Dual-use Technology: Cross-sector Cooperation in the Cybersecurity Sector
Bibliographic record
Abstract
This document is the third policy brief resulting from the letter of intent signed during CYBERSEC EXPO & FORUM 2024 on June 19th in Kraków, in the presence of Deputy Prime Minister and Minister of Digital Affairs Krzysztof Gawkowski. The agreement was concluded between the Kosciuszko Institute and the European Cyber Security Organisation (ECSO) regarding the organization of a series of events focused on the priorities of digital and technological policy during Poland’s Presidency of the EU Council in 2025. The third meeting in the Road to the Polish Presidency series was dedicated to addressing challenges and overcoming obstacles in dual use technology. In recent years, dual use technologies have become increasingly critical for economic competitiveness, national security, and technological sovereignty. Rapid advancements in areas such as artificial intelligence, quantum computing, and advanced digital systems offer transformative potential. However, they also introduce challenges, including regulatory fragmentation, export control complexities, and the need for effective collaboration across sectors. Furthermore, geopolitical tensions and evolving global security threats have highlighted the importance of fostering a balanced, coordinated, and forward-looking approach to dual use innovation and deployment. Drawing from a meeting held on November 26, 2024, attended by representatives of the Ministry of Digital Affairs of Poland, the European Cyber Security Organisation (ECSO), the private sector, academia, and the Kosciuszko Institute, together we were able to identify key challenges and solutions surrounding regulatory harmonization, export controls, cross-sector collaboration, and innovation support for dual use technologies. The developed recommendations represent a significant step in building a digital and secure society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".