Founder
December 14, 2025
32 min read
Has the AI Revolution Hit a Wall of Data Scarcity?
Artificial Intelligence (AI), accepted as the greatest technological transformation of the century, is facing a paradoxical crisis today. While data is the most valuable resource of the digital economy, the processing of this resource is limited by one of the strictest regulation regimes in history, the Law on Protection of Personal Data (KVKK and GDPR). On one hand, there is a technical need for massive and "real" datasets for the development of models; on the other hand, there are legal barriers forcing data controllers to keep data in closed-circuit systems (silos). This situation creates a serious "legal and technical deadlock" that needs to be overcome in the data ecosystem. This tension between privacy and provability in traditional database management systems has evolved to a point where it cannot be managed with classical contractual assurances. While data owners (hospitals, banks) close their doors due to heavy sanctions, developers cannot train their models according to real-life scenarios because they cannot access data.
Is Synthetic Data a Legal Safe Harbor or a Technical Trap?
The first reflex developed by the sector against the data scarcity crisis has been the production of "Synthetic Data." From a legal perspective, this artificial data, whose link with real persons is severed, is perceived as a "safe harbor" since it falls outside the scope of KVKK. However, technical reality threatens this legal comfort zone. The fact that publicly available internet data (Common Crawl, etc.) is coming to the point of exhaustion and the internet itself is filling up with content produced by AI gives rise to the risk of "Model Collapse." This situation also conflicts with the "Data Quality and Accuracy" principles envisaged to be regulated under the EU AI Act. The way to prevent model collapse is to access unprocessed and high-quality "private" data. However, sharing this data, which is locked in the servers of hospitals or banks and has the status of "Special Category Personal Data" or legal "Secret" under KVKK Art. 6, is almost impossible in the current regime. Taking data out is like a legally prohibited minefield due to the risk of data breach.
How Will Big Data Appetite Reconcile with KVKK Limitations?
While the success of AI models depends on the "Big Data" paradigm, i.e., the volume and depth of data, modern data protection law commands exactly the opposite direction, namely "Data Minimization." Article 4 of the KVKK stipulates that data must be "relevant, limited and proportionate to the purposes for which they are processed." This conflict creates a deep-rooted paradox: While an AI model needs to access the widest possible dataset to produce accurate results, the law tells the data controller to keep the data processing activity at a minimum level. This contradiction is an unsustainable equation forcing a choice between innovation and compliance.
Why Does Moving Data to Where the Model Is Carry High Legal Risk?
In traditional machine learning methods, data needs to be physically or digitally moved (data consolidation) to the server where the model is located for the model to be trained. However, this transfer operation is one of the highest-risk activities in terms of KVKK. The main legal risks in this process are:
Data Breach and Violation Liability: As soon as data is taken out of its secure silo, the attack surface expands. A breach on third-party servers directly exposes the data controller (e.g., the hospital) to joint and several liability.
Purpose Limitation Violation: When data goes out, it becomes technically impossible for the data controller to audit that this data is used only for the determined purpose.
Lack of Audit and Verification: Data owners do not have the chance to verify how their data is processed on external servers, which may constitute a violation of the obligation to inform.
Does Anonymization Destroy the Sensitivity Needed by AI?
Data controllers wishing to avoid the risk of data disclosure usually resort to traditional techniques such as Masking or Generalization. However, these techniques are a double-edged sword in the context of AI training.
Utility-Privacy Trade-off: AI models learn by capturing fine patterns in data. Traditional techniques destroy these details by adding noise to the dataset. As the privacy of data increases, its "utility" for model training decreases.
Re-identification Risk: A greater danger from a legal perspective is that the protection provided by these techniques is not absolute. In simply masked datasets, re-identification of individuals is possible through indirect queries (inference attacks). If a person can be re-identified from a dataset by reasonable means, that data is legally still "personal data" and is under KVKK protection.
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Is It Possible to Verify Without Seeing the Data?
The solution to the current data crisis lies not in trying to "extract" data with traditional methods, but in radically changing our approach to data. There is a need for a model where data owners can generate value without taking their data out (data residency), without disclosing content, but by proving the correctness of the computation on that data. The approach of "extracting value without exposing data" marks a new era at the intersection of law and technology.
Zero-Knowledge Proofs (ZKP) allow one party (Prover) to mathematically prove to another party (Verifier) the truth of a statement without revealing the statement itself. This technology eliminates the historical conflict between privacy and provability. While data remains safe in "silos," the insight derived from data can circulate freely and lawfully.
How Does Data Generate Value from Where It Is Confined?
New generation "Zero-Knowledge Database" (ZK-DB) architectures reverse the established "Data-to-Compute" paradigm in the sector and adopt the "Compute-to-Data" model. In this model, data does not move; instead, the query comes to the data. The data controller processes the data on their own secure servers without taking it out and sends only two things out: The result of the operation and the cryptographic proof demonstrating the accuracy of this result.
This approach bypasses leakage risks occurring during data transfer and heavy legal obligations such as "cross-border data transfer" (KVKK Art. 9) via a technical maneuver. Technically, no "personal data" is transferred externally; a mathematical proof derived from the data is exported. Thus, while ownership and security remain with the hospital or bank, the utility obtained from the data becomes shareable with the AI developer.
Can Both Privacy and Transparency Be Provided Simultaneously?
ZK-based systems like PoneglyphDB, unlike traditional databases, present the client not with the data itself, but with the cryptographic proof produced as a result of processing the data. This architecture makes the "Confidentiality" principle absolute by guaranteeing that raw data remains only with the data owner. At the same time, thanks to the proof presented to the client, the transaction becomes "Provable" in that it was performed on the correct data and without manipulation.
This synthesis solves the "utilize without sharing" paradox in the data ecosystem. While the data owner can collaborate without the fear of losing control; the data consumer (e.g., an AI company) becomes sure of the accuracy of the result with mathematical certainty, even though they do not see the data. This is the technical infrastructure of a KVKK-compliant data economy.
Is ZK Technology Synonymous with Blockchain?
The most common mistake made when integrating Zero-Knowledge Proof (ZKP) technologies into the legal world is to identify this technology directly and solely with Blockchain. However, ZKP is a Blockchain-independent cryptographic protocol. This protocol establishes a trustless verification mechanism between parties.
Modern ZK database architectures are "Blockchain-agnostic"; meaning they do not necessarily need a blockchain network to generate and verify proofs. Legally, this means the era of "relying on the counterparty's declaration" (contractual assurance) is closing, and the era of "mathematically proving the accuracy of the transaction" (cryptographic assurance) is beginning.
How Does Collaboration Happen in a World Where Parties Don't Trust Each Other?
In traditional systems, trust is based on institutional reliance on the central authority or service provider. However, in ZK architecture, this trust relationship leaves its place to "cryptographic certainty." The system is built on the assumption of "mutually distrusting parties," which we frequently encounter in law.
The fundamental paradigm shift lawyers need to understand in this new model is this: Trust has been taken from "institutional reputation" and transferred to "mathematical proof." There are two main actors in the model: The Prover holding the data and processing the query, and the Verifier sending the query and auditing the accuracy of the result.
How Does "Data Localization" Eliminate Legal Risks?
The biggest risk in data protection law occurs during the transmission of data from one point to another. The most revolutionary aspect of ZK database architecture is that it embeds the "Confidentiality" principle into the code as a technical necessity. In this architecture, raw data never leaves the Prover's (Host) server.
Since data does not move, the risk of data leakage is minimized and a natural compliance with "Data Locality" laws is achieved. Unlike traditional systems, data is not sent out for training the AI model. From a legal perspective, this strengthens the argument that a technical "transfer" act within the meaning of KVKK Art. 9 has not occurred.
Is a System Possible That Does Not Require the Honesty of the Counterparty?
In this new trust model, the relationship between parties is built on the "Don't Trust, Verify" principle. The Client relies not on the honesty of the Server, but on the unbreakability of the algorithm used.
In this model, three fundamental risks are solved cryptographically:
Fake Database Risk: The Client verifies that the transaction was performed on the committed real data.
Transaction Accuracy Risk: The ZKP protocol proves that SQL queries were correctly executed on arithmetic circuits. If the Server tries to alter the result, the proof becomes invalid.
Data Leakage Risk: Thanks to the "Zero Knowledge" feature, the Client receives only the answer to the question asked and cannot see any other record in the database.
Is "Word" Bond or Is "Math" Bond in the Digital World?
In the legal world, proving that data has not been altered traditionally relies on external authorities such as notarization or timestamps. ZK-based database architectures transform this trust relationship into a mathematical necessity through the concept of "Cryptographic Commitment." The system creates an immutable "digital fingerprint" of the database's current state. This fingerprint legally binds the data owner; even a single bit of change made to the data after the commitment creates a mathematical mismatch and is rejected by the system.
From a legal perspective, this technology operates almost like a digital notary. The generated cryptographic commitment serves as strong electronic evidence under Code of Civil Procedure (HMK) Art. 199, proving that the data existed at a certain moment and has not been altered since. Moreover, a Blockchain network is not necessarily required for this proof; legal trust can also be established via timestamps compliant with Electronic Signature Law No. 5070 or public transparency boards. Thus, while data integrity is protected at the highest level under KVKK Art. 12, sensitive processes such as the "right to be forgotten" in regulated sectors become manageable.
How Does Auditing Transform from a Formal Procedure to a Mathematical Necessity?
In traditional data management law, auditing usually takes the form of reviewing log records or certifying physical server security. However, in ZK systems, auditing moves away from being a formal procedure and transforms into mathematical certainty. In this architecture, the "Third-Party Auditor" is the key legal actor filling the trust gap between the Data Owner and the Client.
While the Client cannot access raw data due to privacy constraints; the Auditor has the authority to examine the raw database within the framework of non-disclosure agreements and legal mandates. The Auditor steps in as the bridge between the digital world and physical reality, acting as a sort of "digital expert witness" from the perspective of Turkish law.
How is Digital "Certified True Copy" Approval Given Cryptographically?
The Auditor's fundamental duty is to certify that the publicly announced cryptographic commitment overlaps with the real and current state of the database. In this process, the Auditor examines the raw database on the data owner's servers on-site and verifies the authenticity of the data. Then, they personally run the polynomial commitment scheme used by the system on this original data to calculate a value.
The Auditor compares the value they calculated with the commitment value declared by the Data Owner. If these values match, this legally means: The database on which the Client will query and receive proof is the real database seen and approved by the auditor. This mechanism is the cryptographic equivalent of the "certified true copy" approval in the legal world; however, the certainty here is absolute and mathematical, leaving no room for human error.
Can the Data Controller Use Fake Data to Manipulate Results?
The biggest fear in data sharing scenarios is the risk of the Data Controller running queries on a manipulated "Bogus Database" instead of the real database. ZK architectures make this attack technically impossible thanks to the "Binding" property of the "Cryptographic Commitment" mechanism.
In the system, the database commitment is embedded into the circuit processing the SQL query. If the data owner uses a fake database, the commitment of this database will inevitably be different from the original commitment. When the Client tries to verify the proof generated from fake data with the original commitment they hold, the mathematical operation fails. This situation means fraud cannot be committed due to "technical impossibility" in law and serves as a revolutionary assurance in the law of evidence.
The most fundamental knot to untie in the integration of Zero-Knowledge Proof (ZKP) systems into the legal world is the legal ontology of the "Proof" π and the "Query Result" (R) verified by this proof. The "Zero Knowledge" claim in technical literature and the "Anonymity" status in legal doctrine are not always synonymous, and this distinction is of vital importance.
Can a Mathematical Proof File Be Legally Considered "Personal Data"?
In traditional encryption methods, encrypted data is legally still considered "personal data"; because someone with the key can reverse it (reversible). However, the proof generated in ZK architecture is not an encrypted text, but a mathematical "certificate" regarding the accuracy of the computation.
This situation creates a gray area regarding the legal nature of the data. Although the proof file is meaningless on its own, when evaluated within a context and together with a query result, its legal status stands on a fine line between "anonymous data" and "pseudonymous data."
Is It Possible to Represent Data Without the Data Itself?
In the technical architecture of ZK-based databases, the "Proof" never contains the raw data (Witness) in the database itself. Instead, it contains a sequence of numbers demonstrating the expansions of certain points and that these points comply with arithmetic circuit constraints on a plane where data is expressed as mathematical polynomials.
Legally, this output should be classified as "Derived Data" or a kind of technical "Meta-Data." When taken alone, the proof does not have the capability to identify any natural person and does not carry information "relating to an identified or identifiable natural person" within the meaning of KVKK Art. 3. This is the key to ensuring legal compliance while protecting the commercial value of data.
Does Using ZKP Completely Zero Out the "Re-identification" Risk?
Recital 26 of the General Data Protection Regulation (GDPR) and the opinions of the Article 29 Working Party (WP29) stipulate that for data to be considered anonymous, "re-identification" must become impossible by reasonable means. WP29 has determined three main risk criteria in the anonymity test: Singling out, Linkability, and Inference.
Categorically counting the outputs produced by ZK-based systems directly as "Anonymous Data" and completely excluding them from the scope of KVKK is a legally risky approach in light of these tests. This data should be evaluated under the safer harbor status of "Pseudonymous" Data. Although pseudonymous data is legally still considered personal data, since it possesses a much higher technical security level compared to unencrypted (clear) data, it is a strong factor in favor of the data controller in risk analysis.
Does Mathematical Difficulty Provide Legal Security?
The most critical technical requirement sought for the validity of anonymization techniques is "Irreversibility." That is, it must be impossible to reach the original data by reversing the process applied to the data. New generation ZK-SQL architectures provide this requirement at the level of "Computational Security."
Polynomial Commitment Schemes used in these systems rely on cryptographic difficulties that are nearly impossible to solve (e.g., Discrete Logarithm Problem). For a malicious actor to reach raw data by "breaking" the proof they hold would require an operation taking as long as the age of the universe with current supercomputers. Legally, this situation means "Mathematical Irreversibility" and meets the "technical measures" obligation under KVKK Art. 12 at the highest standard.
Does Zero-Knowledge Proof Mean "Zero Risk"?
The most frequently misunderstood aspect of ZKP technologies is the assumption that the phrase "Zero Knowledge" in the system's name guarantees the privacy of all outputs produced. However, the ZKP protocol only ensures the privacy of the computation and the process (that the database is not copied); the "Query Result" (R), which is the output of the system, may carry information regarding the content of the data.
While ZKP technology hides "how the data is processed," it may not hide "what the processed data says." This situation is termed "Inference" risk in the legal discipline. An AI model or an attacker can access hidden data by combining information that seems harmless piece by piece.
Can Personal Data Phishing Be Done with Specific Queries?
ZK systems supporting flexible SQL queries do not possess an innate immunity against "Singling out" risks. Even if the Client cannot see the entire database, they can reduce the result to a single individual by using extremely specific filters (WHERE clause).
For example; a query like "What is the diagnosis of the patient who is 42 years old, works at branch X, and was admitted to the hospital yesterday?", even if verified with ZKP, exposes sensitive health data belonging to a specific person via the returned result (e.g., "Heart Failure"). In this scenario, ZKP does not anonymize the data; on the contrary, it "certifies" that the exposed data belongs to that person. Legally, this is an explicit disclosure of special category personal data and must be prevented.
How to Strike a Balance Between Data Accuracy and Privacy?
The legal and technical solution to this paradox lies in combining technological layers. It is mandatory to support ZK architectures with "Differential Privacy" (DP) techniques to minimize data leakage risks. DP makes it impossible to understand whether an individual exists in the dataset by adding a mathematically calculated "Noise" to the query result.
Legally, the ideal architecture is the "Double Shield" approach:
ZKP: Guarantees "Computational Integrity" (that the transaction is done correctly).
DP: Guarantees "Result Privacy" (that no one can be profiled).
The integration of these two technologies technically eliminates "Inference" and "Singling out" risks. This structure is the most competent example in the sector of the "Privacy by Design" principle under KVKK Art. 12 and GDPR Art. 32/25.
Explore our expert legal consulting for Zero-Knowledge (ZK) technology in banking, ensuring KYC/AML compliance and robust data privacy.
Should Data Processing Stop If Obtaining Explicit Consent Is Impossible?
The biggest impasse of the data-driven economy is the legal gap between the initial purpose of data collection and the secondary purpose of use. Obtaining "Explicit Consent" individually from thousands of patients or customers for AI training is operationally close to impossible. At this point, "Legitimate Interest of the Data Controller," regulated in KVKK Art. 5/2-f, is the most vital legal basis for the continuity of the data processing activity.
Can Technology Shift the Legal Balancing Test in Favor of the Data Controller?
In the Balancing Test (LIA) applied for the "Legitimate Interest" condition in legal doctrine, the scales usually tip in favor of the data subject in traditional methods. However, the use of Privacy Enhancing Technologies (PETs) such as Zero-Knowledge, radically changes this balance.
Thanks to the ZK protocol, the researcher or model developer (Verifier) never sees the individuals' raw data; they only see the model's weights or the statistical result. Data is not "shared," the "insight" of the data is shared. Therefore, the interference with the data subject's rights is minimized. This high level of security allows the data controller's legitimate interest to outweigh the data owner's expectation of privacy and makes data processing possible without the need for explicit consent again.
Can Using Data for Another Purpose Become Lawful?
The "Purpose Limitation" principle regulated in KVKK Art. 4 generally prohibits the use of data for purposes other than the initial purpose of collection. However, the law allows secondary purposes "compatible" with the initial purpose (e.g., scientific research or statistics). ZK architecture serves as a critical legal "assurance" in this compatibility test.
In a ZK-based architecture, the data controller can develop this strong argument: "We are testing the accuracy of a model using only its mathematical properties, without taking the data out and without disclosing its content." Since the integrity and confidentiality of the data are preserved, the thesis that the processing activity does not create a "purpose violation," but rather generates value by protecting the data, becomes legally stronger.
Can Information Cross Borders Without Data Crossing Borders?
The conflict between the borderless nature of the global digital economy and the "digital sovereignty" reflex of national data protection laws is felt in cross-border data transfer regimes. Especially in countries like Türkiye where the data localization tendency is high, taking data abroad is one of the most legally costly and risky processes.
What Should Companies Caught Between Global Technology and Local Laws Do?
In Turkish law, KVKK Art. 9 generally ties the transfer of personal data abroad to "Explicit Consent" or Board permission. Additionally, there is an obligation to store data within the borders of Türkiye in critical sectors such as finance and health. However, the most advanced AI models and cloud infrastructures are usually located abroad.
In traditional architecture, for a Turkish company to send its data to a global AI model for analysis, the data physically needs to leave Türkiye and be copied to servers abroad. This "data migration" is a "transfer" act pursuant to KVKK Art. 9 and puts the data controller under a heavy legal burden. This situation creates a de facto "cloud computing impasse."
Is Receiving the Result Without Sending the Data a Legal Solution?
New generation ZK database architectures have the potential to overcome the KVKK Art. 9 barrier with a technical maneuver: Data is not exported, the mathematical proof generated from the data is exported. In this model, raw data remains on the Prover's (Host) local server within the borders of Türkiye.
The AI company abroad sends a query to the server in Türkiye and in return receives only the query result and the ZKP proof showing the accuracy of the transaction. The critical legal distinction is this: The generated Proof π is not the data itself, but a cryptographic meta-data regarding the accuracy of the computation. Therefore, sending π abroad may not be considered a "transfer of personal data" under KVKK Art. 9 as long as the anonymity of the data is ensured. This method is the most sustainable way to integrate into the global value chain without compromising data sovereignty.
Is the Era of "Garbage In, Garbage Out" Over for Legal Compliance?
The European Union Artificial Intelligence Act (EU AI Act), accepted as the constitution of digital transformation, classifies AI systems with a risk-based approach. In this classification, systems directly affecting human life and fundamental rights (health, transportation, recruitment, credit scoring, etc.) are evaluated in the "High-Risk AI" category.
Strict "Data Governance" standards have been introduced regarding datasets used for training these systems. New generation ZK (Zero Knowledge) architectures offer a critical infrastructure in technically meeting these strict standards and implementing "Responsible AI" principles. The legislator is now concerned not only with the accuracy of the result but also with the quality of the data used to reach that result.
Who is Legally Responsible for an AI Trained with Flawed Data?
Article 10 of the EU AI Act stipulates that datasets used in the training, validation, and testing of high-risk AI systems must be "relevant, representative, free of errors and complete." This situation, summarized in the legal world by the principle "Garbage In, Garbage Out," is no longer just a technical problem but a legal compliance obligation.
If a hospital or technology company is going to use its data to train an AI model that diagnoses cancer, it must prove the quality and accuracy of this data. The data remaining in a "black box" (silo) does not eliminate this responsibility; on the contrary, it gives rise to the obligation to prove the quality of the dataset.
Can a Model Trained with Artificial Data Be Legally Considered "Accurate"?
"Synthetic Data" production, frequently resorted to due to data privacy concerns, harbors two major risks legally and technically. The technical risk is that data not reflecting the real world causes "Model Collapse." The legal risk relates to the principle of personal data being "Accurate and kept up to date where necessary," regulated in KVKK Article 4/2-b.
In case an AI model trained on fabricated synthetic data instead of a real patient's data makes a wrong diagnosis on a real patient, the claim that the "data accuracy" principle was violated and the data controller is at fault will come to the agenda. ZK technology solves this dilemma by proving the "accuracy" of the data without revealing the data "itself." In this way, AI models can be trained not with simulations, but with "real, raw, and verified" data in the database.
Is It Legally Possible to Audit That Data Is "Clean" Without Seeing It?
The most critical question lawyers must ask is: "How can we audit that data is of high quality (error-free, complete, consistent) without seeing it?" The "Arithmetic Circuits" found in ZK-DB architectures transform this legal audit into mathematical certainty.
For example, in a pediatric dataset, patients' ages must be in the "0-18" range. The "Lookup Table" mechanism in the ZK infrastructure cryptographically proves that each record is within this range without disclosing the data. Likewise, circuits detecting null values or logical inconsistencies (e.g., Discharge Date < Admission Date) in the dataset guarantee that the dataset is "clean." This technical structure gives the supervisory authority this assurance: "I haven't seen the data, but I have mathematically verified 100% that there are no errors in this data."
Can Legal Audit Be Conducted Without Disclosing Trade Secrets?
For high-risk AI systems, "Transparency" and "Accountability" are not arbitrary choices but a legal obligation. However, completely opening algorithms and sensitive data, which are of trade secret nature, even for audit purposes, constitutes a major commercial risk for companies.
How to Make the Decision Mechanism of the Algorithm Transparent?
Traditional AI models work as a "Black Box" where it is unknown how the decision is taken. The legal system, however, demands "justification" of decisions. New generation ZK systems make this black box transparent with the "Provability" feature they offer.
The system produces an output (Proof) proving that the result of a query was generated on the committed data with the determined algorithm. This output is the strongest part of the technical documentation requested under the AI Act. An auditor, by examining the generated proof, can audit at the code level which data was used during the training of the AI model, whether the data was altered, and whether the process was done correctly. This approach transforms the "Verifiable AI" concept into a legal standard.
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What Does Technology Offer for Bias Detection and "Regulatory Sandboxes"?
ZK technology stands out as a strategic "compliance technology" in two critical articles of the EU AI Act. The law mandates the detection of geographical, behavioral, or functional shortcomings (bias) in training data (Article 10). The aggregation and counting operators of ZK systems can prove the demographic distribution of the dataset without disclosing the data. For instance, a company can prove the proposition "The female/male ratio in our training set is balanced" without opening the data.
Additionally, the law envisages the establishment of "Regulatory Sandboxes" to encourage innovation (Article 53/54). Processing of personal data is allowed in these sandboxes provided that the security of the data is ensured. ZK architecture and the "Zero Knowledge" guarantee it offers meet this legal condition "by design." Especially thanks to the recursive proof capability, a common, powerful, and legal model can be trained by combining only the proofs, without combining the data of multiple institutions.
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Is the Era of "Opinion" Ending in Digital Disputes?
In data-based disputes, data integrity and the accuracy of processing procedures are the most critical elements determining the fate of the case. In the traditional legal system, proving that a database record has not been altered relies on external elements open to manipulation, such as log records, timestamps, and expert examinations.
ZK (Zero-Knowledge) technologies transform this "opinion-based" proof regime into a new proof regime "based on mathematical certainty." This system radically changes the data controller's burden of proof and liability regime through cryptographic commitments showing the ownership and integrity of the data.
Is It Possible to Prove "Flawlessness" with Digital Fingerprints?
Cryptographic commitments and proof files generated by new generation ZK databases are in the nature of "documents" pursuant to Code of Civil Procedure (HMK) Art. 199. Although they are not directly considered "Conclusive Evidence" (Deed) as they do not contain a Qualified Electronic Certificate (NES), they constitute very strong "Discretionary Evidence" before courts thanks to their mathematical certainty which can be confirmed by expert examination.
Moreover, these technical outputs can be elevated to "Conclusive Evidence" status with an "Evidential Contract" (HMK Art. 193) to be made between parties in commercial relationships (B2B). This technological infrastructure also changes the balances in the law of evidence. While it is difficult for the data controller to prove "that they did not do something" (negative fact) in allegations of data leakage or manipulation; ZK technology converts this into a positive proof thanks to its "Binding" property. By presenting commitments that are mathematically impossible to alter, the data controller proves with "non-repudiation" certainty that the data has not been tampered with.
Are the Boundaries Between Data Controller and Data Processor Disappearing?
In traditional data management law, the distribution of roles is constructed upon physical access. The party hosting or processing the data is usually considered the "Data Processor" and bears heavy responsibilities. However, the ZK model radically changes this hierarchical relationship by offering an architecture where data "does not go," only the "proof" goes.
What is the Legal Title of the Party Performing "Blind Processing"?
The greatest innovation brought to legal dogmatics by ZK architecture is the concept of "Blind Processing." The Client (Verifier) never accesses raw data; they only receive the Proof π showing the accuracy of the transaction and the Query Result (R) which is the transaction output.
If the Query Result does not contain personal data (e.g., if it is an anonymous statistic), the Client does not gain the title of "Data Processor" in any way. Because they have no access to personal data, do not store it, and cannot modify it. The Client only performs a mathematical verification. In this scenario, since the data owner (Host) keeps the data within their own structure and performs the processing activity on their own hardware; they are the sole party acting as both the Data Controller and the processor in the technical sense.
Are Heavy Data Processing Agreements Becoming History?
This architecture produces a revolutionary legal result in the sector: A radical lightening of the need for and scope of Data Processing Agreements (DPA). Currently, contracts containing very heavy responsibilities are signed because the AI company "sees" the data.
However, in the ZK model, the AI company does not receive the data, only the proof. This means "Zero Liability Transfer." Since the data never leaves the secure area of the hospital or bank, the institution remains solely responsible for data security. The AI company is relieved of the risk of being the perpetrator in a data breach. ZK technology moves cloud computing contracts from the axis of "Data Storage/Processing" to the axis of "Verification Service."
Can ZKP Be the "Gold Standard" in Data Security?
KVKK Article 12 imposes an obligation on the data controller to "take all necessary technical and administrative measures" to ensure data security. While traditional measures (firewall, antivirus) provide perimeter security; ZK architectures like PoneglyphDB move the concept of data security from the level of "blocking access" to the level of "mathematical impossibility."
How is the Privacy by Design Principle Embedded into Code?
"Privacy by Design," the universal principle of data protection law, is embedded into the core codes of ZK-based systems. While security depends on administrator initiative in traditional systems, here security is embedded in mathematical circuits. Raw data cannot technically leave the server; because the system architecture is built not on transferring data, but on transferring the proof generated from data.
Legally, since this eliminates the risk of "reversing data" at the level of technical impossibility, it should be evaluated as one of the highest-level technical measures in the Board's guide. Data ceases to be a stealable file and transforms only into a verifiable abstract value.
Does Implementing Maximum Measures Mitigate Penalties?
No system is 100% secure; however, when a data breach occurs, the administrative sanction the data controller will face is determined by the nature of the measures taken. In Data Protection Impact Assessment (DPIA) processes, the use of ZK radically reduces the "Residual Risk" score.
In case of a possible leak, the mathematical structure of the data prevents attackers from making sense of the data. In an investigation, the institution can defend itself by stating, "We didn't just put a password to protect the data; we used ZK technology which makes data mathematically inaccessible." This defense demonstrates that the "duty of care" has been exceeded and should be considered as a strong "Mitigating Factor" in a potential administrative fine.
The conflict between "Data Scarcity" and "Data Privacy" in the AI era cannot be solved by traditional prohibitive methods. Instead of being a braking mechanism that falls behind technology and just says "stop," the law must be an "Enabler" that understands technologies like Zero-Knowledge and makes them a part of regulation.
Fully opening data is legal suicide, while keeping it closed is commercial inefficiency. A third way is now possible: Opening the accuracy of the data without opening the data itself.
As Genesis Hukuk, we do not just apply today's laws; we are building the legal architecture of tomorrow's data economy. We are ready to be your strategic partner to open your data treasure to the AI world without taking legal risks, combine your KVKK compliance with technical innovation, and determine the rules of this new digital consensus.
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