How to Secure Your Data Lake?

Your business data lake is one of the best options for warehousing your data from multiple sources no matter what the purpose. However, as I’m sure you have come to realize, securing data lakes is the biggest challenge your enterprise will face. Since you dump data into a data lake in its original format, the dumped data is scattered and accessible for anyone on the enterprise network.

Different organizations use data lakes for different purposes—an archive for their data, a sandbox for data scientists, and a landing zone for data. With the introduction of cloud services, organizations are shifting data lakes to cloud servers so they may later use the data for aggregation, indexing, and analysis.

As Merv Adrian states ‘The idea behind using data from data lakes is to try different ideas and build models. If the organization finds something fruitful, they can put it into their DBMS or RDBMS.”

But the storing of data into data lakes raises multiple security issues and can harm your organization in multiple ways. These security issues can’t be overlooked.

So let’s look at the best approaches you can use for securing your enterprise data lake:

1. Use a Robust Data Lake Management Strategy

You need to have an in-depth understanding of data usage, its planned application, and an understanding of the governance requirements of those applications to secure a data lake.

You can add and maintain different data lakes for different purposes to implement a comprehensive data lake management strategy. For example, having separate data lakes for production workloads and data science can help you to protect your data.

You need to understand that excellent management and security of data lakes is beyond the ability of a single platform. So choose low-cost object cloud storage like Microsoft’s Azure Object Store or Amazon S3.

As stated by Doug Henschen, “The governance and security of individual platforms don’t always meet the requirements for complete access control and new governance requirements.”

2. Classify and Identify Incoming and Existing Data

You need to take necessary precautions when storing sensitive data in your lake. For that, you need to classify and identify your data. Use security classification for classifying different data elements into different security levels.

The sensitivity or security levels should be based on governmental and industry regulation security standards. The data should not be modified or disclosed without an authorization from the upper management in the organization.

Data classification allows administrators to integrate baseline security procedural controls and mechanisms, evaluate existing data in your data lake, and analyze the incoming information effectively.

3. Secure Output, Input, and Work Files

Hackers can gain access to the output files, input files, and different day-to-day processing work files in your data lake to harm your organization.

You need to secure your data lake, including outgoing and incoming files, data transferring to different applications, and data lake backups.

The security of files enables you to strengthen the security of the data lake and prevent organizational losses.

4. Access Rights and Account Management

Platforms like Oracle, Amazon, Cloudera, and Microsoft have popular data lake options you can choose from. Every platform has different processes and mechanisms to create and assign accounts and access rights.

You should adhere to the industry recommendations while assigning rights and stick to granting minimal security rights for users working on the platform. Two-factor authentication, password protection, and enterprise authentication should be a requirement for gaining access to your data lake.

Some vendors include detailed security descriptions and guidelines to help administrators secure their data lake. You can go through these documents for more specific information.

5. Understanding the Data Lake Pipeline

When data is passed into the data lake, there’s not much protection provided to the RDBMS and enterprise database. In traditional management of data, the security team does little once the data enters the database management system,

The structural management of data lakes doesn’t include governance capabilities and policies. You need to consider your data lake pipeline with midstream, upstream, and downstream components when approaching data lake security.

Every stage has different threat vectors, and you need to address them differently to enhance the security of your data lake.

Vice President of Podium Data, John Felahi, believes that understanding the journey of your data is key to enhancing the security of your data lake.

6. Encryption

One of the vital and fundamental security measures for data security and clusters is encryption. You can ensure data encryption based on the details and information provided by your cloud service provider. If your cloud service provider doesn’t provide the encryption certificates, you need to incorporate it yourself.

Irrespective of the providers, you need to develop different methods for certificate rotation within 90 days for robust encryption security.

7. Data Loss Preventive Measures

Take measures to recover your data from accidental deletion or sophisticated hacking. You need to minimize the risk of data loss, create secure data backups, and integrate retention plans for complete safety.

Different services that store or manage data should be protected and evaluated against data loss. You need to manage and secure your data lakes wisely to minimize the damage of data loss.

These are the leading strategies you can use to secure the data lakes of your enterprise and strengthen your overall security.

Summary

A business’s data lake is a complex environment that requires planning, discipline, and expertise to remain secure from external threats and attacks. You have the responsibility and liability to secure your data lakes. Say, if it’s on a public cloud, you need to convert your cloud data lake into a private data lake. Implement these result-oriented strategies to secure your data lake and prevent your data from getting compromised.