Blog post

Tiering self-service by user competence

By Lydia Leong | August 10, 2020 | 1 Comment

GovernanceCloud Computing for Technical ProfessionalsCloud Computing

A nontrivial chunk of my client conversations are centered on the topic of cloud IaaS/PaaS self-service, and how to deal with development teams (and other technical end-user teams, i.e. data scientists, researchers, hardware engineers, etc.) that use these services. These teams, and the individuals within those teams, often have different levels of competence with the clouds, operations, security, etc. but pretty much all of them want unfettered access.

Responsible governance requires appropriate guidelines (policies) and guardrails, and some managers and architects feel that there should be one universal policy, and everyone — from the highly competent digital business team, to the data scientists with a bit of ad-hoc infrastructure knowledge — should be treated identically for the sake of “fairness”. This tends to be a point of particular sensitivity if there are numerous application development teams with similar needs, but different levels of cloud competence. In these situations, applying a single approach is deadly — either for agility or your crisis-induced ulcer.

Creating a structured, tiered approach, with different levels of self-service and associated governance guidelines and guardrails, is the most flexible  approach. Furthermore, teams that deploy primarily using a CI/CD pipeline have different needs from teams working manually in the cloud provider portal, which in turn are different from teams that would benefit from having an easy-vend template that gets provisioned out of a ServiceNow request.

The degree to which each team can reasonably create its own configurations is related to the team’s competence with cloud solution architecture, cloud engineering, and cloud security. Not every person on the team may have a high level of competence; in fact, that will generally not be the case. However, the very least, for full self-service there needs to be at least one person with strong competencies in each of those areas, who has oversight responsibilities, acts an expert (provides assistance/mentorship within the team), and does any necessary code review.

If you use CI/CD, you also want automation of such review in your pipeline, that includes your infrastructure-as-code (IaC) and cloud configs, not just the app code; i.e. a tool like Concourse Labs). Even if your whole pipeline isn’t automated, review of IaC during the dev stage, and not just when it triggers a cloud security posture management tool (like Palo Alto’s Prisma Cloud or Turbot), whether in dev, test, or production.

Who determines “competence”? To avoid nasty internal politics, it’s best to set this standard objectively. Certifications are a reasonable approach, but if your org isn’t the sort that tends to pay for internal certifications or the external certifications (AWS/Azure Solution Architect, DevOps Engineer, Security Engineer, etc.) seem like too high a bar, you can develop an internal training course and certification. It’s not a bad idea for all of your coders (whether app developers, data scientists, etc.) that use the cloud to get some formal training on creating good and secure cloud configurations, anyway.

(For Gartner clients: I’m happy to have a deeper discussion in inquiry. And yes, a formal research note on this is currently going through our editing process and will be published soon.)

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1 Comment

  • Sumit Kumar says:

    This is very informative. I think this technology will help many people.