Publication date: November 04, 2026
Technical debt, GDPR and NIS2 – how yesterday’s shortcuts turn into tomorrow’s liability
Why technical debt arises
Technical debt is a phenomenon that sooner or later affects almost every IT project. It consists in choosing a solution that is quicker to deliver but inferior in technical quality. A team usually “takes out” this loan under pressure from the business side, for which the key metric is time-to-market – the sooner the product reaches the market, the better.
In the short term this brings a quick win, but in the future the company has to start paying the “interest”. It shows up as ever higher system maintenance costs, a slowdown in the pace of work, and the mounting frustration of developers who, instead of developing the product, must keep fixing the same defects over and over. Although the term “technical debt” appears nowhere in legislation, concepts such as due diligence and the adequacy of technical measures in practice force companies to keep that debt from spiralling out of control.
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Publication date: November 02, 2026
A growing number of businesses are investing in artificial intelligence and expect to recover the cost of those projects through the research and development (R&D) tax relief. The Polish tax authorities and the Supreme Administrative Court, however, draw a clear line: integrating an off-the-shelf AI model or implementing a commercially available system – however advanced or “self-learning” – is not R&D activity. The relief is available only to those who create new technology themselves and bear genuine research risk. We explain how the tax authorities’ “two-step test” works, what the Frascati Manual and the case law of the Supreme Administrative Court say, which costs in IT projects qualify and which are traps – and how to document the work so that the deduction survives a tax audit.
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Publication date: October 05, 2026
The loss or corruption of data in a business is rarely “just” a technical problem. In practice it is a serious legal and financial crisis: production downtime, loss of trust among business partners and, in extreme cases, administrative fines running into millions. The scope of liability, however, depends above all on what kind of data has been lost or corrupted. The law treats the loss of source code or technical documentation quite differently from a leak of employee records or a customer database. The first step in assessing liability for a breach of the integrity or availability of data is therefore its unambiguous legal classification.
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Publication date: October 05, 2026
Implementing artificial intelligence in an organisation does not begin with choosing a tool, but with answering the question of who we are in the AI value chain and what we are responsible for. This article sets out the relationship between the GDPR and the AI Act: from the roles of provider, deployer and controller, through algorithmic risks, the limits of lawfulness in the workplace and the rights of individuals, to the DPIA, the FRIA and the AI Governance model. It reflects the legal position following the entry into force of the Polish Act on Artificial Intelligence Systems and the postponement of the AI Act application dates by Regulation (EU) 2026/1744.
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Publication date: September 07, 2026
1. Factual background
The subject of this analysis is a marketing model for a physical product based on the use of influencer marketing combined with the environment of the game Minecraft. Online creators promote branded toys containing candy, widely available in retail sale, including in popular discount store chains. The campaign also involves creating and making available within Minecraft virtual counterparts of the promoted products, which function, inter alia, on servers, on maps and through game modifications.
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