What is Data Lake Security? Importance & Best Practices

Tap into the power of your data lake while ensuring its security. Cover the latest threats, best practices, and solutions to protect your data from unauthorized access and breaches.

A security data lake is a centralized repository where data from your SaaS providers, cloud environments, networks, and devices, both on-premises and from remote locations, are kept. Security data lakes are known to improve visibility across your entire operations and manage data security.

The introduction of data lake security can benefit several organizations and analyze security data at scale. Data lake security utilizes threat intelligence modeling and forecasting to speed up investigations. Many companies are using AI-based analytics, threat-hunting tools, and data retention for compliance, all of which are included with a security data lake.

In this post, we will cover everything you need to know about them and how you can get started.

What is Data Lake Security?

Data Lake Security is a set of procedures to protect and secure data lakes. A data lake is a centralized repository that stores raw, unprocessed data in its native format. Repos may contain unstructured texts designed to handle high volumes of information from various sources.

Data lake security is crucial for Big Data and machine learning applications as it ensures data integrity and confidentiality. It is a way to prevent unauthorized data access, tampering, and unwanted manipulation.

There are various aspects that go into data lake security such as:

• Data Masking and Auditing - Data security involves masking personally identifiable information (PII) and ensuring that third parties don’t gain unauthorized access to it. It maintains a good record of all access logins, modifications, and deletions to identify potential vulnerabilities, ensure compliance, and prevent data breaches.
• Data Governance and Compliance - Good data governance ensures high-quality and availability of data for making effective business decisions. It ensures compliance with relevant regulatory standards such as HIPAA, NIST, CIS Benchmark, ISO 27001, and many others. Strong data compliance keeps customers’ data safe, builds trust, and prevents potential lawsuits. It is considered an essential component of every organization’s risk management strategy.
• Threat Monitoring and Incident Response - Real-time threat monitoring in data lake security is a vital component of effective threat remediation. It helps organizations gain a total understanding of their overall security posture. Continuous threat monitoring can reveal hidden vulnerabilities that may go unnoticed at other times. Data lake security includes an automated incident response component where the organization prevents future data breaches by taking the necessary measures. It takes steps to ensure business continuity, promote rapid disaster recovery, and create data backups for secure storage.

Why is the Security Data Lake Important?

Building a security data lake can safeguard assets in your organization and protect them from hidden and unknown threats. A security data lake can provide a robust set of features to manage assets and mitigate internal and external attacks. Data lake storage management solutions allow automation and provide ample scalability. They incorporate fine-grained access controls that allow only authorized users to view, access, modify, and delete assets. There are other well-integrated features such as data encryption, storage bucket policies, resource-based policies, and access policies as well.

SIEM vs Security Data Lake

SIEM systems are designed for real-time data monitoring, logging, and incident management. They analyze information from various sources and flag potential threats. SIEM solutions deliver actionable insights to organizations about their current security posture and offer real-time analysis.

Legacy SIEM systems struggle with scaling effectively and cannot handle the sheer volumes of data. They can also miss critical security threats, suffer from degraded performance, and lead to slower response query times. Security data lakes address the challenges posed by such SIEM solutions and offer hot storage access for quick and easy analysis.

Key Differences Between SIEM vs Security Data Lake:

SIEM	Security Data Lake
Legacy SIEM systems often come with storage limitations	A security data lake can accommodate large volumes of unstructured and structured data. It offers the added advantage of extended data retention that may last from months to years.
SIEM is a traditional option for threat detection and response	Security data lake offers advanced data analytics capabilities and business contextual data analysis
SIEM is not easy to set up, requires technical know-how to configure, and needs extensive maintenance	A security data lake is more user-friendly and accessible to non-technical users. The setup process is also easy and hassle-free.
SIEM can ingest security alerts and process or analyze data that comes in different formats. SIEM determines baselines for normal behaviors and flags anomalous or suspicious behaviors for manual review by security professionals.	The real value of a security data lake shines when it can take in not only logs and alerts. It can leverage security information from open-source intelligence information (OSINT), malware databases, external threat intelligence feeds, operation logs, IP reputation databases, and dark web sources,

Here are some other characteristics we can compare when it comes to SIEM vs Security Data Lake:

1. Cost

Most SIEM vendors charge by the amount of processed and stored which means prices can go substantially high for organizations. SIEM solutions are traditionally more expensive when compared with cloud commodity storage prices.

A security data lake’s pricing plans are far more reasonable. Many providers provide bulk storage discounts. A normal SIEM solution typically holds logs and alert data for up to less than a year. The time scope can jeopardize the health of the organization and SIEM cannot capture long-term historical data trends. Security data lakes are designed to scale and retain captured data for years instead of months and days. The larger time scope greatly benefits organizations as they can analyze historical patterns and trends. They deliver unique insights that benefit future business performance.

2. Threat Hunting Capabilities

Security data lakes can store data for longer periods and use that data to train AI/ML algorithms. They can ingest many data types, hold contextual information, and assist threat hunters via data query interfaces for further investigation.

SIEM tools can skillfully parse alerts, flag specific events, and do not include threat-hunting features with solutions. Threat hunters will need additional data for contextual analysis and SIEM faces restrictions with limited data sources when it comes to ingestion.

3. Alerts

Security teams have a tough time keeping up with the high volume of alerts generated by SIEM tools. SDLs can provide some relief by narrowing down searches on broader data sets. A security data lake can dramatically reduce investigation time but analysts will have to verify any results shown.

Limited datasets associated with SIEM tools can introduce bias and prevent proper algorithmic training. Security data lakes can work with unfiltered and larger datasets which means AI and ML models can undergo robust training and spot threats and anomalies, much more efficiently. The only downside to that is the significant testing times.

Challenges that Need to be Addressed in Data Lake Security

1. Data Reliability - Data lakes can suffer from reliability issues. If the writing job fails midway, it is up to the security team to check for any issues, fill holes, and delete or implement the necessary fixes. The good news is that a data lake makes the reprocessing job seamless and all data operations can be performed on an atomic level.
2. Data Quality Issues - Data quality problems can go undetected easily without the proper validation mechanisms in place. You don’t know when something goes wrong and you can end up making poor business decisions by relying on it. Data validation challenges associated with data lake security are - corrupted data, edge cases, and improper data types. These can break data pipelines and skew outcomes. The lack of data quality enforcement measures is the big issue here. It gets even more complicated when your datasets evolve and change throughout the entire lifecycle.
3. Combining Batch and Streaming Data - Traditional security data lakes have trouble capturing and combining streaming data with historical data in real time. Many vendors have shifted to a lambda architecture to mitigate this issue, but it requires the use of two separate code bases which are hard to maintain. You need to be able to integrate the batch and streaming sources. Getting consistent views of your diet, observing when users are making changes, and performing other operations, are all essential functions that are missing with usual solutions.
4. Compliance-Friendly Bulk Updates, Merges, and Deletes - Data lakes are not capable of performing bulk updates, merges, and deletes by the latest regulatory compliance standards.  There is no tool for ensuring data consistency and bulk modifications are very much needed. Companies may be required to delete customer data sometimes to comply with regulations or for other reasons. It can get incredibly difficult to fulfill their requests and quickly turn into a time-consuming process. Companies will need to delete data on a row-by-row basis or make data queries using SQL.
5. Poor Query and File Size Optimization - Most data lake query engines are not optimized by default. There are issues with ensuring adequate query performance and response times can be slow. Data lakes store millions of files and tables and contain several smaller files. Having too many small files that have not been optimized can slow down performance. It is necessary to accelerate the throughput and avoid processing any information that is not relevant to queries. Data caching issues also persist. Deleted files remain for up to 30 days before being permanently removed, as in the case of many solutions.

Data Lake Security Best Practices

1. Encrypt Data at Rest And in Transit - Every data lake security framework should protect sensitive information by encrypting it. It should allow users to apply server-side encryption and encrypt all network traffic across data centers at the physical layer. Users should have the option to choose from different encryption mechanisms and apply the one they want.
2. Create a Data Classification Scheme And Catalog - The data lake security solution should classify data by content, size, usage scenarios, types, and other filters. It should be possible to group data into catalogs and allow their quick search and retrieval. There should also be a method for searching the data you want and separating it from the data you want to delete.
3. Access Controls and Data Governance - Strong access controls are a must to prevent unauthorized data access. Because employees in the company can feed data from different sources without any inspection, it’s crucial to incorporate good access control. There should be a way to view, manage, and remove user permissions. Clear data management policies at work should be communicated with employees, including how to make use of the data lake, navigate complex scenarios, and promote data quality and ethical use. If any user or party performs suspicious activities, the organization should be immediately notified. Enforce data governance and privacy controls that ensure continuous compliance with the latest regulatory standards in the industry.

Why SentinelOne For Data Lake Security?

SentinelOne Singularity™ Data Lake allows users to centralize and transform data into actionable intelligence for real-time investigation and response. By using an AI-driven, unified data lake, SentinelOne provides complete flexibility to enterprise and IT security operations by rapidly ingesting data from multiple sources.

With AI-assisted monitoring, investigation, and rapid scaling capabilities, users can store their sensitive data for as long as needed. There is no need for rebalancing nodes, resource reallocations,  or expensive retention management. Its patented architecture enables lightning-fast queries in real time that can scale data in the cloud at machine speed.

Here are the key features offered by SentinelOne Singularity™ Data Lake to global organizations:

• AI-assisted analytics, automated workflows, and data ingestion from any first or third-party sources
• Automatically normalize your data using OCSF standard
• Gain visibility into threats, anomalies, and behaviors across the entire enterprise by connecting disparate and siloed datasets
• Keeps control of mission-critical data using full-stack log analytics
• Eliminates data duplication and accelerates mean-time-to-response
• Removes threats completely with the full event and log context
• Runs rapid searches across enterprise-wide data, monitors performance at scale
• Resolve alerts quickly with automated and customizable workloads and preempt issues
• Augments SIEM and automates response with built-in alert correlation and custom STAR Rules

Fore more detail, read Data Lake Best Practices

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Conclusion

Data lake security serves as a foundation for modern organizations and is designed to protect data no matter where it lives. Organizations should invest in holistic data-centric solutions like SentinelOne to easily classify and locate where their data resides. Next, after data identification, they can control user access management, set permissions, and prevent data from being stolen or breached by malicious insiders.

Relational databases were the default storage solutions in the past but SentinelOne leverages the latest advancements in data storage, capture, and analytics. You can extract real value from your raw data and take advantage of the actionable insights generated. Scale up your organization today, boost business revenue, and watch your customer loyalty grow.

You can schedule a live demo with us and try out our Singularity Data Lake’s features.

FAQ

What is a Security Data Lake?

Security data lake is a service that gives complete visibility into your entire organization and allows you to ingest data rapidly from across multiple sources. It’s a great solution for enhancing the cloud security posture of the enterprise. A security data lake is designed to centralize and transform sensitive information. It extracts actionable insights from structured and unstructured data by organizing and cleaning it up. The centralized data repository is used to run advanced data analytics, logging, and maintain data audit trials. With industry-leading performance and continuous regulatory compliance, a security data lake can significantly improve the data security management posture of an organization.