A quick search on the internet took me here: Choosing an Authentication Mode. And if you go down to the section Connecting Through Windows Authentication it points out a few important things and then even farther down the section Disadvantages of SQL Server Authentication has a bit more. Then I found a couple of good forum questions here and here. In summary (and only discussing actual security features):
Click through for the answers. Also read Cristian Satnic’s comments below, as Cristian is correct about wanting to keep passwords hashed instead of encrypted. Incidentally, Windows passwords aren’t encrypted, either—they’re hashed.
All communication with the Azure Storage via connection strings and BLOB URLs enforce the use of HTTPS, which provides Encryption in Transit. You can enforce the use of “Always HTTPS” by setting the connection string like this: “DefaultEndpointsProtocol=https;AccountName=myblob1…” or in SAS signatures, as in the example below:
To protect data at rest, the service provides an option to encrypt the data as they are stored in the account. There’s no additional cost associated with encrypting the data at rest and it’s a good idea to switch it on as soon as the account is created. There is a one-click setting at the Storage Account level to enable it, and the encryption is applied on both new and existing storage accounts. The data is encrypted with AES 256 cipher and it’s now generally available to all Azure regions and Azure clouds (public, government etc)
There’s some good information here, making it worth the read.
If you have multiple objects or actions to audit, then just separate them with a comma, just like the AuditActionGroups parameter. The one key piece to remember is you must specify all audit actions and action groups together with each execution of Set-AzureRmSqlDatabaseAuditingPolicy. There is no add or remove audit item. This means if you have 24 actions to audit and you need to add one more, then you have to specify all 25 in the same command.
Now let’s run a few queries to test the audit. First, we’ll run a simple select from the Salaries table.
Patrick shows off the UI (which is nice for one-off checking) and also the function sys.fn_get_audit_file(), which you’d probably want to use for automated audit checks.
Today I am very excited to announce that I have (finally!) launched my email course covering the basics of SQL Server Security.
This has been a lot of work to get a new system in place to make the learning experience a little different. It is like a normal email course, but at the same time it isn’t.
I have been waiting for this for months ever since hearing Chris first talk about it.
With this is mind, Microsoft has made some big changes to CLR in SQL Server 2017. SQL CLR has always been an interesting area of the engine—it allows for the use of .NET code in stored procedures and user defined types. For certain tasks , it’s an extremely powerful tool—things like RegEx and geo functions can be much faster in native CLR than trying to do the equivalent operation in T-SQL. It’s always been a little bit of a security risk, since under certain configurations, CLR had access to resources outside of the context of the database engine. This was protected by boundaries defined in the CLR host policy. We had SAFE, EXTERNAL_ACCESS, and UNSAFE levels that we could set. SAFE simply limited access of the assembly to internal computation and local data access. For the purposes of this post, we will skip UNSAFE and EXTERNAL_ACCESS, but it is sufficed to say, these levels allow much deeper access to the rest of the server.
Code Access Security in .NET (which is used to managed these levels) has been marked obsolete. What does this mean? The boundaries that are marked SAFE, may not be guaranteed to provide security. So “SAFE” CLR may be able to access external resources, call unmanaged code, and acquire sysadmin privileges. This is really bad.
It’s not the end of the world for CLR, but this is a breaking change. Read on for more details.
We can try it out with a very familiar domain:
(converted <- to_homoglyph("google.com")) ##  "ƍ၀໐|.com"
Now, that’s using all possible homoglyphs and it might not look like
google.comto you, but imagine whittling down the list to ones that are really close to Latin character set matches. Or, imagine you’re in a hurry and see that version of Google’s URL with a shiny, green lock icon from Let’s Encrypt. You might not really give it a second thought if the page looked fine (or were on a mobile browser without a location bar showing).
Click through for more details, as well as information on punycode.
Row level security is the ability to filter content based on a users role. There are two primary ways to implement row level security in Power BI – through Power BI or using SSAS. Power BI has the ability in the desktop to create roles based on DAX filters which affect what users see in the various assets in Power BI.
In order for this to work, you will need to deploy to a Workspace where users only have read permissions. If the members of the group associated to the Workspace have edit permissions, row level security in Power BI will be ignored.
Read on for more details as well as a set of how-to links.
Power BI Privacy Levels “specify an isolation level that defines the degree that one data source will be isolated from other data sources”. After working through some testing scenarios and trying to discover the real impact to data security, I was unable to effectively show how this might have any bearing on data security in Power BI. During one test was I shown a warning about using data from a website with data I had marked Organizational and Private. In all cases, I was able to merge the data in the query and in the relationships with no warning or filtering. All of the documentation makes the same statement and most bloggers are restating what is found in the Power BI documentation as were not helpful. My takeaway after reviewing this for a significant amount of time is to not consider these settings when evaluating data security in Power BI. I welcome comments or additional references which actually demonstrate how this isolation actually works in practice. In most cases, we are using organizational data within our Power BI solutions and will not be impacted by this setting and my find improved performance when disabling it.
As Steve notes, this is not really a security feature. Instead, it’s intended to be more a warning to users about which data is confidential and which is publicly-sharable .
First, I will not be discussing the personal gateway in this post. If you have chosen to use the personal gateway, you have limited functionality and should consider using the on-premises data gateway for corporate use.
The on-premises data gateway (referred to as gateway throughout this post) “acts as a bridge, providing quick and secure data transfer between on-premises data and the Power BI, Microsoft Flow, Logic Apps, and PowerApps services.” (ref) Much of what is discussed here will apply to all of the services referenced above, but our primary concern is related to Power BI. Please refer to references at the end of this post for details about data sources supported within the gateway.
Click through for more information.
Another way to compartmentalize or secure data is using Workspaces within Power BI. Every user, including free users, have access to My Workspace which is the default location for deploying Power BI and other BI assets. However, you also have the option to create additional workspaces as deployment targets. These Group Workspaces usually have functional and security separation associated with them.
This post is a good overview of methods available for data sharing.