Scalable Cloud Strategies: Values for Higher Education
It's an example of one of the cloud networking security features that is unique to the cloud. Another is establishing cloud firewalls, which requires the review and cleanup of hundreds of firewall policies and rules that must be evaluated at the campus level.
There were several other major security enhancements accomplished in the CSU-Unisys cloud transformation partnership: A SIEM platform from Securonix was implemented, along with a public key infrastructure and certificate management system. The Delphix Data Vault, plus a privileged account management system, a cloud infrastructure entitlement management system, and an endpoint detection and response system were all key systemwide cloud initiatives that have taken the CSU's security posture and risk management to a highly mature level.
That's all I'll say for now about security transformation, but those are just a few examples of the security improvements possible in a cloud-based environment.
Grush: Thanks! Transformative cloud security is a huge area, and it's hard to choose what to mention in a limited space.
Another such area is cloud-based teaching and learning innovation. What can you tell me about that?
Wessells: I would say that if there's data that is accessible, harmonized across systems, and secure, and there's modern integration in place, it sets up an institution for the future with AI-enabled teaching and learning applications… because AI depends on high-quality data.
If there's data that is accessible, harmonized across systems, and secure, and there's modern integration in place, it sets up an institution for the future with AI-enabled teaching and learning applications.
I think institutions that are seeing AI innovations in teaching and learning are those that have invested in ensuring that their data is in a good state within the cloud. It's protected. It's cleansed. It's harmonized across systems with a master data hub. These institutions are in a much better state for taking on all of the unknowns and exciting new possibilities with artificial intelligence.
Success in teaching and learning innovations with AI is highly dependent upon, again, high-quality data that the academic community has vetted and approved. Inclusion of human-in-the-loop features and responsible AI capabilities to minimize bias and AI hallucinations is critically important.
Native cloud and AI services appear to be a critical foundation for making serious advancements in the nascent world of teaching and leaning with artificial intelligence. Unisys is working closely with the CSU Chancellor's Office, CSU Fullerton, CSU Channel Islands, and CSU Northridge on a groundbreaking GenAI solution to enhance teaching and learning. We look forward to unveiling it in 2025.
Grush: And I'm looking forward to hearing more about that!
But now, what about the research cloud? What's going on there?
Wessells: As you know, the offerings from Google, AWS, and Azure for on-demand HPC and research storage are only getting better. Additionally, resources like NSF's Jetstream2 are bringing free or affordable high-end compute resources to researchers who might not have access otherwise.
The offerings from Google, AWS, and Azure for on-demand HPC and research storage are only getting better.
My sense is that the HPC space is going to expand for a lot of research opportunities on campuses. There are situations in which faculty need HPC clusters for certain types of research in solving very complex problems. Yet some research problems may be more ideally suited to a cloud-based research or storage solution that can be easily provisioned and scaled back through cloud services — it strikes me that research on demand will be gaining even more traction with researchers.