The growing complexity of organizing files has pushed organizations to adopt data lakes for storing vast amounts of information. However, as repositories grow deeper and wider, they become tempting mines for cybercriminals. Without proper security measures, a data lake can quickly become a breach waiting to happen.
What Is a Data Lake?
A data lake is a large, centralized storage system that stores structured and unstructured information, such as spreadsheets, emails, videos and sensor logs, in their original, raw forms. Unlike traditional databases, which require records to be preprocessed and organized in neat rows and columns, all the files are pooled.
As the name suggests, it’s a digital reservoir where companies can dump all the information they collect, only to be processed and analyzed later when needed. Due to the volume of data being created, captured and consumed worldwide by internet-connected apps, the lake market is projected to reach $90 billion by 2032.
The Best Security Practices for Data Lake Protection
Data lakes are targeted because they store vast amounts of personal information, business assets and financial records. What makes them even more vulnerable is that they’re continuously fed by data streams from multiple sources, meaning a single weak point can expose the entire repository to cybercriminals.
More organizations are shifting to data lakehouses to merge the best of both worlds — the raw storage capabilities of lakes with the structured processing power of warehouses. Therefore, the security stakes are even higher. That’s why strong, consistent safeguards are essential across both environments.
1. Use Role-Based Access Control
A wide-open data lake is a hacker’s dream. The first line of defense is ensuring only the right people can reach specific records. Poor access management leads to privilege creep, where insiders can glean information they’re not supposed to see. Although 75% of insider threats come from nonmalicious intent, they can still cost a business as much as 20% of its earnings.
Role-based access control (RBAC) restricts entry based on a person’s job. For example, a marketing analyst may need customer engagement data to strengthen campaigns but has no business dealing with financial transactions. RBAC, combined with the principle of least privilege, ensures employees don’t see anything outside their scope of work, so they’re only granted entry for the minimum information needed to perform their roles.
Access permissions should be regularly audited, not just annually, but every time someone changes roles or projects.
2. Encrypt Everything
Encryption is another nonnegotiable that ensures the security of all information stored in data lakes. In a recent industry survey, 33% of cybersecurity professionals cited the lack of encryption as a primary reason for data loss.
There are two ways to encrypt data. Things at rest should be secured using AES-256 encryption, which is considered the gold standard due to its 256-bit key size and 14 rounds of encryption that further scramble the information.
Meanwhile, data in transit should be encrypted using TLS/SSL protocols. These create stable connections between servers and devices to ensure hackers cannot corrupt or intercept them during transmission.
Securely manage encryption keys using tools that offer automated rotation for stronger protection and restricted access to minimize risk. Additionally, the keys should never be kept on the same server as the data lake.
3. Implement Multifactor Authentication
Relying on passwords alone is not enough to match the voracity of hackers. Multifactor authentication (MFA) adds another layer of identity verification, making it far harder for attackers to access the lake, even if they steal a user’s login credentials.
Microsoft found that over 99.9% of compromised accounts didn’t use MFA, leaving them highly vulnerable to password reuse, phishing and password spraying. Requiring MFA for all accounts, not just administrators, adds a strong layer of protection, especially when accessing sensitive environments from new or unrecognized devices. Using a mobile authenticator app or security key before login helps ensure the data repository stays safe.
4. Set up Real-Time Anomaly Detection
Subtle threats may be more difficult to catch since data lakes hold volumes of digital assets. Leverage machine-powered anomaly detection to identify between normal and unusual events within the lake to automatically alert when something is off. This can include sudden spikes in downloads, access from unusual locations and after-hours activity, even by privileged users.
Choose tools that integrate with security information and event management (SIEM) systems for better visibility. Time is money in cybersecurity. The earlier anomalies are discovered, the more potential damage is prevented.
5. Classify and Catalog Data
It’s easy for the data lake to become a swamp, a murky dumping ground for all collected information. This can make it difficult to identify which files hold sensitive details. While going through everything stored in the reservoir is unnecessary, it gets much simpler when the records are classified as confidential, internal or public. That way, teams know what needs the most protection.
Use automated catalog tools to tag new data as it enters the lake to accelerate identification and containment in case breaches occur.
Secure the Reservoir Before It Leaks
Data lakes are highly scalable and flexible, which makes them great for storage, but they can also become treasure chests for hackers if not protected. Smart, layered protection that combines people, policies and powerful tools can balance accessibility with control to ensure nothing gets leaked to where it doesn’t belong.
As the Features Editor at ReHack, Zac Amos writes about cybersecurity, artificial intelligence, and other tech topics. He is a frequent contributor to Brilliance Security Magazine.
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