Database security shouldn’t be hard, so Imperva has made it simple. Imperva Cloud Data Security (CDS) is a SaaS solution specifically designed to secure organizations’ data stored in their AWS managed databases (DBaaS). It works with all database types available through Amazon RDS, including MySQL, PostgreSQL, Microsoft SQL Server, MariaDB, Oracle, and Amazon Aurora. You can try it out for free here.

For organizations that purchase solutions through AWS, buying CDS via the AWS Marketplace unlocks some great advantages. It minimizes procurement friction by consolidating vendors and billing. Buying CDS applies towards organizations’ AWS spending commitments. Since CDS participates in the Private Offers purchasing program, organizations can also negotiate customized terms, pricing and payment schedules.

Transform DBaaS security to keep up with digital transformation
We’re currently witnessing a huge transformation in how organizations store and use data. Industry analysts believe that by 2022, 75% of all databases will be deployed in the cloud. Imperva’s own research reveals that 96% of organizations are using DBaaS, and almost half of all organizations have 75% or more of their data in a DBaaS environment.

Why is this seismic shift happening? The short answer is because of the advantages DBaaS provides in cost, flexibility and agility. Cloud platform providers like AWS have made it extremely easy and inexpensive to provision new managed databases. The evolution toward DevOps processes to develop, test and deploy IT solutions has also resulted in a huge proliferation of managed databases in use.

With all this sudden and accelerating growth in DBaaS and the speed it brings, organizations are struggling to keep track of all the databases they use, let alone which ones hold sensitive, compliance-relevant information. How do they secure data they don’t even know about?

AWS secures the platform, but not your data
A common misconception among organizations using AWS DBaaS is that AWS’s default security protects their data. AWS does offer terrific security, but it only covers the infrastructure used for their DBaaS offerings. In other words, AWS DBaaS native security protects the platform, but not your data.

You can be confident that the underlying OS and database technologies used are fully protected. But as explained by AWS themselves in their shared responsibility model disclosure, they take no responsibility for what their customers put into their managed databases and what they do with it. It’s up to each organization to ensure their data is properly secured.

CDS delivers security in minutes
As a SaaS solution, CDS requires no software installation. Within a few minutes, CDS reveals the location and security status of all your data and begins alerting you when any violations of your security policies occur. At any time, you can also run comprehensive reports to support security reviews or compliance audits.

CDS will also help you identify security issues you didn’t even know existed, by developing a baseline understanding of typical user behavior, and notifying you when it detects atypical or unusual activity.

Why home-grown DBaaS security tools are not an option
AWS offers a lot of powerful tools and services for “builders” of IT solutions. In theory, these can be used to assemble a security solution for DBaaS. Some organizations we’ve talked to have tried to use this approach.

In practice, we’ve found that most of them underestimated the effort and simply don’t have the time or the skill set needed to assemble and maintain this kind of solution. What they thought would be a simple effort turned out to be a massive, ongoing project.

Security needs are not constant. Internal teams’ demands grow and change on a regular basis, requiring a level of scalability and flexibility that’s difficult for home-grown tools to match. By comparison, CDS offers a purpose-built, inexpensive, scalable SaaS alternative that focuses on your DBaaS security so you don’t have to.

Amazon Web Services recently announced the general availability of AWS Glue DataBrew, a new visual data preparation tool that enables users to prepare data without writing code. While AWS Glue provides both code-based and visual interfaces, data analysts and scientists now gain an easier way to clean and transform data. AWS Glue DataBrew is a service that allows data exploration and experimentation directly from AWS data lakes, data warehouses, and databases. It also touts more than 250 pre-built transformations for data prep automation.

AWS Glue DataBrew is a visual data preparation tool for AWS Glue that allows data analysts and data scientists to clean and transform data with an interactive, point-and-click visual interface, without writing any code. The solution lets users access and visually explore any data across their organization directly from their Amazon Simple Storage Service (S3) data lake, Amazon Redshift data warehouse, and Amazon Aurora and Amazon Relational Database Service (RDS) databases. DataBrew recommends data cleaning and normalization steps as well.

For complex tasks like converting words to a common base or root word, AWS Glue DataBrew provides transformations that use advanced machine learning techniques like Natural Language Processing and apply them automatically to future incoming data. The tool publishes prepared data to Amazon S3, which makes it simple for customers to immediately use it in analytics and machine learning. DataBrew is serverless and fully managed so users never need to configure, provision, or manage any compute resources.

In a media statement, AWS VP of Database and Analytics Raju Gulabani said: “Customers love the scalability and flexibility of code-based data preparation services like AWS Glue, but they could also benefit from allowing business users, data analysts, and data scientists to visually explore and experiment with data independently, without writing code. AWS Glue DataBrew features an easy-to-use visual interface that helps data analysts and data scientists of all technical levels understand, combine, clean, and transform data.”