In 2024, the global average data breach cost had climbed to an unprecedented $4.88 million. According to the IBM Cost of a Data Breach Report 2024, this was a 10% increase from the previous year. As breaches become increasingly costly, the urgency to secure data lakes has never been more apparent. It highlights growing financial risks and the critical need for more robust cloud security measures.
Organizations need to work on reducing data breaches and keep their sensitive data safe. This post will cover the critical steps to securing your data lake. You will learn to handle access, encryption, compliance issues, and secure user permissions. You will also discover and implement the top data lake security best practices. Now, let’s get started.
What Is a Data Lake?
A data lake is a storage system that retains huge volumes of raw data in a centralized repository. Unlike a database, it doesn’t impose strict rules or structure on the data.
Because of this, it becomes the perfect fit to store both structured data like SQL tables and unstructured data, such as images or logs. This flexibility allows organizations to store all types of data, ready to be processed and analyzed as needed.
But with such flexibility comes the need for robust security.
What Is Data Lake Security?
Data lake security refers to securing large volumes of both structured and unstructured data stored in a data lake. It covers a wide array of parameters, from encryption to setting up access controls that have to be continuously monitored to ward off unauthorized access.
Data lakes are becoming larger and more complex, which makes their security even more crucial. Let’s now explore the reasons why data lake security is vital.
The Need for Data Lake Security
Security is critical in data lakes, which store vast volumes of personal information, financial records, and business data. Without proper safeguards, they become prime targets for hackers.
Data lakes consolidate information from various sources, making them complex and more difficult to secure. One small vulnerability can expose the whole ecosystem of data, leading to huge financial and reputational damage.
An organization exposing its data lake can result in identity theft or fraud, particularly if it contains customer information. In healthcare, a breach could expose patient records, violating laws like HIPAA.
Security Challenges of Data Lakes
Securing data lakes is bound to several pitfalls resulting from the scale, complexity, and rich types of data they store. Some of these challenges include large data volumes, unstructured data, access management, and regulatory compliance.
1. Large Data Volume
Data lakes contain a huge amount of information coming from different sources, and it’s pretty tricky to track and keep everything secure properly. A breach at one point may affect the entire system.
2. Unstructured Data Management
Data lakes typically store unstructured data (e.g., documents, videos, images) that lacks predefined formats. This presents challenges for classification, making it difficult to consistently apply security policies such as access control, encryption, and monitoring. As a result, the likelihood of data breaches or unauthorized access increases.
3. Access Management
In data lakes, numerous teams or departments might be accessing sensitive data. Without strict access control and user permission, unauthorized access is a high risk.
4. Regulatory Compliance
For some specific industries like healthcare and finance, there are rather strict regulations, including GDPR and HIPAA. Oversight in ensuring that a data lake meets these standards often involves labor-intensive processes and audits.
Data Lake Security Best Practices
Implementing best practices is essential to minimize risk and safeguard the data lake. Let’s explore key security strategies every organization should implement to strengthen the security of its data lakes.
#1. Network Segmentation
Implementing segmentation in the data lake allows you to separate sensitive information into distinct sections. This reduces the likelihood of a large-scale breach by reducing the attack surface. If an attacker gains access to one segment, they’ll be unable to readily access other areas of the data lake, limiting potential damage.
#2. Using Firewalls
Firewalls act like gatekeepers. They monitor the incoming and outgoing traffic, ensuring that only authorized users and data can enter or leave the data lake. If they’re well configured, they block questionable activities before damage may incur.
#3. At-Rest Encryption
At-rest encryption protects data stored in the lake. The data isn’t viewable without keys, so there’s no chance of unauthorized access. In the same way, when there’s a data breach, the encrypted files are still useless to the attackers because they need keys to decrypt the files.
#4. In-Transit Encryption
In-transit encryption secures data as it’s moved between systems—for example, moving data from the data lake to other ecosystems. Various encryption protocols keep data secure between transmissions. They prevent anyone from intercepting or tampering with it.
#5. Multi-Factor Authentication
In addition to encryption, multi-factor authentication adds another layer of security. It requires not only a password but also an additional form of verification, such as a one-time code sent to the user’s phone. This way, even if someone obtains the password, they can’t access the system without the second factor, ensuring stronger protection.
#6. Strong Password Policies
Strong password policies play a critical role by requiring users to create long, complex passwords and update them regularly. This approach actively reduces the risk of using weak or compromised passwords.
#7. Continuous Monitoring
Continuous monitoring means monitoring what’s happening in a data lake in real-time. It would be good to catch suspicious behavior when it happens. For example, if someone unauthorized attempts to access sensitive data, such an action can be highlighted right there and then. Continuous monitoring also helps detect sudden spikes in data usage, which could signal a breach.
#8. Anomaly Detection and Response
Anomaly detection tools are designed to find unusual patterns in the data lake. For example, if an employee suddenly starts downloading large volumes of sensitive data outside working hours, the system can automatically detect that behavior as strange. You can then set up automatic responses to block access or send an alert to the security team for further investigation.
#9. Regular Backup
Regular backups are a must-have for data lake security. If data gets corrupted or lost due to an attack or system failure, backups let you quickly restore everything. For example, setting up daily or weekly backups for critical data means you won’t lose weeks’ worth of work if something goes wrong. It also keeps operations running smoothly, minimizing downtime.
#10. Disaster Recovery Planning
Disaster recovery planning involves having a clear plan for restoring data and resuming services if something big happens, like a cyberattack or natural disaster. Your plan should cover how to get data back from backups, reestablish services, and communicate with your team. For instance, having backups stored at a secondary location means you can switch over quickly if your main site goes down.
#11. Backup Integrity Testing
You need to test your backup regularly. You don’t want to wait for an actual fire to find out that something’s broken. Run simulated recoveries every quarter to make sure you can restore data properly and identify any issues. However, to further enhance security, leveraging advanced tools is crucial. This brings us to the next step.
Enhancing Data Lake Security with SentinelOne
SentinelOne extends data lake security with its specialized products, Singularity Data Lake and Singularity Cloud Data Security.
Singularity Data Lake unifies real-time security analytics with threat detection and response on a single platform for security information and event management (SIEM) and extended detection and response (XDR). It uses AI to correlate security events, detect advanced threats, and automate responses. By doing so, security teams are at an advantage with centralized visibility for easier tracking and follow-up on suspicious behavior in large-scale data lake environments.
Singularity™ Cloud Data Security keeps cloud-based data lakes safe using AI-powered malware scanning and automated responses across cloud storage solutions such as Amazon S3. It performs inline scanning, quarantining malicious files within milliseconds while ensuring integrity among cloud-stored data. Singularity™ Data Lake removes the burden of data wrangling; it ingests data from any first or third-party source using pre-built connectors and automatically normalizes using the OCSF standard. You get complete visibility into your entire cloud estate and protect your mission-critical assets.
This provides immense value to organizations operating in hybrid or multi-cloud environments where security must be prioritized as data scales up.
With these solutions, SentinelOne offers robust protection against cyber threats like ransomware or malware, thus securing on-premises and cloud-based data lakes.
Securing Your Data Lake: A Vital Investment for the Future
While the volume and complexity of data stored in data lakes continue to grow, so does the associated risk. Approaches like network segmentation, encryption of data, and monitoring can reduce the exposure of sensitive data and eventually minimize the harm in cases of a potential breach. By taking these important steps, organizations can protect their data lakes from the increasing financial and reputational damage caused by cyberattacks.
In that respect, investment in data lake security isn’t an option but a necessity. Equipped with advanced integrated tools, such as SentinelOne powered by AI, organizations can stay ahead of threats while their data lakes remain secure, compliant, and resilient against ever-evolving cyber risks.
Faqs
1. Why you should consider a data lake?
Data lakes ensure scalability, flexibility, and cost efficiency in storing structured and unstructured data. They allow businesses to analyze large datasets for insights to make better decisions.
2. Are data lakes secure?
While data lakes may be secure, their complicated nature opens them to vulnerabilities when they’re not properly managed. Best practices such as access controls and encryption should be instituted so that sensitive information is kept secure.
3. What is a security data lake?
A security data lake is a specialized data lake that collects and analyzes security logs and data. It helps in detecting threats and supports proactive threat-hunting efforts.