For decades, enterprises have relied on a traditional data warehouse approach for data discovery and storage. This approach consists of single, static structures to analyze and store data and help organizations become more efficient. But with the rise in online users and applications, enterprises often find it challenging to drive digital information using this approach.

A major issue with data warehouses is that the data needs proper structuring before storage, and with zettabytes of data available, it can be time-consuming. Hence, a modern architecture called data lakes was proposed to solve this issue.

Data lake offers a flexible format for storing data and eliminates the necessity of structuring it before storing it. In addition, data lake associates itself with metadata tags and identifiers when storing data, thus making it easily retrievable for future use.

As data lakes provide a simplified repository for storing a large amount of data, any compromise can have severe implications for organizations, particularly healthcare, defense, or finance. Hence, it’s crucial to implement tools and strategies to secure data lakes.

Here are seven ways to help enterprises secure their valuable data lakes.

1. Network Perimeter Security

Hackers are always on the lookout for vulnerabilities in data lake platforms. In addition, as organizations allow their partners and employees to access stored data from data lakes, they often risk exposing their network to multiple threats.

Securing the network perimeter is crucial to your data lake security posture. It acts as a virtual wall that allows authorized aspects through the network and blocks malicious attacks.

Creating a secure network perimeter is crucial to protecting from internal and external threats from in-house or cloud data lakes. An ideal network perimeter security involves some of the best data lake protection tools, including firewalls, border routers, host and network-based intrusion detection systems, and VPNs. Implementing these tools will significantly reduce cyberattacks and improve the system’s efficiency.

2. Security Function Isolation

Security function isolation is the foundation of data lake security, especially for cloud storage. It isolates all security functions from non-security ones using an isolation boundary.

The goal of implementing isolation is to limit platform capabilities to their intended use only. For data lakes, a security function isolation should define their roles to platform management and administration. Moreover, it should also ensure only experienced security administrators can modify the security functions.

Security function isolation reduces the risk of data breach and restricts inexperienced users from accessing the security functions and causing misconfigurations.

3. Data Loss Prevention (DLP)

DLP is a technique that protects organizations against unwanted data breaches, exfiltration, or destruction. In addition, it prevents illicit data transfer by end-users outside your organizational boundaries.

DLP is a critical aspect of improving information security as it allows you to classify, prioritize, monitor, filter, and report sensitive data stored in data lakes. Moreover, implementing DLP to the access policies helps organizations comply with regulations, such as PCI-DSS, HIPPA, and GDPR.

4. Monitor Compliance and Incident Response

Compliance monitoring


Compliance monitoring and incident response is another essential way to secure data lakes. It assists organizations with early threat detection, identification, and investigation. In addition, integrating your data lake security infrastructure with compliance monitoring and incident response will help you quickly and effectively deal with security threats.

5. Introduce Robust Authentication Measures

Restricting data access to authorized users is the basis of data lake security. It can be done by implementing a secure authentication process to keep your data safe and help users quickly access the information.

Authentication validates the identity of users who are trying to access sensitive information. There are numerous ways in which organizations can validate user identity, including

  • Single sign-on (SSO) offers users a set of login credentials.
  • Multi-factor authentication (MFA) adds an extra security layer in the form of a one-time access code for a higher level of assurance.
  • Consumer identity and access management (CIAM) allow organizations to securely capture and manage authorized users’ information.

6. Authorization

Authorization is another login step that determines what actions authorized users can perform in your stored database. It is based on the principle of least privilege that provides specific permissions to ensure the data is accessible to those who need it.

Authorization is crucial as it restricts everyone from accessing sensitive information stored in the data lake.

7. Enforce Data Encryption

Data Encryption


Data encryption is a vital information security standard that involves encoding data to ensure only authorized users can access it. Organizations can substitute sensitive information stored in data lakes with another form or code and send it over other computer networks. Users with a decryption key can access this code and use it to translate the code back to its original form.

Enforcing data encryption helps organizations protect sensitive data and securely transfer it between servers.


Data lakes offer end-to-end services to store terabytes of data while reducing the time, effort, and cost required to manage and access them. But keeping data lake secured is a complex process and involves the right expertise, strategic planning, and quality implementation. By implementing the best security practices and solutions, you can leverage the tremendous analytical benefits of data lakes while ensuring data integrity.