This is a fantastic question and I think your pro-active approach to securing the most valuable asset of your business is something that a lot of people should heed, especially if you're sharing the data with your co-workers. Letting people see only what they need to see is an undeniably good way to reduce your attack surfaces. Standard cyber security methods are no longer enough imo, demonstrated by numerous attacks/people losing laptops/usbs with sensitive data on. We are just humans after all. With the GDPR coming in to force in May next year, any company with customers in the EU will have to demonstrate privacy by design and anonymisation techniques such as masking have been cited as way to show this.
NOTE: I have a vested interest in this answer because I am working on such a service you're talking about.
We've found that depending on your exact use case, size of data set and contents will depend on your masking method. If your data set has minimal fields and you know where the PII is, you can run standard queries to replace sensitive values. i.e. John -> XXXX. If you want to maintain some human readability there are libraries such as Python's Faker that generate random locale based PII you can replace your sensitive values with. (PHP Faker, Perl Faker and Ruby Faker also exist).
DISCLAIMER: Straight forward masking doesn't guarantee total privacy. Think someone identifying individuals from a masked Netflix data set by cross referencing with time stamped IMDB data or Guardian reporters identifying a Judges porn preferences from masked ISP data.
Masking does get tedious as your data set increases in fields/tables and you perhaps want to set up different levels of access for different co-workers. i.e. data science get lightly anonymised data, marketing get a access to heavily anonymised data. PII in free text fields is annoying and generally understanding what data is available in the world that attackers could use to cross reference is a big task.
The service i'm working on aims to alleviate all of these issues by automating the process with NLP techniques and a good understanding of anonymisation maths. We're bundling this up in to a web-service and we're keen to launch on the AWS marketplace. So I would love to hear more about your use-case and if you want early access we're in private beta at the moment so let me know.
