Creating and developing an AI model requires an unimaginable amount of data. Once input, the model analyzes the information, performs calculations, and draws conclusions based on this data that informs its future operations. Comparing this process to that of humans, one could say that AI “learns” in this way. AI systems are trained on numerous examples and draw model patterns from them, allowing them to predict correct solutions. This process is called “AI training”. Data is currently so expensive and difficult to access that it is estimated that it may be in short supply by 2032. The answer to these problems is synthetic data. This data is generated by the AI itself, which uses parameters from real-world data and randomly generates subsequent scenarios. These scenarios are designed to faithfully reproduce the properties, complexities, and relationships observed in the original data from which they were generated. There are certain risks involved. First of all, it is about creating synthetic data based on erroneous assumptions from real data – with the continuous introduction of new real data, previous errors can be corrected and artificial intelligence will be able to “unlearn” them, whereas if the first synthetic data generated is “contaminated”, each subsequent one will also contain erroneous information. The undoubted advantage of synthetic data is that it can be used to generate subsequent scenarios that may not actually occur at all or very rarely. This is used in industries such as automotive (for simulating traffic scenarios), finance (detecting fraud), and healthcare (detecting and treating rare conditions).
The Polish Ministry of Digital Affairs is establishing a new sectoral cybersecurity incident response team – CSIRT Cyfra. The formal establishment of CSIRT Cyfra is planned for April 2026, with full operational readiness for June 2026. The unit will be responsible for protecting digital infrastructure, and its tasks will include monitoring threats, rapidly responding to attacks, and providing technical support to institutions using digital services.
The capabilities of AI systems are increasingly impressive, largely based on machine learning. This technique allows for the “training” of algorithms by providing vast amounts of data, which in turn leads to the automation of the algorithm and a radical increase in its “cognitive” capabilities, particularly through generalization, drawing conclusions from the obtained data, and predictive language models that allow for highly probable prediction of the next word that fits a given statement or sentence, taking into account its context. Most AI systems are based, to be precise, on a specific variety or technique of machine learning, referred to as an artificial neural network, or deep neural network, a metaphor that approximates the logic of AI systems to the functions of the human brain.
Covering 13 practice areas and 17 jurisdictions – from Albania to Ukraine – the awards were grounded in Legal 500’s independent research, ensuring credibility and impartial recognition across the region.
We would like to share the information that the leader of the Legal 500 CEE Awards 2025 (WARSAW, Thursday 16 October 2025 | The The Westin Warsaw) in the pharma and biotech law (LIFESCIENCE AND HEALTHCARE) category was selected Małgorzata Kiełtyka, partner of KG LEGAL Kiełtyka Gładkowski – Professional Partnership. Attorney Law Firm.
Currently, useful data includes not only specific information organized into rows, columns, or databases, but also data that is not organized in any specifically defined way. This constitutes the majority of data we encounter, including images and text documents such as tweets and blog posts. Thousands of individuals and organizations generate it daily, with little regard for how it can be used. It is precisely thanks to unstructured data that such rapid AI development is possible through machine learning, which involves training algorithms to find patterns and correlations in large data sets.
