artificial intelligence and digital sustainability. Infrastructure, security, and intelligent citizenship

Capítulo 1

Aslan, T., Holzapfel, P., Stobbe, L., Grimm, A., Nissen, N. F., Finkbeiner, M. (2024). Toward Climate Neutral Data centers: Greenhouse Gas Inventory, Scenarios, and Strategies. iScience 28(1):111637. Disponible en: https://pmc.ncbi.nlm.nih.gov/articles/PMC11773490/utm_source=chatgpt.com

Instituto Mexicano para la Competitividad (imco) (2024). Prodesen 2024–2038: El sistema eléctrico mexicano ante el crecimiento de la demanda. Disponible en: https://imco.org.mx/prodesen-2024-2038-el-sistema-electrico-mexicano/

Instituto Federal de Telecomunicaciones (ift) (2024). Índice de Infraestructura Digital. Disponible en: https://centrodeestudios.ift.org.mx/admin/files/indicadores/1751482253.pdfCentro de Estudios

Khosravi, A., Sandoval, O. R., Taslimi, M. S., Sahrakorpi, T., Amorim, G., & García Pabón, J. J. (2024). Review of Energy Efficiency and Technological Advancements in Data Center Power Systems. Energy and Buildings, 323, Article 114834.Disponible en: https://doi.org/10.1016/j.enbuild.2024.114834

Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating Global Data Center Energy-Use Estimates. Science, 367(6481), 984–986. Disponible en: https://doi.org/10.1126/science.aba3758

McKinsey & Company (2024, septiembre 17). How Data centers and the Energy Sector can sateai’s Hunger for Power. Disponible en: https://www.mckinsey. com/industries/private-capital/our-insights/how-data-centers-and-the- energy-sector-can-sate-ais-hunger-for-power

Mondal, S., Faruk, F. B., Rajbongshi, D., Efaz, M. M. K., & Islam, M. M. (2023). geeco: Green Data centers for Energy Optimization and Carbon Foot- print Reduction. Sustainability, 15(21), 15249. Disponible en: https://doi. org/10.3390/su152115249

Murino, T., Monaco, R., Nielsen, P. S., Liu, X., Esposito, G., & Scognamiglio, C. (2023). Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations. Energies, 16(15), 5764. Disponible en: https://doi.org/10.3390/en16155764

Rodríguez-Vizuete, J. D., Viteri-Ojeda, J. C., & Villa-Feijoo, A. L. (2024). Adop- ción de tecnologías sostenibles en infraestructuras de tecnologías de la información. Revista Científica Ciencia y Método, 2(1), 55-67. Disponible en: https://doi.org/10.55813/gaea/rcym/v2/n1/31

Ramachandran, K., Stewart, D., Hardin, K., & Crossan, G. (2024, noviembre 19). As Generative ai asks for more Power, Data centers seek more Reliable, Cleaner Disponible en: https://www.deloitte.com/us/en/insights/indus- try/technology/technology-media-and-telecom-predictions/2025/genai- power-consumption-creates-need-for-more-sustainable-data-centers.html

Sampedro Guamán, C. R., Machuca Vivar, S. A., Palma Rivera, D. P., & Villalta Jadán, B. E. (2021). Impacto ambiental por consumo de energía eléctrica en los data centers. Dilemas contemporáneos: educación, política y valores, 8(spe4), 00034. Disponible en: https://doi.org/10.46377/dilemas.v8i.2786

Secretaría de Energía (sener) (2023). Prospectiva del sector eléctrico 2023–2037. Disponible en: https://base.energia.gob.mx/Prospectivas23/PSE_23- 37_VF.pdf base.energia.gob.mx

Shehabi, A., Smith, S. J., Hubbard, A., Newkirk, A., Lei, N., Siddik, M. A., Hole- cek, B., Koomey, J. G., Masanet, E., & Sartor, D. A. (2024, diciembre 19). 2024 United States Data Center Energy Usage Report. Lawrence Berkeley National Laboratory. Disponible en: https://eta.lbl.gov/publications/2024- lbnl-data-center-energy-usage-report

S&P Global Ratings (2025). Digital Infrastructure: Data Centers, ai Growth and Implications on Power Demand. Disponible en: https://www.spglobal.com/ ratings/en/research/digital-infrastructure

Takci, M. T., Qadrdan, M., Summers, J., & Gustafsson, J. (2025). Data Centers as a Source of Flexibility for Power Systems. Energy Reports, 13, 3661-3671. Disponible en: https://doi.org/10.1016/j.egyr.2025.03.020

us Congress, Congressional Research Service (USCRS) (2025, 26 agosto). Data Centers and Their Energy Consumption: Frequently Asked Questions (Report R48646). Disponible en: https://crsreports.congress.gov/product/pdf/R/R48646

us Department of Energy (doe) (2024, diciembre 20). Report evaluates Increase in us Electricity Demand from Data centers. Disponible en: https://www. energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity- demand-data-centers

Wang Y., Han Y., Shen J., et al., (2024). Data Center Integrated Energy System for Sustainability: Generalization, Approaches, Methods, Techniques, and Future Perspectives. The Innovation Energy 1(1): 100014. Disponible en: https://doi.org/10.59717/j.xinn-energy.2024.100014

Zhang, Y., & Liu, J. (2022). Prediction of Overall Energy Consumption of Data centers in Different Locations. Sensors, 22(10), 3704. Disponible en: https://doi.org/10.3390/s22103704

Capítulo 2

Bardach, E., & Patashnik, E. M. (2020). A Practical Guide for Policy Analysis. The eightfold path to more effective problem solving (6th ed.). https:// justicepolicynetwork.com/wp-content/uploads/2021/03/Bardachs- Eightfold-Path-1.pdf

Buyya, R., Ilager, S., & Arroba, P. (2023). Energy-Efficiency and Sustainability in New Generation Cloud Computing: A Vision and Directions for Integrated Management of Data Centre Resources and Workloads. arXiv. https://doi.org/10.48550/arXiv.2303.10572

Deng, Q., Goudarzi, M., & Buyya, R. (2021). FogBus2: A lightweight and distributed container-based framework for integration of IoT-enabled systems with edge and cloud computing. In S. Groppe, L. Gruenwald & C.-H. Hsu (Eds.), Proceedings of the International Workshop on Big Data in Emergent Distributed Environments (BiDEDE 2021) (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3460866.3461768

Esenarro, D., Rodriguez C., & Reyes A. (2019). Hyper Converged Systems Applied (HSA) Methodology to Optimize the Process of Technological Renewal in Data Centers. International Journal of Recent Technology and Engineering, 8(2S11). https://doi.org/10.35940/IJRTE.B1592.0982S1119

Fernández-Caramés, T. M., & Fraga-Lamas, P. (2020). Towards Next Generation Teaching, Learning, and Context-aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing. Applied Sciences. https://doi.org/10.3390/app9214479

Gao, J.(2016). Machine Learning Applications for Data Center Optimization. Google. https://static.googleusercontent.com/media/research.google.com/es//pubs/archive/42542.pdf

Gregor, S. (2006). The Nature of Theory in Information Systems. MIS Quar- terly, 30(3), 611–642. https://doi.org/10.2307/25148742

Katal, A., Dahiya, S., & Choudhury, T. (2022). Energy Efficiency in Cloud Computing Data Centers: A Survey on Software Technologies. Cluster Computing. https://doi.org/10.1007/s10586-022-03713-0

Kitchenham, B., Budgen, D., & Brereton, P. (2015). Evidence-based Software Engineering and Systematic Reviews. https://dl.acm.org/doi/10.5555/2994449

Lazic, N., Lu T., Boutilier C., & Ryu M. (2018). Data Center Cooling Using Model- Predictive Control. Advances in Neural Information Processing Systems (NeurIPS) 2018 Proceedings. https://proceedings.neurips.cc/paper_files/paper/2018/file/059fdcd96baeb75112f09fa1dcc740cc-Paper.pdf

Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2019). Resource Management With Deep Reinforcement Learning. Proceedings of the ACM Symposium on Cloud Computing (SoCC), 50–60. http://doi.org/10.1145/3005745.3005750

Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating Global Data Center Energy-use Estimates. Science, 367(6481), 984–986.

https://doi.org/10.1126/science.aba3758

Murino, T., Monaco, R., Nielsen, P. S., Liu, X., Esposito, G., & Scognamiglio,

C. (2023). Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations. Energies, 16(15), 5764. https://doi. org/10.3390/en16155764

Panwar, S., Rauthan, S., & Barthwal, V. (2022). A Systematic Review on Effective Energy Utilization Management Strategies in Cloud Data Centers. Journal of Cloud Computing, 11, Article 95. https://doi.org/10.1186/ s13677-022-00368-5

Patterson, D., González, J., Le, Q., Liang, C., Munguía, L. M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon Emissions and Large Neural Network Training. arXiv. https://doi.org/10.48550/arXiv.2104.10350

Qi, S., Milojicic, D., Pasricha, S., & Bash, C. (2023). SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management. arXiv. https://doi.org/10.48550/ arXiv.2308.13086

Rongon, R. K., & Das, K. (2025). Energy-Aware Data Center Management: A Sustainable Approach to Reducing Carbon Footprint. arXiv. https:// doi.org/10.48550/arXiv.2509.10462

Sampedro Guamán, C. R., Machuca Vivar, S. A., Palma Rivera, D. P., & Villalta Jadan, B. E. (2021). Impacto Ambiental por Consumo de Energía Eléctrica en los Data Centers. Dilemas Contemporáneos: Educación, Política y Valores, Edición Especial. https://doi.org/10.46377/dilemas.v8i.2786

Shehabi, A., Smith, S. J., Masanet, E., Horner, N., Azevedo, I. L., & Limmer, S. (2016). United States Data Center Energy Usage Report. Lawrence Berkeley National Laboratory. https://eta.lbl.gov/publications/united- states-data-center-energy

Snyder, H. (2019). Literature Review As a Research Methodology: An Overview and Guidelines. Journal of Business Research, 104, 333–339. https://doi. org/10.1016/j.jbusres.2019.07.039

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL). https:// arxiv.org/abs/1906.02243

Tuli, S., Gill, S. S., Xu, M., Garraghan, P., Bahsoon, R., Dustdar, S., & Jennings, N. R. (2021). HUNTER: ai Based Holistic Resource Management for Sustainable Cloud Computing. arXiv. https://arxiv.org/abs/2110.05529

Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Litera- ture Review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971

Xu, J., Toosi, A. N., & Buyya, R. (2020). A Self-adaptive Approach for Managing Applications and Harnessing Renewable Energy for Sustainable Cloud Computing. arXiv. https://arxiv.org/abs/2008.13312

Zakarya, M.(2018). Energy, Performance and Cost Efficient Datacenters: A survey. Renewable and Sustainable Energy Reviews / Elsevier (survey). https://doi.org/10.1016/j.rser.2018.06.005

Capítulo 3

Accenture. (2021). Uniting sustainability and technology: How tech and sustainability come together to drive growth and value. https://www.accenture.com/content/dam/accenture/final/a-com-migration/pdf/pdf-177/accenture-tech-sustai- nability-uniting-sustainability-and-technology.pdf

Boston Consulting Group. (2023). Accelerating climate action with artificial intelligence. https://web-assets.bcg.com/72/cf/b609ac3d4ac6829bae6fa 88b8329/bcg-accelerating-climate-action-with-ai-nov-2023-rev.pdf

McKinsey & Company. (2024). ai for social good: Improving lives and protecting the planet. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/ai%20for%20social%20good/2024/ ai-for-social-good-improving-lives-and-protecting-the-planet-v2.pdf

PricewaterhouseCoopers. (2020). Fourth Industrial Revolution (4ir)-enabled appli- cations for the Sustainable Development Goals (sdgs). https://www.pwc.com/gx/en/sustainability/SDG/4ir-enabled-applications-for-sdgs.pdf

Organisation for Economic Co-operation and Development. (2022). Harnessing the power of ai and emerging technologies. (oecd Digital Economy Papers). https://www.oecd.org/content/dam/oecd/en/publications/reports/ 2022/harnessing-the-power-of-ai-and-emerging-technologies.pdf

United Nations. (2023). The Sustainable Development Goals Report 2023. https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Go-

als-Report-2023.pdf

Unesco. (2021). Recommendation on the ethics of artificial intelligence. https://

unesdoc.unesco.org/ark:/48223/pf0000381137

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... &

Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, 233. https:// doi.org/10.1038/s41467-019-14108-y

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/ s11023-018-9482-5

Cap´ítulo 4

Abdalzaher, M. S., Fouda, M. M., Emran, A., Fadlullah, Z. M., & Ibrahem, M. I. (2023). A Survey on key Management and Authentication Approaches in Smart Metering Systems. Energies, 16(5), 2355. Disponible en: https:// doi.org/10.3390/en16052355

Barnes, R., Beurdouche, B., Robert, R., Millican, J., Omara, E., & Cohn-Gordon, K. (2023). The Messaging Layer Security (mls) Protocol (rfc 9420). Internet Engineering Task Force. Disponible en: https://doi.org/10.17487/RFC9420

Debnath, S. K., Saha, M., Islam, M. M., Sarker, P. K., & Pramanik, I. (2021). Evaluation of Multicast and Unicast Routing Protocols Performance for Group Communication with QoS Constraints in 802.11 Mobile ad hoc Networks. International Journal of Computer Network and Information Security, 13(1), 1–15. Disponible en: https://doi.org/10.5815/ijcnis.2021.01.01

Duan, Y., Ni, H., Zhu, X., & Wang, X. (2022). A Single-rate Multicast Con- gestion Control (srmcc) Mechanism in Information-centric Networking. Future Internet, 14(2), 38. https://doi.org/10.3390/fi14020038

Hu, H., Ye, M., Zhao, C., Jiang, Q., & Xue, X. (2023). Intelligent Multicast Routing Method Based on Multi-agent Deep Reinforcement Learning in sdwn. Mathematical Biosciences and Engineering, 20(9), 17158–17196. Disponible en: https://doi.org/10.3934/mbe.2023765

International Telecommunication Union (itu) (2021). Lignes directrices en matière de sécurité relatives à l’utilisation d’algorithmes à l’épreuve des attaques quantiques dans les systèmes imt-2020 (Recommendation itu-T X.1811). itu. Disponible en: https://handle.itu.int/11.1002/1000/14454-en

Lim, L.-H., Ong, L.-Y., & Leow, M.-C. (2025). Federated Learning for ano- maly Detection: A Systematic Review on Scalability, Adaptability, and Benchmarking Framework. Future Internet, 17(8), 375. Disponible en: https://doi.org/10.3390/fi17080375

Makris, I., Karampasi, A., Radoglou-Grammatikis, P., Episkopos, N., Iturbe, E., Rios, E., Piperigkos, N., Lalos, A., Xenakis, C., Lagkas, T., Argyriou, V., & Sarigiannidis, P. (2024). A Comprehensive Survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions. https://doi. org/10.1016/j.cosrev.2024.100717

Mirsky, Y., Doitshman, T., Elovici, Y., & Shabtai, A. (2018). Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. En Network and Distributed System Security Symposium (ndss 2018). Disponible en: https://doi.org/10.14722/ndss.2018.23204

Pramitarini, Y., Perdana, R. H. Y., Shim, K., & An, B. (2023). dlsmr: Deep Learning-based Secure Multicast Routing Protocol against Wormhole Attack in Flying ad hoc Networks with Cell-free Massive Multiple-input Multiple-output. Sensors, 23, 7960. Disponible en: https://doi.org/10.3390/ s23187960

Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architec- ture (nist Special Publication 800-207). National Institute of Standards and Technology. Disponible en: https://doi.org/10.6028/nist.SP.800-207

Tam, P., Ros, S., Song, I., Kang, S., & Kim, S. (2024). A Survey of Intelligent End-to-end Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches. Electronics, 13(5), 994. Disponible en: https://doi.org/10.3390/electronics13050994

Wijnands, I. J., Rosen, E., Dolganow, A., Przygienda, T., & Aldrin, S. (2017). Multicast using Bit Index Explicit Replication (bier) (rfc 8279). Internet Engineering Task Force. https://www.rfc-editor.org/info/rfc8279

Yang, H. (2025). A Study on Zone-based Secure Multicast Protocol Technique to improve Security Performance and Stability in Mobile ad hoc Network. Applied Sciences, 15(2), 568. Disponible en: https://doi.org/10.3390/ app15020568

Ye, M., Hu, H., Wang, X., Wang, Y., Peng, W., & Zheng, J. (2024). ma-cdmr: An Intelligent Cross-domain Multicast Routing Method Based on Multiagent Deep Reinforcement Learning in Multi-domain sdwn [Manuscrito no publicado / preprint arXiv:2409.05888]. https://arxiv.org/abs/2409.05888

Zhao, C., Ye, M., Xue, X., Lv, J., Jiang, Q., & Wang, Y. (2022). drl-m4mr: An Intelligent Multicast Routing Approach Based on dqn Deep Reinforcement Learning in sdn. Physical Communication, 55, 101919. Disponible en: https://doi.org/10.1016/j.phycom.2022.101919

Capítulo 5

Association for Financial Markets in Europe (afme) (septiembre de 2023). Artificial Intelligence: Challenges and Opportunities for Compliance. www.afme.eu/media/d5gm0fsu/finalafmeaicompliance202305.pdf. Consultado: 2025/10/20.

Bitsight (2025). Security Ratings for Third-party Risk —documentación de producto. www.bitsight.com. Consultado: 2025/11/10.

Cámara de Diputados del H. Congreso de la Unión (marzo de 2025). Ley Federal de Protección de Datos Personales en Posesión de los Particulares (Nueva lfpdppp). www.diputados.gob.mx/LeyesBiblio/pdf/LFPDPPP.pdf. Consul- tado: 2025/10/20.

Charles, J. (marzo de 2024). Evaluating roi in ai Security Implementations: Balan- cing Cost with Long-Term Security Benefits. Disponible en: www.research- gate.net/publication/385747332, Consultado: 2025/11/02.

Cofense (2025). Phishing Detection and Employee Training —documentación de producto. www.cofense.com. Consultado: 2025/11/10.

CrowdStrike (2025). CrowdStrike Falcon —documentación de producto. Disponi- ble en: www.crowdstrike.com. Consultado: 2025/11/10.

Darktrace (2025). Self-Learning ai for anomaly Detection — documentación de producto. Disponible en: www.darktrace.com/products/network. Con- sultado: 2025/11/10.

Drata (2025). Automate iso 27001 Compliance —documentación de producto. Dis- ponible en: www.drata.com/grc-central/automate-iso-27001-compliance. Consultado: 2025/11/10.

Edgescan (2024). 2024 Vulnerability Statistics Report. Disponible en: www. edgescan.com/wp-content/uploads/2025/04/2024-Vulnerability-Statis- tics-Report.pdf. Consultado: 2025/10/20.

European Union Agency for Cybersecurity (enisa) (septiembre de 2024). enisa Threat Landscape 2024. www.enisa.europa.eu/publications/enisa- threat-landscape-2024. Consultado: 2025/10/20.

European Union Agency for Cybersecurity (enisa) (diciembre de 2024). 2024 Report on the State of Cibersecurity in the Union. Disponible en: www.enisa. europa.eu/publications/2024-report-on-the-state-of-the-cybersecurity- in-the-union. Consultado: 2025/10/20.

Google (2025). Cloud Data Loss Prevention —documentación de producto. www. cloud.google.com/dlp. Consultado: 2025/11/10 de

International Accreditation Forum (iaf) (septiembre de 2025). The iso Survey of Management System Standard Certifications 2024. Disponible en: www. iafcertsearch.org/services/iso-survey. Consultado: 2025/10/20.

International Business Machines Corporation (ibm) (2024). Cost of a Data Breach 2024. Disponible en: www.ibm.com/think/insights/cost-of- a-data-breach-2024-financial-industry. Consultado: 2025/10/20.

International Organization for Standardization (iso) (octubre de 2022). iso/ iec 27001:2022 -Information Security Managment Systems-Requirements. Disponible en: www.iso.org/standard/27001. Consultado: 2025/10/15.

KnowBe4 (2025). Security Awareness Training and Simulated Phishing —docu- mentación de producto. Disponible en: www.knowbe4.com. Consultado: 2025/11/10.

Lampis, A. (2024). Automated Cybersecurity Compliance and Threat Response using ai, Blockchain & Smart Contracts. International Journal of Informa- tion Technology. Disponible en: www.link.springer.com/article/10.1007/ s41870-024-02324-9. Consultado: 2025/11/02.

Microsoft (2025a). Administración de identidades y acceso —documentación de producto. Disponible en: www.microsoft.com/es-mx/security/business/ identity-access/microsoft-entra-id. Consultado: 2025/11/10.

——— (2025b). Microsoft Purview —documentación de producto. Disponible en: www.learn.microsoft.com/en-us/purview/trainable-classifiers-get-started- with Consultado: 2025/11/10.

Mohale, V. Z., & Obagbuwa, I. C. (2025). A Systematic Review on the Integration of Explainable Artificial Intelligence in Intrusion Detection Systems to Enhancing Transparency Aand Interpretability in Cybersecurity. Disponible en: www.doi. org/10.3389/frai.2025.1526221. Consultado: 2025/11/02.

National Institute of Standards and Technology (nist) (2025). Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations. Disponible en: www.csrc.nist.gov/pubs/ai/100/2/e2025/final. Consul- tado: 2025/10/20.

National Institute of Standards and Technology (nist). (Enero de 2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0).: Disponible en: www. nist.gov/itl/ai-risk-management-framework. Consultado: 2025/10/20 de

Okta (2025). Securing all Types of Identity from ai Agents to Customers, Emplo- yees, and Partners —documentación de producto. Disponible en: www.okta.com/resources/whitepaper-getting-the-most-out-of-okta-threatinsight/. Consultado: 2025/11/10.

Olabintan, A. H. (2024). The Effect of Adversarial Machine Learning in Secu- rity and Privacy Analytics: A Review. fuoye Journal of Pure and Applied Sciences. Disponible en: www.researchgate.net/publication/383696148_ The_Effect_of_Adversarial_Machine_Learning_in_Security_and_Privacy_ Analytics_A_Review. Consultado: 2025/11/02.

Parlamento Europeo (abril de 2016). General Data Protection Regulation (gdpr). Disponible en: www.gdpr-info.eu Consultado: 2025/10/20de:

Qualys (2025). Vulnerability Management, Detection and Response (vmdr) — documentación de producto. Disponible en: www.qualys.com/aplicaciones/ vulnerability-management-detection-response/. Consultado: 2025/11/10.

Rapid7 (2025). Insightvm Vulnerability and risk prioritization —documentación de producto. Disponible en: www.rapid7.com/products/insightvm/. Consul- tado: 2025/11/10.

Recorded Future (2025). Third-party Intelligence and Monitoring Disponible en: documentación de producto. Disponible en: www.recordedfuture.com. Consultado: 2025/11/10.

RiskLens (2025). Cyber Risk Quantification —documentación de producto. Dis- ponible en: www.safe.security/cyber-risk-quantification/. Consultado: 2025/11/10.

Salem, A. H., Azzam, S. M., Emam, O. E., & Abohany, A. A. (2024). Advan- cing Cybersecurity: A Comprehensive Review of ai-driven Methods. Journal of Big Data 2024. Disponible en: www.journalofbigdata.springeropen. com/articles/10.1186/s40537-024-00957-y. Consultado: 2025/11/02.

Sharma, A., Rani, S., & Shabaz, M. (2025). A Comprehensive Review of Explainable ai in Cybersecurity: Decoding the Black Box. Journal of Cybersecurity Research. www.sciencedirect.com/science/article/pii/ S2405959525001584. Consultado: 2025/11/02.

SecureFrame (2025). Automated Compliance & Continuous Monitoring —docu- mentación de producto. Disponible en: www.secureframe.com. Consultado: 2025/11/10.

SentinelOne (2025). Behavioral ai —documentación de producto. Disponible en: www.sentinelone.com/blog/behavioral-ai-an-unbounded-approach-to- protecting-the-enterprise/. Consultado: 2025/11/10.

Shao-Fang, W., Ankur, S., & Basel, K. (2024). Artificial Intelligence for System Security Assurance: A Systematic Literature Review. International Journal of Information Security. Disponible en: www.doi.org/10.1007/s10207-024- 00959-0. Consultado: 2025/11/02.

Tenable (2025). Vulnerability Priority Rating (vpr) —documentación de producto. Disponible en: www.tenable.com/capabilities/vulnerability-priority-rating. Consultado: 2025/11/10.

Verizon (2024). 2024 Data Breach Investigations Report. Disponible en: www.verizon.com/business/resources/reports/2024-dbir-data-breach-investi- gations-report.pdf. Consultado: 2025/10/20.

Yamin, M. M., Shao-Fang, W., & Basel, K. (2025). ai-Assisted Assurance Profile Creation for System Security Assurance. Computer Security. esorics 2024 International Workshops. Disponible en: www.link.springer.com/chap- ter/10.1007/978-3-031-82362-6_29. Consultado: 2025/11/02.