Well, sorry I've been quiet... I was asked to give an impromptu conference talk since the scheduled speaker couldn't attend. Fortunately, I had a number of talks downloaded to my laptop so I was able to pick another talk from a few years back but hey, I had it :). So yeah, just something any and all conference speakers should consider... keep an archive of your talks available on your system or quickly retrievable from the cloud. You never know when you might be needed/asked to give a talk on short notice.
Back to today's other festivities (woo!)...
How much thought do we give to Data Management and Security? What happens to our data as we are trying to perform workflows? Where does our data go on its journey? At what point is our data standing in the line of fire or in a position to be compromised, stolen, or tainted?
Natasha Nicolai is discussing ways in which we can better manage and maintain our data and how that data is accessed, modified, deleted, and secured in the process of us doing our work.
Odds are most organizations at this point are not using a monolithic data model, where everything is in one place and suffering a single point of failure or where a single vector being exploited could bring the whole system down or compromise all of the data.
I'm somewhat familiar with this by virtue of frequently testing data transformations. Most of these data transformations are being done on actual live customer data. That means I have to be exceptionally careful with this data and make sure that it cannot fall into the wrong hands. Additionally, I need to also make sure that none of the interactions I perform will mess up or modify that data.
Natasha is sharing a variety of strategies to make sure in production environments and specifically in Cloud environments like AWS. She makes the case that we want to make sure that the data that flows through our apps and what is visible is appropriately given permission to do exactly that. She refers to the s steps and gates as "data pillars" to make sure that we are allowing visibility to just those who need to see it and hiding/protecting the data from all who do not. The idea of "data lakes" is again ways to make sure that we maintain data integrity but to also give us the ability to store data and pack it away so as to not be accessed when it isn't meant to be.
There's a lot here that I must confess I have limited exposure to but I'd definitely be interested in seeing ways to learn more about these data security options.