Top 11 Data Lake Security Best Practices

Enforcing these data lake security best practices can provide reliable threat intelligence and eliminate false positives. Learn how you can enhance protection for your organization today.

A data lake is a central repo that stores a huge volume of raw data in its original format, be it structured, unstructured, or even semi-structured. It can ingest and store data from multiple sources. You can get insights from this data for later use to improve business workflows, applications, and intelligence.

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.

Need for Data Lake Security

How do you know you are collecting the right logs and what data do you own? What about how you use data in the organization and how it flows? What do you generate in and out of systems? Are you sure of your data's use cases?

If you're not confident about your data's format or seem to keep losing track of information, a data lake can be really useful. Data lake security protects your data lake and keeps the environment safe.

Data lake security is critical for 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. Data lake security can tell you what’s wrong with your current configuration and help you see if it’s working right as intended.

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. Here are the most common data lake security challenges:

1. Managing Large Data Volumes - 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. Identity & 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.
5. Platform & Infrastructure Risks - You may encounter vulnerability risks from misconfigured cloud services, unpatched systems, and weak network controls that could expose your entire data repository. Cloud-based data lakes often have inadequate infrastructure hardening, where unnecessary services and ports remain active; they can create and expand new attack surfaces. If you store data across multiple cloud environments, managing consistent security policies becomes complex.

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 & Firewalls

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.

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.

#2. At-Rest & In-Transit 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.

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.

#3. Multi-Factor Authentication & Identity Governance

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.

Data lake identity governance involves building a framework of policies, processes, and controls. These are used to manage access, use, and the sharing rights to sensitive information. They include role-based access controls (RBAC), data catalogs, data lineage tracking, auditing, and data classification. Data lake governance can help you improve data quality, trust, and enhance the accuracy and reliability of your data.

#4. Strong Password Policies & Credential Hygiene

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.

One-way hashing can protect credentials even when hashes get compromised, especially when combined with Multi-factor Authentication (MFA). You should set passwords that are at least 16 characters long and use multi-word pass phrases. Mix up uppercase and lowercase letters, numbers, and symbols. Don't make your passwords easily guessable and avoid using personal information in them. Every password for each account should be unique and not repeated to prevent credential stuffing. You can also use password managers to store and autofill these passwords for different accounts, and even the Federal Trade Commission recommends it.

Good credential hygiene for strong data lake security will include things like regular auditing, enforcing the principle of least privilege access, and doing secrets management properly. It will also involve educating your users on using the best credential hygiene practices for data lakes. They will learn how to recognize phishing attempts and avoid sharing their credentials to strangers or unknown individuals online.

#5. Access Controls

Access controls will include Access Control Lists (ACLs) to manage user permissions. It will let only authorized users view, modify, and interact with specific data. Good data lake security will also include assigning permissions to users based on their respective jobs and responsibilities. These permissions can be organized and applied on a scope level as well. You can also unite all roles assigned to a single user and get granular controls over files and directories within the data lake.

#6. Continuous Monitoring & AI-Powered Anomaly Detection

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.

AI-powered anomaly detection focuses on identifying data patterns that don't conform to expected behaviors within the data lake. It monitors stores, batch inserts, and enhances data integrity.

#7. Audit Logging & Security Analytics

Audit logging will help you ensure data lake compliance and centralise security data from various sources. You should configure your data lake platform to log every query execution, access event, and changes to data in centralized log stores.

Set up automatic log parsing to extract necessary security events such as failed logins, privilege escalation, and unusual data behavior patterns. Make security analytics dashboards monitor user behavior baselines and automatically flag anomalies such as off-hour access or excessive downloads of data.

#8. Data Classification, Cataloging & Lineage

Do automated data classification by implementing scanning tools that identify sensitive data types like PII, financial records, and healthcare information across your data lake. Create data catalogs that automatically discover and document data assets, including metadata, business descriptions, and quality metrics.

You can do comprehensive data lineage tracking by visualizing data flow from source systems via transformations to final consumption points. Tag sensitive data with classification labels and apply appropriate security policies based on data sensitivity levels. Before schema changes occur, enable automated lineage updates to ensure your downstream impact analysis remains accurate.

#9. Data Isolation & Logical Zone Structuring

For data isolation, you can use temporary network connections with layered access controls.  You can enhance your isolation with air gap technology that meets RTO/RPO objectives. There are four levels of data isolation - read uncommitted, read committed, repeatable read, and serializable. You can implement isolation via AVID transactions and use a mix of optimistic and pessimistic transaction models.

You can do logical zone structuring to ensure that data quality improves at every stage. The Medallion Architecture is a common example of logical zone structuring in data lakes. It uses 3 zones: bronze, silver, and gold.

You can use these zones by defining clear data flows. Enforce one-way movement and use automated pipelines to move data between these zones. You can also apply role-based access controls (RBAC), use data catalogs, and store data in the right file formats.

#10. Backup, Integrity Testing & Disaster Recovery

Use immutable backups that are based on the write-once-read-many (WORM) model. Prevent your backups from getting encrypted, altered, and deleted by using immutable backups. Turn on object versioning for storage and use air-gapped backups for best results. Also, separate your accounts and permissions.

You should also backup schemas and backup data catalogs for restoring data discoverability. Include all your stakeholders, IT team and security members throughout the phased testing process. Walk through tabletop exercises, mock drills, and do full failover tests. Automate switching to backups during disasters and enable cross-region replication by using automated data recovery solutions.

You also want to define the maximum acceptable downtime and data loss to dictate your backup frequency for high and low-priority data in your data lake.

#11. Incident Response Planning

Build a cross-functional incident response team. Define roles, responsibilities, and create detailed, scenario-specific playbooks. Do regular game-day simulations and build secure out-of-band communication channels. Invest in data logging and monitoring and use tools like SentinelOne that can do anomaly detection and generate threat intelligence.

You should also set up real-time alerts and use an AI-SIEM to aggregate logs from all components of your data lake. For containment, eradication and recovery, you should apply security patches, revoke unneeded access, and restore from clean backups. Do forensic imaging of your data stores and conduct a blameless post-mortem analysis. Update your risk registry as well.

#12. Regulatory Frameworks (GDPR, HIPAA, SOX)

You should align compliance programs with GDPR’s data-protection-by-design requirements, including documented lawful processing bases, data-subject rights procedures, and 72-hour breach notification. You can meet HIPAA by enforcing role-based access controls, maintaining audit trails, and running regular risk assessments on patient health information. If you follow SOX, maintain accurate financial records with tested internal controls, executive certifications, and automated change monitoring for seven-year data retention.

#13. Security Standards (ISO 27040, NIST 800-88, FIPS)

Apply ISO/IEC 27040:2024’s storage controls—encryption, key management, logging, and sanitization—for SAN, NAS, and cloud systems. If you need secure media sanitization, use NIST SP 800-88’s Clear, Purge, and Destroy methods based on data sensitivity and reuse plans. You can adopt FIPS 140-2 Levels 1–4 for validated cryptographic modules, adding tamper-evidence and identity-based authentication as risk increases.

#14. Governance Automation & Policy Enforcement

Deploy automated policy engines that scan metadata and enforce rules in real time, storing policies centrally with full audit logs. You should set exception-based alerts for policy deviations and automate data classification across all sources. If you integrate governance with SIEM, you’ll enable rapid violation detection, automated containment, and incident workflows.

#15. Leveraging Delta Lake & ACID Transactions

ACID transactions are new to data lakes and can build data reliability. You can use Delta Lake which is an open-source option to build consistency for data warehouses used with data lakes. ACID properties are database transaction properties that you can find in traditional relational database management systems.

Delta Lake can scale metadata handling, batch and stream unification, and is also very compatible with the Apache Spark API.

#16. Optimizing Query Performance & Secure Data Access

You can improve query performance by indexing frequently accessed columns and partitioning large tables to reduce scan times. If you apply caching at the application or database layer, repeated queries will return results faster while reducing compute workloads. You should enforce least-privilege access by granting roles only the specific permissions needed for each user or service.

When you enable row-level security, you will restrict data retrieval to authorized rows based on user context. Implement parameterized queries to prevent injection attacks and ensure that queries run with stored execution plans. If you monitor query execution metrics, you can identify slow operations and adjust how you allocate your resources.

Enhancing Data Lake Security with SentinelOne

Singularity™ Data Lake can level up your security posture by helping you get more out of your data. You will receive actionable insights from across your organization all in one place. It will help you turn your data into your strongest security asset.

You can detect threats in real-time with its AI-powered threat hunting capabilities and stay ahead of attackers. It will grant greater visibility and help you normalize all your data into OCSF as your organization grows.

Other data lakes often come with limited capabilities and a high price tag. But Singularity™ Data Lake is a comprehensive solution with predictable, transparent, and affordable pricing that ensures you get the most value from your investment.

Securing Your Data Lake: A Vital Investment for the Future

Your data lake can be a prime target for attackers, ransomware, and financial and reputational risks. Securing your data lake is a vital investment because it can help protect your customers and keep your sensitive data safe from various data lake security risks. Strong data lake security is all about implementing the right measures. You can turn your data lake into an avenue for innovation, insight, and business growth. Contact SentinelOne to find out how today and get assistance.

FAQs

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.

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.

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.