Data lakes are convenient.
They provide storage for a seemingly endless stream of data integrated from versatile sources at a low cost.
Lakes allow you to save different versions and copies of the same data in their raw, processed, or unstructured form — making them ideal for keeping historical documentation.
However, without suitable security measures, this convenience can come at the cost of a data breach.
As more companies use this flexible and inexpensive form of storage, data lakes are becoming interesting to opportunistic hackers.
What are some of the basic principles for strong data lake security you should keep in mind in 2024?
Restricting Access to Safeguard Sensitive Data
Keeping sensitive data safe is a priority — whether it’s kept in a lake or other kinds of repositories. One way to secure them within the lake is to limit access.
Not everyone needs to have complete access to a data lake.
But how to restrict it?
Access management within the data lake is more complex compared to other storage forms because lakes collect data in all forms. When managing access, you need to think about both the object store’s access and several query engines.
Storing data in the lake also means there are no database tables — making the permissions more challenging to set up and overall more flexible.
Several solutions can help you catalog data and let you know which files are of sensitive or personal nature. Once you know that, you can limit access based on the group’s or an individual’s role.
To apply thorough governance and access policies, start by defining the role that users have within your company and teams. Set the access based on their responsibilities with one of many governance tools that are accessible on the market.
Regularly update and audit the access to a data lake to reduce the chances of unauthorized access and data breaches.
Encrypting Data in a Lake
Among others, data lakes keep a lot of information that is considered confidential, private, and sensitive. These kinds of documents are of special interest to malicious hackers. Therefore, encrypting it is a priority.
The data you have needs to be guarded against possible compromise in case the bad actor does manage to reach your data lake.
Another thing to consider is that the data lake has to be protected both when in transit and at rest. It’s more challenging, but data in transit also has to be encrypted.
Where to start?
Make sure that the data is locked with strong encryption algorithms and keys — even before you store them.
Then, set up robust protocols for securing the data as you move it from one part of the network to the next.
Building a Data Retention Policy
All data has a lifecycle — from its creation, and storage to the point when you dispose of it. Within a data lake, files shouldn’t be stored for years and years.
Even if it doesn’t seem so, your data lake’s storage has its limits. Free up that space by regularly removing old or unnecessary documents.
A data retention policy is also a matter of meeting compliance.
Compliance laws such as GDPR, Australian APP, and California CCPA can help you set retention deadlines. Some of them suggest how long the data can be kept within the lake until they have to be disposed of.
However, the retention policy widely varies from one company to another because the data is kept and stored for different purposes.
Set a time limit to govern how long the files will be kept within the lake until they have to be removed.
This also means that you need to classify the data within the lake. And have a way to separate the files that need to be removed at a specific time from documents that need to stay within the repository.
Automating Data Risk Analysis
Data lakes store large volumes of files and their repositories are growing at a rapid pace. The only way to stay on top of it is to automate analysis of potential risks. Secure data by identifying, preventing, and responding to threats on time.
Even though the files will be kept in versatile forms within the lake, it’s important to ensure that the lake is not in any way tampered with or altered by bad actors who are looking for sensitive information.
With a chaotic data lake, the only way to keep up with all the small changes that might indicate malicious behavior is to automate data risk analysis. That is, have a tool that can monitor and identify anomalies within your unique infrastructure in real-time.
The sooner you uncover that the data is at risk, the sooner you can react and mitigate the damage of a possible data breach. And with it prevent an incident that takes a major toll on both one’s reputation and finances.
Businesses that rely on data lake storage also have multi-layered security that consists of several layers of security controls.
Covering the Basics of Data Lake Security
Whether you store data in a lake or a warehouse, it has to be protected from possible modifications, illicit access, or compromise.
Data lake security comes with its challenges.
The main one is that data in the lake is saved in different forms that don’t have to be cleaned, or processed following the strict rules as it might have been within a warehouse. With that kind of freedom, security concerns can arise.
Regardless, keeping large amounts of data in the lake shouldn’t equal a big security problem.
To get the most out of this low-cost data repository and keep the most important assets secure at all times, cover all security basics such as access restrictions, data encryption, and retention policies.
Then, make sure that the data that is coming into the lake and the environment are analyzed at all times to catch any signs of malicious activity in time.