The shortage of high-quality, representative data is one of the most acute problems hindering the development of artificial intelligence. In the context of the Ukrainian market, this is particularly noticeable, as access to large, annotated, and confidential datasets is limited both by economic factors and stringent regulatory requirements. It is in these conditions that synthetic data for AI in Ukraine becomes not just an alternative, but a vital infrastructure niche, opening new opportunities for domestic startups and innovative companies. This direction allows for bypassing limitations related to privacy and the bias of real-world data, while ensuring high quality for training AI models.
Data shortage for AI: why real-world datasets fail to meet the needs of the Ukrainian market?
The problem of data scarcity for AI training is global, but in Ukraine, it takes on specific characteristics. Limited access to large, clean, and annotated datasets is a significant barrier to developing complex AI solutions. Even when data exists, its collection, cleaning, and annotation require significant financial and time investments, which is often unfeasible for many young companies and startups.
Furthermore, privacy and regulatory issues play a critical role. Laws such as GDPR in Europe, as well as national personal data protection standards, significantly limit the use of sensitive information. Risks of data leaks and massive fines block innovation, forcing companies to seek safer alternatives. This is especially relevant for sectors that process personal data, such as healthcare, finance, or public administration.
Existing real-world data often contains biases and imbalances, leading to unethical or ineffective AI models. For example, data collected from one demographic group may be unrepresentative of others, causing the system to function incorrectly. This creates serious challenges for developers striving to create fair and accurate algorithms. For Ukrainian startups, these barriers slow down the development of AI products and reduce their competitiveness in the global market.
Synthetic data for AI: infrastructure potential and business models
Synthetic data consists of artificially generated information sets that are statistically equivalent to real data but contain no actual personal or confidential information. This opens the door to innovation, allowing for the development and testing of AI models without compromising privacy. Generation technologies, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, are capable of creating high-quality synthetic data that accurately reflects the properties of the original sets.
This capability forms a new infrastructure niche, which can be called "data factories." Business models for such factories are diverse: from Data-as-a-Service (DaaS), where companies provide access to ready-made synthetic datasets, to licensing generative models or custom data development tailored to specific client needs. Global players like Syntho or Gretel.ai are already actively working in this direction, offering solutions for various industries. The high cost of collecting and annotating real data creates significant profit potential in the synthetic data segment, as once a generative model is created, its scalability is almost unlimited.
Synthetic data is of particular value to regulated industries where privacy is a priority. In finance, medicine, and the defense sector, it allows for the development and testing of new AI solutions without violating data protection laws. This is critically important for Ukrainian Military Tech, where the need for rapid and secure development of innovative systems is extreme. Synthetic data can accelerate the creation of intelligent systems for intelligence, logistics, or medical assistance for the military.
Ukraine has the potential not only to consume but also to export solutions in the field of AI infrastructure, particularly synthetic data. Companies specializing in generating high-quality and validated synthetic datasets can attract significant investment into deep tech startups. This creates new jobs for highly qualified professionals and facilitates the integration of the Ukrainian IT industry into global value chains.
Building trust and validating synthetic data quality
While synthetic data offers many advantages, a key challenge remains building trust and validating its quality. For clients to be confident in the representativeness and utility of synthetic sets, it is necessary to guarantee their statistical equivalence to real data. This means that the distribution of variables, correlations between them, and other important statistical properties must be preserved.
Various methods are used to validate the quality of synthetic data. These include comparing distribution metrics, visual analysis, and evaluating the performance of AI models trained on synthetic data compared to those trained on real data. It is also important to ensure that synthetic data contains no "traces" of the original data that could allow for the re-identification of individuals. This requires deep expertise in cryptography and cybersecurity.
The lack of generally accepted standards for evaluating synthetic data is a problem. Therefore, independent verification and certification could be a key factor in increasing trust. Creating open benchmarks and developing transparent evaluation methodologies will allow supplier companies to demonstrate the quality of their solutions. According to Serhiy Balashuk, an innovative approach to information security opens up new protection opportunities, and synthetic data is one such tool that requires careful validation to ensure maximum efficiency and security.
This aspect is particularly important for GovTech solutions, where data accuracy and security are absolutely critical. For example, the company InBase, which specializes in electronic document management and business process automation for government structures, can use synthetic data to test new features of its systems, ensuring a high level of protection for confidential information.
Development paths for Ukrainian AI startups in the synthetic data niche
For Ukrainian founders looking to occupy a niche in the synthetic data market, developing technological expertise is key. The demand for specialists in ML, generative models, cryptography, and cybersecurity is extremely high. It is necessary to invest in R&D and form strong teams capable not only of developing advanced generative models but also of ensuring a high level of privacy and quality for the generated data.
Investment landscape for deep tech startups in the field of synthetic data is attractive. The global synthetic data market is estimated to be growing at a significant pace, with projections to reach multi-billion dollar figures in the coming years. This creates favorable conditions for attracting venture capital. However, to achieve this, it is necessary to demonstrate a clear MVP (Minimum Viable Product) and proven traction, which indicates the real value of the product for potential clients.
Go-to-market strategies must be well-thought-out. Focusing on specific niches can ensure a faster start and allow for the accumulation of expertise. For example, Ukrainian startups could focus on generating synthetic data for the agritech sector, which is a strong point of the economy, or for the aforementioned defense and fintech sectors. Partnerships with large corporations, government institutions, and even international organizations can become a catalyst for growth.
Furthermore, an important component of success is active participation in international conferences, publishing research, and collaborating with scientific institutions. This will allow not only for sharing experience but also for attracting talent, forming a community, and increasing the authority of Ukrainian developers on the world stage. Developing a support ecosystem, including accelerators and incubators focused on deep tech, is also critical for stimulating innovation in this field.
Overall, the market for synthetic data for AI is not just a technological trend but a strategic opportunity for Ukraine. It allows for not only solving urgent problems with data scarcity and privacy but also positioning the country as a player in the global AI infrastructure market. Ukrainian founders and investors have a unique chance to build sustainable and high-margin businesses that will contribute to the development of the national economy and strengthen technological independence.
Frequently asked questions
What is synthetic data for AI?
Synthetic data is artificially generated data that statistically reproduces the properties of real data but contains no confidential information. It is used for training, testing, and developing artificial intelligence models without privacy risks.
How does synthetic data help Ukrainian startups?
It allows Ukrainian startups to develop and test AI solutions while bypassing problems with access to large volumes of real, high-quality, or confidential data. This accelerates R&D, reduces costs, and opens access to regulated markets.
Why is real-world data not enough for AI training?
Real-world data is often lacking due to high costs of collection and annotation, privacy concerns (GDPR), the presence of biases, or insufficient quantities for rare scenarios. Synthetic data solves these problems by providing controlled and scalable datasets.
Which industries in Ukraine can benefit most from synthetic data?
Primarily the financial sector, medicine, the defense industry, and agritech, where access to real data is limited by regulations or its specific nature. Synthetic data allows for modeling risks, diagnostics, or process optimization without compromising sensitive information.
How much does it cost to generate synthetic data?
The cost varies depending on data complexity, volume, required accuracy, and the technology used. While initial investments in developing generative models can be significant, in the long term, it is cheaper and faster than collecting and processing real data, especially for large datasets.