Inteligencia artificial y sostenibilidad digital. Infraestructura, seguridad y ciudadania inteligente

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

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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

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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

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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

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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

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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

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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

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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.

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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.

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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

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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.

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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.

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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.

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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.

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