In the first part of this blog two-part series, we took a deep dive on Data Shuffling techniques aiming to mix up data and allowing to optionally retain logical relationships between columns. In this second part, we will now focus on Data Masking techniques as one of the main approach to guarantee Data Privacy.
As APIs become the de facto standard for building and connecting business-critical applications, it’s important for operations teams to gain visibility into the security attributes of your APIs in order to continuously monitor and maintain the health of your API programs.
In the current microservices DevOps environment, there are tough new and evolving challenges for developers and teams to consider on top of the more traditional ones. From worsening versions of already common threats to new-generation evolving threats, new perspectives are required on securing microservices. These new perspectives may not be intuitive for many otherwise sophisticated DevOps and data teams.
Earlier this year, Unravel released the results of a survey that looked at how organizations are using modern data apps and general trends in big data. There were many interesting findings, but I was most struck by what the survey revealed about security. First, respondents indicated that they get the most value from big data when leveraging it for use in security applications. Fraud detection was listed as the single most effective use case for big data, while cybersecurity intelligence was third.
A 2015 internet security report found that 60% of cyber attacks were aimed at small to medium-sized businesses.
