Some sections of this work are incomplete but have been posted as is. They will be updated and queries resolved as time permits.
This meant we had to store data for multiple customers and protect their data from each other, even if it was in the same table, as well as support data protection within a single customer, and agencies that were able to manage multiple customers.
We had to build our own access control system within the database and application, and spent as much time implementing, maintaining, and troubleshooting those parts of the code as we did on the parts that our customers actually used.
It would have been great if there were some high-level feature in SQL Server that could help implement row-granularity access control without having to write and debug all of that code.
Solution SQL Server introduces a new feature called Row-Level Security, which can simplify much of the work required in earlier versions. The goal is to provide per-row read and write access control, based on attributes of the executing user such as role or departmentwith minimal schema, application, or query changes.
In previous versions, you would typically implement this using views over your own access control table, or just applying WHERE clauses to queries, both of which can introduce a lot of complexity.
This tip walks through basic Row-Level Security configuration, shows some examples, and explains limitations, all based on the most recent build at the time of writing CTP 2. Security predicate - Glues a predicate function to a table for example, applying a function that checks for rep name or the rep manager role to the Accounts table.
Security policy - A set of security predicates for example, you might have an Account policy that applies multiple security predicates to Account-related tables, and an HR policy that applies several security predicates to various HR tables.
SQL Server Row-Level Security Example To illustrate with an example, let's assume we have a table of Accounts and a table of AccountReps, where an Account belongs to a single rep, and that rep should only be able to see their own Accounts and their own AccountRep details. We also have a RepManager, Susan, who should be able to see the rows belonging to all of the reps, and a user named Peon, who shouldn't be able to see anything.
The logic can be a lot more complicated than that, of course. There could be a hierarchy of reps and rep managers, or rep managers who only oversee certain reps, or reps with seniority who can see all others, or a variety of other required scenarios.
The function above is simple for brevity, not necessarily illustrative of an exhaustive security policy. Next, we need a policy to apply those predicates to our tables.
I wrote the function so that it could be used against both of our tables; in reality, a slightly simpler function could be used separately against the AccountReps table, since all that would have to match for a given rep is the principal name.
Also, be aware that this can easily be spoofed in the connection string. This also demonstrates swapping out the function used you can't alter a function that is currently referenced by a security policy, so an easy workaround is to create a new function, and then alter the policy.
Summary Row-Level Security provides a way to apply granular control and filtering without a hefty investment in schema, query changes, or application logic. There are some limitations though, as well as some impacts to performance and vulnerabilities to information leakage, which I will cover in Part 2.
Try out Row-Level Security in scenarios where it may seem useful. Stay tuned for Part 2 of this tip, where I will go over some of the limitations and gotchas.
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