This annual report analyzes how cybersecurity policy translates into practical actions, investments, and operational changes within organizations across the EU, particularly those in high-criticality sectors under the NIS2 Directive. The findings, based on a survey of over 1,000 professionals, highlight that while regulatory compliance is the main driver of investment, challenges persist, such as the cyber talent crunch and difficulties with fundamental tasks like patching and security assessments. Key insights from the report show a shift in spending toward technology and outsourcing, and an ongoing concern over ransomware and supply-chain attacks. This ENISA study ultimately aims to inform policymakers by revealing the practical obstacles and shifting priorities faced by entities working to enhance their cyber resilience.
This paper explores the relationship between Artificial Intelligence (AI) and cybersecurity, emphasizing AI's critical role in modern digital defense. The abstract and introduction establish the urgent need for advanced security solutions due to the increasing reliance on digital platforms and the rise of cyber threats. The research specifically examines how AI technologies like machine learning and deep learning enhance threat detection and incident response for organizations. Conversely, the document addresses significant risks associated with AI in security, including algorithmic bias, adversarial attacks, and the threat of deepfake technologies. Finally, the conclusion argues that AI's benefits outweigh its drawbacks when implemented with robust mitigation strategies, such as quantum security and human oversight, ensuring ethical and effective use.
This paper summarizes the use of Extreme Value Theory (EVT) for modeling large insurance claims, particularly within reinsurance, where managing tail risk is paramount.
The core argument is that standard EVT must be adapted to overcome unique actuarial data challenges, including censoring (due to limits/delays), truncation (due to maximum possible losses), and data scarcity.
Key adaptations discussed include:
Truncation and Tempering Models to account for limits or weakening tail behavior.
Censoring-Adapted Estimators (e.g., modified Hill) for incomplete data.
Splicing/Composite Models that combine body and tail distributions (e.g., Mixed Erlang/Generalized Pareto) for a full-range fit.
Advanced Regression and Multivariate Models to incorporate covariates (like climate change effects) and analyze spatial dependencies.
A profound, tailored application of EVT is deemed critical for sound pricing and risk management of catastrophic risks.