit scoring, fraud detection, or customer segmentation, topics like model risk management, traceability of decisions, supervision of outsourced providers, and data security come into play. Especially in cloud-based or external-API AI solutions, where data is processed and compliance with the BRSA's outsourcing rules are critical. This directly affects banks' AI architecture decisions (self-hosting vs. outsourcing). ### Health: A High-Risk and Highly Supervised Area AI in health — diagnosis support, image analysis, patient triage — is potentially the area of highest benefit and highest risk. Here both the KVKK's heavy protection regime for special-category personal data (health data) and health legislation and medical-device regulations come into play. If an AI software performs a medical function (making a diagnosis, recommending treatment), it may be assessed as "software as a medical device," which brings a serious conformity burden. Processing health data requires the strictest standards for consent and security. ### Other Sectors: Insurance, Public, Telecom, Retail AI in insurance for risk pricing and claims assessment; in public services for citizen data and transparency in administrative acts; in telecommunications for traffic and location data; in retail for behavioral profiling and personalization — each becomes subject to regulation with its own sectoral sensitivities. The common denominator is this: whatever the sector, if AI touches personal data or an important decision process, both horizontal (KVKK) and vertical (sectoral) rules apply together. This sectoral diversity explains why a one-size-fits-all compliance recipe does not work. A retailer's compliance roadmap cannot be the same as a bank's. Therefore, a solid compliance effort starts by defining the organization's sector and that sector's specific obligations. For the intersection of this journey with digital transformation, see the what is digital transformation guide, and for a tailored compliance and strategy effort, the what is AI consulting guide. ## How Does the National AI Strategy Shape AI Regulation in Türkiye? Türkiye's most important policy framework in AI is the National AI Strategy. This document is not a law; it imposes no binding obligations. But for those who want to read the future of AI regulation in Türkiye, it is a critical map, because it signals the state's view of AI, its priorities, and the likely direction of future regulation. Regulation often appears first as strategy and policy, then turns into law; so reading the strategy document is one of the best ways to anticipate future rules. The National AI Strategy typically highlights several main axes: qualified human capital and employment, the R&D and entrepreneurship ecosystem, data infrastructure and access, AI applications in the public sector, international collaboration, and — most importantly for regulation — trustworthy, ethical, and human-centric AI. This last axis gives the most clues about the regulatory future: the emphasis on "trustworthy AI" shows that principles like transparency, accountability, human oversight, and anti-discrimination will be central in future regulation. ### Why Is Strategy a Herald of Regulation? A strategy document, even without direct sanctions, shapes organizational behavior in three ways. First, it encourages public bodies to adopt these principles in their own AI applications, which forms a de facto standard. Second, it prepares the conceptual framework of future legislation — today's "trustworthy AI" rhetoric becomes tomorrow's legal definitions. Third, it creates a compliance expectation in public tenders and incentives; organizations doing business with the state turn early toward aligning with strategy principles. For this reason, smart organizations read the National AI Strategy not merely as a vision document but as an early draft of future compliance obligations. Incorporating the principles the strategy highlights (ethics, transparency, human oversight) into the AI governance framework today is valuable both as preparation for future regulation and for today's stakeholder trust. Reading the National AI Strategy this way is the most practical way to foresee the direction of AI regulation in Türkiye. ### The Gap Between Strategy and Implementation An honest assessment must acknowledge that there is always a gap between strategy and implementation. A strategy document saying "trustworthy AI" does not mean this is automatically realized; turning goals into concrete regulations, supervision mechanisms, and institutional capacity takes time. So organizations should take the direction the strategy points to seriously but be realistic about the timeline. The National AI Strategy shows the direction; the speed and exact form are determined by political will, international pressure, and the ecosystem's maturity. ## Is an AI Law Coming to Türkiye? This is the most frequently asked and most speculation-prone question: will Türkiye enact its own comprehensive AI law, and if so, when and how? The honest answer is: today there is no AI law in force, and giving a definite timeline would be inappropriate. But by analyzing the trends and driving forces, we can reasonably foresee the logic by which a possible AI law might take shape. The forces pushing toward a horizontal AI law are clear. First, the international trend: with the EU AI Act, Europe made the world's most comprehensive AI regulation, and many countries are evaluating similar frameworks. Second, rising AI use and its attendant risks: proliferating automated decisions, generative AI, and technologies like deepfakes make visible the areas where the current scattered legislation falls short. Third, the need for alignment: trade and data flows with Europe encourage Türkiye to adopt a framework converging with EU standards. ### What Logic Would a Possible AI Law Adopt? Looking at the international trend, it can be foreseen that a possible Turkish AI law would most likely adopt a risk-based approach — just as the EU AI Act does. The risk-based approach, instead of putting all AI in the same basket, imposes graded obligations according to the harm the system could cause: heavy rules for high-risk uses (health, hiring, credit, critical infrastructure), and light or no rules for low-risk uses. This approach aims to balance not stifling innovation with managing real risks. The likely components of an AI law could be: risk classification, mandatory obligations for high-risk systems (risk management, data quality, documentation, human oversight, transparency), prohibited practices (e.g., manipulative or discriminatory systems), transparency requirements (the user knowing they are interacting with AI), a supervision and enforcement mechanism, and a structure compatible with the existing KVKK. Most of these components exist in the EU AI Act today; that is why understanding the EU AI Act is the best way to foresee a possible Turkish AI law. The most common mistake organizations make is to wait, thinking "let the AI law come, then we will comply." This is wrong for three reasons: (1) existing legislation (KVKK, sectoral rules) is binding today; (2) when a possible law comes, the compliance grace period may be short and unprepared organizations will struggle; (3) an organization aligned with the EU AI Act is largely ready for a possible Turkish law too. Instead of waiting, preparing today according to the most likely standard is both risk-free and strategically superior. It must be emphasized once more in this section: what is done here is not a claim about the exact content or date of an AI law, but a reasonable foresight based on international trends. The future of AI regulation in Türkiye will be shaped by the interaction of political will, economic integration, and technological developments. For organizations, the right stance is not to attempt precise prediction but to build a compliance framework resilient to all possible scenarios. ## How Does EU AI Act Alignment Affect Türkiye? The strongest external factor shaping AI regulation in Türkiye is, without doubt, the European Union's EU AI Act. As the world's first comprehensive, horizontal AI law, this regulation affects not only Europe but all countries with an economic relationship with Europe. For Turkish organizations, EU AI Act alignment can become an effective obligation despite their being located in Türkiye; this is the AI-domain counterpart of the "Brussels effect" GDPR created in data protection. The EU AI Act's core logic is risk-based classification. AI systems are divided into four categories according to the harm they could cause, and each category is subject to a different obligation level. This pyramid structure is the heart of the regulation and, being the most likely model for a possible Turkish AI law, must be understood carefully. ### Which Turkish Organizations Does the EU AI Act Bind? EU AI Act alignment becomes binding for Turkish organizations in three typical situations. First, providers placing an AI system or a product containing it on the European market (e.g., a Turkish technology company selling software to Europe). Second, organizations operating AI systems whose output is used in Europe. Third, Turkish organizations producing AI-based services for a European customer or parent company — contractually, EU AI Act alignment is required. In any of these situations, the defense "we are in Türkiye, it does not bind us" is invalid. For a Turkish organization that does not trade with Europe and serves only the domestic market, the EU AI Act is not directly binding. But even for these organizations, the EU AI Act is a strategic reference: since it is highly likely that a possible Turkish AI law would adopt a similar risk-based logic, preparing according to the EU AI Act is the safest bet for the future. We cover the EU AI Act's detailed structure in the what is the EU AI Act guide. ### How Do the EU AI Act and the KVKK Work Together? The EU AI Act and the KVKK/GDPR do not replace each other; they work together, in layers. While the KVKK/GDPR regulates how data is processed, the EU AI Act regulates how the AI system itself is designed, documented, and audited. A high-risk AI system requires both GDPR compliance (data) and EU AI Act compliance (system). The practical result for Turkish organizations: if KVKK compliance is already done, the data dimension of EU AI Act compliance is largely ready; what needs to be added are the system-level obligations (risk management, technical documentation, human oversight, record-keeping). Building these two frameworks together avoids duplicate work. ## Global Comparison: How Do the EU, US, and China Regulation Models Differ? To understand where AI regulation in Türkiye might evolve, comparing the world's three major regulatory models is illuminating. The European Union, the United States, and China regulate AI with three different philosophies; and Türkiye's eventual model will likely be a distinctive synthesis drawing especially from the EU. **European Union: rights-based, comprehensive regulation.** The EU model centers the individual's fundamental rights and imposes risk-based obligations through a comprehensive, horizontal law (the EU AI Act). The priority is to protect citizens and build a trustworthy AI ecosystem. Its criticism is that it may slow innovation; its strength is offering clear and predictable rules. **United States: market- and innovation-oriented, sectoral approach.** Rather than a single comprehensive federal AI law, the US advances through sectoral regulations, institutional guidance (e.g., voluntary frameworks like the NIST AI RMF), and state-level initiatives. The priority is preserving innovation and competitiveness. Its strength is flexibility; its weakness is being fragmented and potentially unpredictable. **China: state-centric, control-oriented regulation.** China manages AI with fast, targeted regulations (e.g., specific rules on recommendation algorithms, generative AI, and deepfakes); the priority is social stability, state control, and strategic technology leadership. Its strength is speed and resolve; its difference is that the emphasis on individual rights differs from Western models. Türkiye's position in this table is meaningful: currently it follows a data-protection (KVKK) based approach resting on a legislative mosaic; but economic integration with the EU and a GDPR-aligned KVKK structure strongly signal that a possible regulation would converge toward the EU model. This gives Turkish organizations a practical inference: among the global models, the EU (EU AI Act) is the most likely reference for Türkiye; so preparing compliance according to the EU standard is the most sensible bet. To connect such international frameworks with corporate strategy, the what is responsible AI guide offers a holistic view. ## Data Protection and AI: What Are the Intersection Points in Türkiye? The most mature, most binding, and most concrete dimension of AI regulation is data protection. The reason lies in AI's nature: AI is fed on data, trained on data, and runs on data. When this data contains personal data, data-protection law (the KVKK in Türkiye) applies directly. That is why data protection is not the theoretical but the daily-practice dimension of AI regulation in Türkiye. Every stage of the AI lifecycle harbors a data-protection intersection. **At collection:** lawful acquisition of training and operational data, disclosure, and where required explicit consent. **At preparation:** data minimization, stripping unnecessary personal data, and where possible anonymization or pseudonymization. **At training:** purpose limitation — the compatibility of the data's collection purpose with the model-training purpose. **At operation:** outputs not leaking personal data, and the auditability of automated decisions. **At storage:** the storage-period limit and secure disposal. ### Why Is a Data-Protection Breach Riskier in AI? AI can amplify the impact of data-protection breaches. In a classic breach, specific records leak; whereas a poorly designed AI system can unintentionally reveal personal information from the training data in its outputs, produce sensitive inferences through profiling, or create systematic discrimination through scaled automated decisions. That is why data protection in AI means not just "keep the data safe" but "also audit how the system transforms the data and what it produces." This expanded responsibility makes data-protection design an inseparable part of AI architecture. ### How Is Privacy by Design Applied? The most robust approach is to treat data protection not as a layer added afterward but as a principle embedded from the system's first design — this is called "privacy by design." In practice, this means making data minimization the default, setting up access controls from the start, applying anonymization wherever possible, keeping a personal-data processing inventory, and conducting a data-protection impact assessment (DPIA). In high-risk AI projects, a DPIA is a common and valuable tool for both KVKK and possible EU AI Act compliance. You can find the way to embed these principles into AI architecture in the what is KVKK-compliant AI guide, and the conceptual basis in the what is GDPR guide. A frequently overlooked risk in enterprise use of generative AI tools is employees entering sensitive corporate or personal data into an external AI tool. This data goes to a third party's system and can fall out of control. Creating a clear policy on generative AI use (what data can and cannot be entered) is an urgent measure for data protection. We cover the nature of generative AI in the what is generative AI guide. ## What Should Organizations Do Today for AI Regulation in Türkiye? So far we have covered the current state and the future; now to the most practical question: what should an organization do today, despite regulatory uncertainty? The answer is clear — not to wait, but to build a resilient compliance foundation. This foundation meets today's KVKK and sectoral obligations while also laying the ground for a possible AI law and EU AI Act alignment. The good news is that most of these steps are shared: a well-built foundation serves all possible regulations. The compliance foundation consists of seven core components. **Inventory:** recording all AI systems the organization uses and the data they process. An organization without an inventory cannot even know what to make compliant. **Risk classification:** classifying each system by impact (high/medium/low); focusing resources on the highest-risk uses. **Data-protection compliance:** ensuring KVKK requirements (lawfulness, disclosure, security, data-subject rights) for each system. **Governance:** an AI governance framework, internal policy, and responsibility assignments. **Human oversight:** meaningful human supervision and the ability to intervene in critical decisions. **Transparency and explainability:** users knowing they are interacting with AI and automated decisions being justifiable. **Documentation and traceability:** recording decisions, data flow, and model behavior. The order of these steps matters: without an inventory there is no risk classification, and without risk classification there is no prioritization. Organizations often start with the most visible step (writing a policy); yet the right starting point is the inventory. Without knowing what you use, you cannot know what to regulate. To integrate this compliance journey with corporate transformation, the what is digital transformation and, to deepen the governance side, the what is AI governance guides help. ### Why Is AI Governance Central? The roof holding all the above steps together is AI governance. Governance is the framework defining who is responsible for which AI decision, which uses require approval, how risks are assessed, and how breaches are handled. Without good governance, compliance steps remain scattered and unsustainable; each project invents its own rules and consistency across the organization is lost. AI governance turns compliance from a one-off project into a continuous discipline embedded in how the organization operates. A solid governance framework answers three questions clearly: (1) Who decides? — a mechanism approving AI uses and responsible roles. (2) Which rules apply? — acceptable-use, data, transparency, and human-oversight policies. (3) How do we audit? — regular review, record-keeping, and incident management. An organization answering these three questions gains the flexibility to adapt quickly however regulation evolves. ## AI Compliance Checklist for Türkiye The checklist below turns the above steps into an operational audit tool. If you can mark each item "yes," your compliance posture is largely solid; the ones you cannot mark are your priority work areas. This list is a starting point; it should be expanded according to the organization's sector and risk profile. Applying this checklist at least once a year, ideally with every important new AI project, keeps compliance alive. Compliance is not a certificate to be obtained once and shelved but a state requiring continuous maintenance, because both technology and regulation change rapidly. For teams to gain this awareness, the what is AI literacy guide and the corporate AI training page are valuable. ## Sectoral Expectations: What Does Each Sector Expect from AI Regulation in Türkiye? The future of AI regulation in Türkiye is not uniform; each sector has different expectations and priorities. Understanding these expectations is valuable both for reading the likely priorities of regulation and for positioning the organization's own readiness within its sector. The **finance and banking** sector expects clarity and predictability: clear standards on model risk management, explainability, and outsourcing rules, aligned with the BRSA. Being already heavily supervised, this sector wants to integrate additional AI rules into the existing framework. The **health** sector expects a balance aligned with patient safety and medical-device regulations but not stifling innovation; it seeks clarity especially in the approval processes of diagnosis-support systems. The **technology and software** sector, especially exporters, expects a framework aligned with the EU AI Act so it can serve both the local and European markets by complying with a single standard. The **retail and e-commerce** sector expects a reasonable balance between consumer rights and commercial flexibility on personalization and profiling. The **manufacturing and industry** sector wants low-privacy-risk uses like predictive maintenance and quality control not to be subjected to needlessly heavy rules. The **public** sector, meanwhile, focuses on transparency and accountability standards that preserve citizen trust. The common expectation is this: a risk-based, proportionate regulation — a framework that manages high-risk use seriously without stifling low-risk use. The strongest common expectation across sectors is proportionality: applying the same strict rule to both a spam filter and a credit-decision system is neither fair nor efficient. The risk-based approach (the EU AI Act model) is preferred by both regulators and organizations because it delivers this proportionality. That a possible Turkish AI law adopts this proportionality principle is the common denominator of sectoral expectations. These sectoral expectations also give organizations a message: knowing your own sector's regulatory sensitivity and expectation lets you prioritize your preparation correctly. A health institution's and a retailer's compliance roadmaps should differ, because the regulatory burden and timing they will face will differ. For a tailored roadmap, the what is AI consulting guide and the consulting services are the starting point. ## Who Is Responsible in AI Regulation? (Provider, Deployer, Supplier) One of the most misunderstood topics in AI regulation in Türkiye is the distribution of responsibility. When an AI system causes harm — a faulty decision, a discriminatory output, a data breach — who is responsible? The one who developed the model, the organization using it, or the supplier providing the infrastructure? This question is critical for both existing legislation and a possible AI law, and most organizations hold a dangerous misconception here. The most common misconception is "we only use a ready-made AI tool, so responsibility lies with the provider." In reality, when personal data processing is involved, the KVKK defines the organization that processes the data and determines the purpose and means as the "data controller"; and this responsibility does not vanish by using an external tool. If an organization processes customer data using a third party's AI service, it is largely responsible itself for the lawfulness of that processing. The supplier may be in the "data processor" position, but this does not eliminate the responsible organization's obligation; it only shares it by contract. The EU AI Act divides responsibility into roles in even more detail: the "provider" who develops and places the system on the market, the "deployer" who uses the system on their own behalf, the importer, and the distributor. Each role has different obligations. The practical inference for Turkish organizations: you must clearly define your role in the AI value chain — are you a developer, an integrator, or only an end user? Your role determines which obligations you are subject to. The strongest tool to manage this uncertainty is the contract. When procuring an external AI service, the contract should clearly regulate data-processing conditions, compliance commitments, liability limits, audit rights, and breach-notification obligations. Without contractual clarity, a breach produces chaos where the parties point at each other and responsibility hangs in the air. That is why supplier management and contract hygiene are among the invisible but most practical parts of AI compliance. One of organizations' costliest mistakes is the assumption "we use a ready-made AI tool, the rest is not our concern." Under the KVKK, the data controller is the organization that processes the data; using an external tool shares this responsibility by contract but does not eliminate it. In a breach, the defense "we did not build the tool" does not free the data controller from obligation. That is why supplier selection and its contract are a compliance decision. ## What Do International Standards Mean for AI Regulation in Türkiye? A dimension often skipped but increasingly important in the context of AI regulation in Türkiye is international voluntary standards. These are not laws and not directly binding; but they both set the good-practice bar and shape the technical language of possible regulations. Two references stand out especially: ISO/IEC 42001 and the NIST AI RMF. **ISO/IEC 42001** is an international standard for an AI Management System. Just as ISO/IEC 27001 does in information security, this standard defines the processes, roles, and controls an organization needs to manage AI responsibly. When an organization is structured according to ISO/IEC 42001, it gains a systematic framework on topics like risk management, data governance, transparency, human oversight, and continuous improvement. This both gives stakeholders confidence and largely completes readiness for a possible AI law. **The NIST AI RMF** (the US National Institute of Standards and Technology's AI Risk Management Framework) is a voluntary but effective framework for managing AI risks. It is built around four core functions: govern, map, measure, and manage. This framework offers a practical language especially on risk classification and measurement and is widely adopted worldwide. For Turkish organizations, these standards mean three layers. First, they offer a compliance bar: an organization wondering what to do can use these standards as a checklist. Second, they are preparation for possible regulation: both the EU AI Act and a possible Turkish AI law are compatible with the concepts in these standards; an organization prepared to the standard is prepared for the law too. Third, international credibility: for Turkish organizations serving European or global customers, a standard like ISO/IEC 42001 is a mark of trust and competitiveness. To combine these frameworks with corporate governance, the what is AI governance and what is responsible AI guides form a foundation. ## How Are Public and Employment AI Regulation Taking Shape? Two special areas of AI regulation in Türkiye are public services and employment; because in these two areas AI use directly touches citizens' fundamental rights and lives. For this reason, these areas are handled with special sensitivity in both existing legislation and possible regulations. **AI in the public sector** draws its legitimacy from transparency and accountability. When a citizen's access to a public service or the result of an administrative act is determined by AI, they must have the right to know the rationale of that decision, to object, and to demand human review. Public bodies' AI use is subject to a higher transparency standard than the private sector, because public power is being exercised and the citizen must be protected against that power. The National AI Strategy's emphasis on "trustworthy and human-centric AI" finds its most concrete counterpart in public applications. **AI in employment**, especially when used in hiring, performance evaluation, and termination decisions, carries high risk in terms of discrimination and fairness. A hiring algorithm can learn historical bias in the training data and systematically disadvantage certain groups. That is why employment is an area classified as "high risk" in the EU AI Act, and a similar sensitivity is expected in a possible Turkish regulation. Employer organizations, when processing employee and candidate data, must act in compliance with both the KVKK and anti-discrimination principles; human oversight and explainability in automated decisions are especially critical here. These two areas show why AI regulation is not only a technical but also an ethical and societal matter. Uses in the public sector and employment go beyond "is AI efficient?" and move "is AI fair, transparent, accountable?" to the center. An AI application that cannot answer these questions positively carries both legal and reputational risk, however technically successful it is. To understand AI bias and fairness in depth, the what is bias in AI and, for decision transparency, the what is explainable AI guides help. For organizations to prepare their teams in these areas, the what is AI literacy guide is also a valuable start. ## Does AI Regulation Stifle Innovation? How Is the Balance Struck? In debates on AI regulation in Türkiye, the most frequently voiced concern is that regulation will stifle innovation. The fear "if too many rules come, startups slow down, investment flees, and Türkiye falls behind in the AI race" is common. This concern is not entirely baseless; but reading the relationship between regulation and innovation as a balance problem — and, when well designed, as complementarity — is more accurate. Excessive and poorly designed regulation can indeed harm: a vague and unpredictable framework imposing heavy obligations on low-risk uses disproportionately burdens small startups in particular and deters innovation. But the absence of regulation is not a solution either; a rule-less environment damages consumer trust, lets bad actors cause harm, and lowers trust in the whole ecosystem long-term. In an unsafe market, innovation is unsustainable too, because no one pays for a technology they do not trust to use. The right balance is risk-based and proportionate regulation — an approach that frees low-risk use and manages high-risk use seriously. This approach imposes almost nothing on the vast majority of innovation (low- and minimal-risk applications); it focuses only on areas with real harm potential. In addition, tools like regulatory sandboxes let startups test novelty in a controlled environment, reconciling regulation with innovation. That Türkiye's possible regulatory approach observes this balance is the common demand of both sectoral expectations and a healthy ecosystem. The practical inference for organizations is this: seeing regulation not as an enemy but as a framework is more productive. A good compliance posture actually accelerates innovation — because an organization operating within clear rules does not have to stop a project later with the worry "is this legal?" Organizations that embed compliance into design from the start move both faster and more confidently. In this sense, regulation, when well managed, is not the brake but the ground of sustainable AI innovation. Experience shows that the fastest-scaling AI organizations are those that design compliance not as a burden to be patched on later but as the trust layer of the product; because once customer, supplier, and regulator trust is lost, it is the most expensive asset to regain. To connect this balance with corporate strategy, the what is digital transformation guide and, for a tailored roadmap, AI consulting are starting points; and for teams' compliance and ethics awareness, corporate training programs are a strong complement. ## How Is a Compliance Strategy Built Under Uncertainty? The most challenging aspect of AI regulation in Türkiye is that it is not yet fully clear. Organizations often face this dilemma: "If we do too little, we get caught unprepared when regulation arrives; if we do too much, we spend resources on something not yet mandatory." The approach that resolves this dilemma is the discipline of decision-making under uncertainty — intelligence, scenario planning, and flexibility. The most robust strategy is the principle of "aligning with the strictest reasonable standard." In practice, this means building your compliance framework according to the combination of the standard valid today (KVKK) and the most likely near-future reference (EU AI Act). Why the strictest? Because an organization ready for the strictest reasonable standard is already compliant with any looser regulation; the reverse is not true. This is the "upward compliance" approach (building compliance to the highest possible bar) and it turns uncertainty into an advantage. ### A Flexible and Modular Compliance Architecture The second principle under uncertainty is flexibility. You should build your compliance foundation not as a rigid structure locked to a single regulation but as adjustable modules. For example, an AI inventory, risk classification, and governance framework work whatever regulation arrives. When regulation clarifies, you adjust these modules' parameters (e.g., risk thresholds, documentation depth), but you do not have to rebuild the core structure. This modularity both preserves today's investment and lowers the cost of adapting to future change. ### Scenario Planning: Three Possible Futures The classic tool for managing uncertainty is scenario planning. Three reasonable scenarios can be envisioned for AI regulation in Türkiye. **Scenario A — gradual existing legislation:** no separate AI law is enacted; compliance advances through strengthening KVKK interpretations and sectoral rules. **Scenario B — a horizontal law converging with the EU:** Türkiye enacts a risk-based AI law similar to the EU AI Act. **Scenario C — fast, targeted regulations:** quick, pinpoint rules come on specific topics (generative AI, deepfakes, automated decisions). The good news is that the flexible compliance foundation proposed above is resilient to all three of these scenarios — because their common denominator is data protection, risk management, and governance. The main message of this table is powerful: whichever direction the future takes, the organization that builds the right foundation today wins. Uncertainty should be a reason not for inaction but for intelligent preparation. The added value of an AI consultant appears exactly here: reading the scenarios, determining the strictest reasonable standard, and designing a flexible framework. You can get this support with AI consulting and deepen all concepts in the learning center. ## What Are the Common Mistakes and Violations in AI Regulation in Türkiye? Seen through an experienced compliance eye, organizations fall into the same mistakes again and again in the face of AI regulation. Knowing these mistakes in advance is the easiest way to avoid them. The most common ones are: - **The "no law, so we are free" fallacy:** the most basic and dangerous mistake. The absence of a separate AI law does not mean the KVKK and sectoral legislation do not apply. This fallacy leaves the organization defenseless against today's binding obligations. - **Skipping the lawfulness of training data:** training the model "with the data at hand" is a common reflex; but if that data's collection purpose was not model training, a risk of purpose-incompatible use and KVKK violation arises. The legal basis of the data source must be questioned from the start. - **Lack of disclosure and transparency:** data subjects not knowing their data is processed in an AI system or that they are interacting with AI is a fundamental transparency violation. - **No human oversight in automated decisions:** making high-impact decisions (credit, hiring) entirely automatically and without supervision carries both legal and ethical risk; in a faulty decision, responsibility is the organization's. - **Unexplainable "black box" models:** systems that cannot produce the rationale of decisions eliminate the right to object and auditability; this is a large compliance gap in possible regulations. - **Ignoring the supplier chain:** using an external AI service does not eliminate responsibility. If contracts lack data-processing, compliance, and liability clauses, the organization is exposed to chained risk. - **Not auditing discrimination and bias:** AI can learn bias in the training data and produce discrimination at scale; an organization that does not measure this unknowingly accumulates legal and reputational risk. Generative AI and deepfakes are a rapidly growing violation area: non-consensual fake images/audio, impersonation, fraud, and misleading content violate both existing criminal law and personality rights. Organizations must both not misuse this technology and protect themselves against such attacks. We cover deepfake risk in what is deepfake and AI bias in what is bias in AI. The common feature of these mistakes is that all are preventable. The compliance foundation proposed above (inventory, risk classification, KVKK compliance, governance, human oversight, documentation) prevents the vast majority of these mistakes from the start. Another common trait of the mistakes is that they accumulate through the "we will deal with it later" deferral; yet the compliance gap grows over time and becomes expensive to remedy when regulation arrives. ## How Is AI Regulation Compliance Maturity Measured in Türkiye? Compliance is not a "yes/no" state but a maturity spectrum. Measuring an organization's readiness against AI regulation in Türkiye is necessary both to see the current state and to track progress. Measuring maturity turns compliance from an abstract worry into a manageable indicator. It is practical to assess compliance maturity at five levels. **Level 1 — Unaware:** the organization uses AI but is unaware of regulatory obligations; there is no inventory. **Level 2 — Aware:** risks are known but there is no structural response; reactions are scattered and reactive. **Level 3 — Structured:** there is an inventory, basic KVKK compliance, and an initial policy. **Level 4 — Managed:** risk classification, governance framework, human oversight, and documentation work systematically. **Level 5 — Optimized:** compliance is embedded in the organization's culture; it is continuously monitored, regularly audited, and proactively adapted to regulatory changes. Most Turkish organizations today are between Level 1 and Level 3; and this is actually an opportunity: the early mover both reduces its risk and gains a trust and compliance advantage over competitors. The practical way to measure maturity is to apply the checklist and level definitions above once a year and track progress against the previous period. A measurable maturity goal (e.g., "moving from Level 3 to Level 4 this year") makes compliance concrete and manageable. To strengthen the organizational-competency side, see the what is AI literacy guide and, for team training, the corporate training programs. ## Frequently Asked Questions ### Is there a dedicated AI law in Türkiye? No, as of today there is no single, comprehensive AI-specific law in Türkiye. AI regulation in Türkiye is run through existing horizontal legislation (especially Law No. 6698, the KVKK), the Turkish Penal Code, the Code of Obligations, consumer and competition law, and sectoral rules (banking/BRSA, health, insurance). The National AI Strategy also provides a policy framework. This is not legal advice; organizations should consult a legal expert for their specific situation. ### Why is the KVKK so central to AI projects? Because the vast majority of AI systems process personal data, and the KVKK is the fundamental law setting the general framework for personal data processing in Türkiye. If an AI system uses personal data, a lawful basis (such as explicit consent or legitimate interest), the disclosure obligation, purpose limitation, data minimization, data security, and respect for data-subject rights are mandatory. Automated decision-making and profiling require extra care. That is why the KVKK is the de facto backbone of AI regulation in Türkiye today. ### Does the EU AI Act bind Turkish companies? Not directly, but indirectly yes. The EU AI Act can cover providers and deployers that place AI systems or their output on the EU market, wherever they are established. So for Turkish organizations exporting products/services to Europe or offering AI-based solutions to EU customers, EU AI Act alignment becomes effectively binding. Moreover, the EU AI Act is a risk-based reference model for Türkiye's possible own regulation; aligning early is a strategic advantage. ### Is the National AI Strategy a regulation? No, the National AI Strategy is not a law but a policy and roadmap document. It defines Türkiye's priorities, goals, and action areas in AI, highlighting themes such as trustworthy and ethical AI, qualified employment, data infrastructure, and public-private collaboration. Although it does not impose binding obligations, it signals the direction of future regulation, so it is one of the key documents organizations reading AI regulation in Türkiye should follow. ### What should organizations do today for AI compliance? Without waiting for uncertainty to resolve, they should build a basic compliance foundation: (1) an inventory of AI systems and the data they process, (2) a risk classification for each system, (3) KVKK compliance (lawfulness, disclosure, data security), (4) an AI governance framework and internal policy, (5) human oversight and decision explainability, and (6) documentation and traceability. These steps form common ground for both KVKK and possible EU AI Act compliance. ### What does a high-risk AI system mean? In a risk-based regulatory approach (especially the EU AI Act), AI systems are classified by risk level: unacceptable risk (prohibited practices), high risk (uses affecting human life, fundamental rights, or critical infrastructure; e.g., hiring, credit scoring, health), limited risk (transparency obligations), and minimal risk. High-risk systems are subject to heavy obligations like risk management, data quality, technical documentation, human oversight, and record-keeping. A possible AI law in Türkiye is expected to adopt a similar logic. ### What is the relationship between AI and data protection? Data protection is the most mature and most binding dimension of AI regulation. AI models are trained on data and run on data; when that data contains personal data, the KVKK applies. Data-protection principles (purpose limitation, data minimization, retention period, security) apply directly to the AI lifecycle. The lawful sourcing of training data, anonymization, and the auditability of automated decisions are also critical topics. In short, AI compliance is impossible without solid data-protection practice. ### When will an AI law be enacted in Türkiye? It is not possible to give a definite date, and speculating would be inappropriate. Today there is no comprehensive AI law in force in Türkiye; regulation advances through existing legislation and the National AI Strategy. International trends (especially the EU AI Act) and rising AI use strengthen the need for a horizontal regulation in the medium term. For organizations, the right approach is not to wait for a law but to be ready today according to existing legislation and international best practice. ### Is AI compliance too costly for SMEs? When scaled proportionally, compliance is manageable for SMEs too. An SME does not need to do everything at once; it starts with risk-based prioritization: the AI use that processes the most personal data and has the highest impact comes first. Basic KVKK compliance, a simple AI inventory, a clear usage policy, and compliance clauses in supplier contracts provide significant protection at low cost. Compared with the administrative fines and reputational risk of non-compliance, the cost is usually far lower. ### Are there special rules for generative AI? There is no separate law specific to generative AI in Türkiye; however, existing rules also apply here. Generative AI is subject to existing legislation on copyright and intellectual property, personal data (training data and outputs), misleading content (deepfakes), consumer protection, and liability. The EU AI Act imposes transparency and documentation obligations for general-purpose AI (GPAI) models. For Turkish organizations, the practical approach is to create an internal policy on sourcing, copyright, personal data, and content accuracy in generative AI use. ## In Short: Where Is AI Regulation in Türkiye Today, and Where Is It Heading? In short, AI regulation in Türkiye today lives not in a single law but in a legislative mosaic: the backbone is the KVKK, around it sit sectoral regulations (BRSA, health, insurance, competition, consumer), and above it, as a policy roof, the National AI Strategy. A separate, comprehensive AI law does not exist yet; but international trends, rising AI use, and integration with Europe point toward a risk-based horizontal regulation in the medium term. EU AI Act alignment, meanwhile, is a reality today for Turkish organizations opening to Europe and the most likely reference model for a possible Turkish law. The message this picture gives organizations is clear: uncertainty should be a reason not for inaction but for intelligent preparation. The most robust strategy is to align with the strictest reasonable standard (KVKK + EU AI Act); to build a flexible compliance foundation based on inventory, risk classification, data protection, AI governance, human oversight, and documentation; and to manage this as a maturity journey. Such a foundation keeps the organization ready and resilient however regulation evolves. What must be remembered is this: this content is for information only, not legal advice; organizations should consult legal and data-protection experts for their specific situations. To start the journey with the basic concepts, see the what is AI, what is the KVKK, and what is the EU AI Act guides; for a KVKK-compliant AI architecture, review the what is KVKK-compliant AI guide; for a tailored compliance and governance effort, start with AI consulting, evaluate corporate training options for your teams' compliance competency, and deepen all concepts in the learning center.