Cybersecurity 2030: The Synergy Between Machine Learning and Generative Artificial Intelligence

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dc.contributor.authorMarques da Silva, Mario
dc.contributor.authorCorreia, Fernando
dc.contributor.authorOrrillo, Héctor
dc.contributor.authorCruvinel, Laercio
dc.date.accessioned2026-09-28T10:36:09Z
dc.date.available2026-09-28T10:36:09Z
dc.date.issued2025en_US
dc.date.updated2026-01-18T13:22:18Z
dc.description.abstractArtificial Intelligence (AI) and Machine Learning (ML) have rapidly become transfor-mative forces across various fields and industries, including official statistics. The ability of AI-ML systems to process large datasets, identify patterns, and generate predictive insights has opened up new possibilities for statistical agencies. These technologies offer the potential to enhance efficiency, accuracy, and relevance in statistical production, enabling data-driven decision-making at unprecedented scales.
dc.description.version991A-44CC-149B | Héctor Orrillo
dc.description.versionN/A
dc.identifier.doi10.1007/978-3-032-01429-0_48en_US
dc.identifier.slugcv-prod-4666917
dc.identifier.urihttps://hdl.handle.net/11144/8063
dc.language.isoN/Apor
dc.rightshttps://purl.org/coar/access_right/c_abf2en_US
dc.subjectEthical principles
dc.subjectOfficial Statistics
dc.subjectArtificial Intelligence
dc.subjectResearch Ethics Reviews
dc.subjectResearch Ethics Committees
dc.subjectAI-related challenges
dc.titleCybersecurity 2030: The Synergy Between Machine Learning and Generative Artificial Intelligenceen_US
dc.typeBook part
oaire.citation.conferenceDate2025-09-17
oaire.citation.conferencePlaceETHICOMP 2025
oaire.citation.locationPortugalen_US
oaire.citation.titleLicense Springer Nature - Cybersecurity 2030: The Synergy between Machine Learning and Generative AIen_US

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