Publication date: February 12, 2026
In recent years, the importance of artificial intelligence (AI) in drug development, evaluation, and monitoring has grown significantly. AI technologies have the potential to accelerate research, improve predictions of drug efficacy and safety, and reduce the need for animal testing. At the same time, their use presents new challenges. AI models can make errors, be susceptible to unforeseen risks, or use data in a non-transparent manner. To fully realize the benefits of AI while minimizing risks, it is essential to establish clear and common principles for the use of these technologies. In response to these challenges, the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) have jointly developed ten principles of good practice for the use of AI in the drug lifecycle. This document is fundamental and a framework, not a binding legal regulation – it provides general directions and guidelines that should guide drug manufacturers, applicants, and regulators. These principles indicate how AI should be designed and used to ensure it is ethical, safe, transparent, and based on reliable data. The ten principles also identify areas where international regulators, standards-setting organizations, and other collaborating entities can work together to promote good practice in drug development. These areas of collaboration include: conducting scientific research, creating educational tools and resources for market participants, international harmonization, and developing consensus standards. To facilitate initial analysis, these principles can be grouped into three logical pillars. Principles 1-3 address organizational foundations and people, focusing on interdisciplinary team expertise and ensuring that AI remains under human control within specific, established governance processes. Principles 4-7 address technical quality and model integrity, addressing the “heart” of the technology. Principles 8-10 address accountability and lifecycle, defining standards for documentation, clear communication with users, and continuous monitoring of the model after its implementation. Below is a detailed summary of the 10 principles of good practice for AI in the drug lifecycle:
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Publication date: February 6, 2026
Cybersecurity certifications are designed for IT professionals, including system and network administrators, security specialists, engineers, and those aspiring to these roles, to validate their knowledge and practical skills in protecting against digital threats. The certification also covers ICT products, services, and processes, and aims to inform consumers about the level of digital security and support Polish companies in European markets.
On August 28, 2025, the Act of June 25, 2025, on the national cybersecurity certification scheme entered into force, implementing Regulation (EU) 2019/881 of the European Parliament and of the Council of April 17, 2019, on ENISA (the European Union Agency for Cybersecurity) and cybersecurity certification in information and communication technologies, and repealing Regulation (EU) No 526/2013 (Cybersecurity Act) (OJ L 151, 7.06.2019, p. 15 and OJ L 2025/37, 15.01.2025).
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Publication date: February 2, 2026
According to an analysis by lawyers from KG LEGAL KIELTYKA GLADKOWSKI, legal concepts such as incident and cyberattack are key elements in the EU cybersecurity and data protection law ecosystem. The fragmentation of cybersecurity law into various sectoral legal acts necessitates a comprehensive analysis of the coherence of all the legal acts comprising this ecosystem. This article demonstrates that the legal layer of cybersecurity in an incident is a highly sensitive issue from the perspective of the responsibility to protect, and therefore, responsible entities should examine the differences in the legal scope of application of individual acts. These concepts are intuitively understood but in legal practice are only superficially identical and lead to different regulatory obligations. The following summary is original and creative and can be used by entities to properly analyze their obligations under current EU law.
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Publication date: January 28, 2026
In Poland there is planned the e-contract system – central IT system operated by the Ministry of Family, Labor and Social Policy. It will enable the electronic conclusion and management of employment contracts, contracts of mandate, contracts for specific work, and other HR documents, integrating with public registers (ZUS, CEIDG, KAS). Accessible through praca.gov.pl, the system will simplify HR processes, automate ZUS filing, provide access to documentation for the National Labor Inspectorate (PIP), and store and manage full employee records online, becoming a tool for the comprehensive digitization of the labor market.
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Publication date: January 22, 2026
A hull board (or “hull”) is a management body of a legal person (e.g., a company, association, foundation) that formally exists, but its numerical composition has fallen below the minimum number required by the statute or agreement, preventing it from effectively adopting resolutions, representing the organization, and managing its affairs, which may result in the need to appoint a curator.
The provisions of the Polish Commercial Companies Code in the regulations concerning capital companies provide that the company’s management board consists of one or more members. In capital companies, this is a mandatory body, but it may also be appointed optionally in a professional partnership. The company’s articles of association or statute may specify how many people will comprise it, most often by indicating a specific number of management board members or defining it using a range of numbers (e.g. the management board consists of 3-5 people). A problematic situation arises when the number of management board members falls below that required in the articles of association or statute. Then we are dealing with the so-called hull board where a corporate structure is in a state of inconsistency between the established composition of a body and its proper staffing quantitatively.
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