Cybersecurity and the risks associated with the quantum transition, artificial intelligence, and human factors
DOI:
https://doi.org/10.5753/compbr.2026.55.7498Keywords:
Cybersecurity, Reference Curriculum, Quantum Threat, Emerging Technologies, Human factorsAbstract
This article describes the main research and innovation challenges in the field of Cybersecurity. Building upon the thematic axes defined in the Cybersecurity Bachelor’s Degree Curriculum Reference Framework designed by the Brazilian Computer Society (SBC), we focus on the following topics: the threat of quantum computing, which, at least in theory, might compromise the security properties of several cryptographic algorithms in use today; the risks introduced by emerging technologies, particularly those associated with the development and operation of autonomous and intelligent systems; and the need to reduce risks arising from human interaction with computational systems, through decisions and behaviors that accidentally or intentionally, facilitate attacks. In addition to the technical discussion, we also list ongoing initiatives aimed at addressing the identified challenges.
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ITU-T (2019). Recommendation ITU-T Y.3800 - overview on networks supporting quantum key distribution. Relatório técnico, International Telecommunication Union.
NIST (2023). SP 1800-38B: Migration to Post-Quantum Cryptography. Quantum Readiness: Cryptographic Discovery. National Institute of Standards and Technology.
Pollini, A., Callari, T. C., Tedeschi, A., Ruscio, D., Save, L., Chiarugi, F. e Guerri, D. (2022). Leveraging human factors in cybersecurity: an integrated methodological approach. Cognition, Technology & Work, 24(2):371–390.
Rose, S., Borchert, O., Mitchell, S. e Connelly, S. (2020). NIST SP 800-207: Zero trust architecture. Relatório técnico, National Institute of Standards and Technology.
SBC (2023). Referenciais de formação para o curso de bacharelado em cibersegurança. Relatório técnico, Sociedade Brasileira de Computação (SBC).
Vassilev, A., Oprea, A., Fordyce, A. e Anderson, H. (2024). Adversarial machine learning: A taxonomy and terminology of attacks and mitigations. Relatório técnico, National Institute of Standards and Technology.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
