What Is a Data Lake (and a Security Data Lake)?

In today’s era of big data, artificial intelligence (AI)-based analytics, and machine learning (ML), data lakes have become critical components of modern enterprise and security strategies. The term “data lake” was coined by James Dixon of Pentaho in 2010, giving a name to a new concept for storing, analyzing, and managing raw data. According to Gartner, “A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the source format and are in addition to the originating data stores.”

The exponential growth in data volume, variety, and velocity has created an urgent need for scalable big data storage solutions. Today, the data lake market is growing by double digits—estimated at between $16 billion and $20 billion in 2024 and projected to reach $34.07 billion by 2030.

However, as businesses increasingly adopt general and security data lake architectures, data protection and governance become paramount. Without robust measures to ensure the integrity and privacy of sensitive data, organizations risk exposing themselves to cyberthreats. Companies like Zscaler, which specialize in unified data protection and zero trust security, address these challenges by safeguarding sensitive data at rest and in motion across cloud environments.

Unlike other types of data storage, such as data warehouses (which operate on a schema-on-write model requiring data to be structured before storage), data lakes leverage a schema-on-read architecture. Specifically, data lakes and security data lakes:

1. Accept all types of data without rigid format restrictions.
2. Store data in its original form rather than applying a predefined schema first.
3. Transform data on demand only when it is queried or analyzed.

Storing All Types of Data (General and Security Telemetry)

IDC estimates there will be 175 zettabytes of data globally by 2025, with 80% of that data being unstructured. By decoupling storage from rigid schemas, data lakes ensure that no valuable context is discarded. They store a mix of:

• Structured data: Rows in a database or neatly formatted spreadsheets.
• Semi-structured data: Formats like JSON, XML, CSV, or NoSQL data formats.
• Unstructured data: Emails, documents, videos, and IoT sensor readings.

When deployed specifically as a security data lake, the repository ingests and centralizes security-specific data sources across the organization’s network, including:

• Logs
• Alerts
• Network traffic
• Endpoint telemetry
• Threat intelligence feeds
• Malware databases
• Vulnerability scans

Core Components of Data Lake Architecture

Building or leveraging a data lake requires scalable storage capabilities, processing power, and strong security controls. The architecture consists of four primary layers:

• Storage layer: The foundation of any data lake, implemented in the cloud (e.g., AWS S3, Azure Data Lake), on-premises, or in a hybrid environment.
• Data ingestion tools: Solutions like Apache Kafka, AWS Glue, or Apache NiFi handle the continuous flow of structured and unstructured data for real-time or batch ETL pipelines.
• Data catalog: Metadata management tools like Informatica or Talend organize data assets, track lineage, and establish governance policies.
• Analytics tools: Platforms such as Apache Spark, Presto, or Dremio empower teams to query data using SQL, full-text search, and AI/ML algorithms to transform raw data into actionable insights.

• Analyzing security data is fundamental to gaining insights into risks, threats, and overall security posture. Storing security telemetry in rigid, structured databases can lead to the unwitting deletion of information or context that analysts may need in the future.
• An Aberdeen survey found that companies leveraging data lakes outperformed their peers by 9% in organic revenue growth, thanks to rapid, data-driven decision-making. For cybersecurity teams, security data lakes support threat detection, incident response, and threat hunting, as well as compliance and auditing. Because a security data lake serves a broad range of users, it delivers a strong return on investment across roles:
• Security analysts can take advantage of extended data retention periods—spanning months or years, compared to a legacy SIEM’s typical 30- to 90-day window—providing critical historical context for investigating incidents.
• Data scientists can use massive, unfiltered data sets to construct AI and ML models that reveal subtle threat patterns and anomalies.
• Less-technical users can more easily learn to query a security data lake than navigate a highly complex SIEM tool.
• Security leaders can improve decision-making and board-level reporting with data-driven, actionable insights..

Consolidating enterprise data and threat intelligence into a unified repository provides several operational and security advantages:

• Holistic view and stronger security: When data is dispersed across siloed tools and cloud services, identifying threats is difficult. Centralizing all telemetry makes it easier to identify vulnerabilities faster and spot threats that might be missed with a piecemeal approach.
• Scalability: Data lakes effortlessly accommodate fast-growing data volumes—from terabytes to petabytes—without performance degradation, ensuring long-term storage for historical analysis and threat hunting.
• Flexibility: A data lake aggregates and correlates data in any format without requiring a predefined schema.
• On-demand advanced analytics: Without needing complex infrastructure to pre-process data, teams can combine structured and unstructured data to run AI/ML algorithms that identify patterns, correlations, and anomalies.
• Cost-effectiveness: Rather than maintaining expensive, dedicated hardware for constant processing, data lakes leverage affordable cloud storage that charges only for the compute power used during analysis.
• Data democratization and collaboration: Centralized access to the same dataset facilitates cross-functional teamwork across security, IT, compliance, and business operations while maintaining proper governance.

Despite their flexibility, data lakes and security data lakes introduce unique operational and security challenges that organizations must manage:

• Prime targets for cyberattacks: The massive amounts of sensitive information stored in security data lakes make them tempting targets for malicious actors and insiders. Attack vectors include phishing, SQL injection, zero-day exploits, and man-in-the-middle attacks. Protecting these repositories requires granular, role-based access controls and zero trust architectures.
• Privacy and regulatory compliance: Vast data volumes must be kept private and properly governed to comply with regulations such as Europe’s GDPR, the U.S. Federal HIPAA statute, and the California Consumer Privacy Act.
• Data sprawl and “data swamps”: Without strong governance, ingested data can grow uncontrollably, driving up storage costs and turning the repository into a “data swamp” filled with disorganized, duplicated, or irrelevant data that obscures critical security insights.
• Complexity of stack integration: Legacy systems and existing security stacks can struggle to integrate seamlessly with modern data lake solutions, creating bottlenecks in ETL pipelines.
• High in-house costs and skills shortages: Building and maintaining a security data lake in-house requires substantial upfront and ongoing investment. Because security data lakes are relatively new, organizations may also face a shortage of qualified staff to manage them.

Security Data Lake vs. SIEM

Traditional security information and event management (SIEM) systems predate the Big Data and cloud eras. As security telemetry exploded, SIEM vendors sharply increased processing and storage fees, forcing organizations to either filter out telemetry or shorten retention windows. Furthermore, legacy SIEM software often lacks robust user and entity behavior analytics (UEBA), hindering the detection of compromised accounts or insider threats.

Here is how security data lakes and SIEMs compare across critical SecOps functions:

• Data ingestion: SIEM solutions typically ingest structured data or auto-normalize security data as it is ingested (schema-on-write). Security data lakes ingest raw data in its source format (schema-on-read).
• Data storage and retention: SIEM vendors charge high fees based on ingested data volume and retention length, often storing logs for less than a year. Security data lakes use commodity cloud storage to sharply reduce fees and retain data for years.
• Infrastructure: While neither holds an inherent infrastructure advantage, organizations with an existing enterprise data lake can easily add security telemetry to that repository. However, security teams accustomed to SIEM workflows may face a learning curve when adopting a security data lake.
• Threat hunting: To support proactive threat hunting, security data lakes ingest more data types, hold larger volumes for longer periods, and provide direct query interfaces. SIEMs issue alerts and flag events, but deep threat hunting often has to be performed outside the tool.
• AI and ML training: A limited dataset inhibits algorithm training. A security data lake’s massive, unfiltered dataset offers far better opportunities to train AI and ML threat detection models than a restricted SIEM dataset.

Next-Gen SIEM + Security Data Lake: Solving the Data Problem

Acknowledging the limitations of legacy SIEM tools, the industry has developed “next-gen” SIEM technologies that incorporate ML, UEBA, automation, and cloud-native connectors. Still, next-gen SIEMs can be resource-intensive to manage and struggle with massive, low-fidelity log storage.

By combining the strengths of next-gen SIEMs and security data lakes, organizations get the best of both worlds: next-gen SIEMs excel at real-time threat detection and automated response, while security data lakes enable cost-effective long-term retention, advanced analytics, and proactive threat hunting to adapt to emerging threats.

Beyond dedicated cybersecurity operations—where data lakes aggregate logs across SIEM, SOAR, and endpoint systems—cloud native data lakes power critical use cases across industries:

• Healthcare: Storing genomic and patient data for personalized medicine and disease prediction.
• Finance: Detecting fraud and automating compliance reporting by analyzing transaction logs and customer behavior.
• Retail: Optimizing inventory and personalizing customer experiences through behavior analytics.
• IoT: Processing telemetry from connected devices for predictive maintenance.

With the data lake market projected to grow at a CAGR of 23.8% from 2024 to 2030, several key developments are reshaping how organizations manage and secure their data:

• Lakehouse Architecture: Blurring the line between data lakes and data warehouses, lakehouses combine low-cost, flexible storage with structured performance, reducing ETL overhead while maintaining zero trust governance across hybrid environments.
• AI-Driven Governance: Automated metadata tagging, cataloging, and classification at scale enhance data security by giving cybersecurity teams real-time visibility into sensitive data flows.
• Edge Data Lakes: With worldwide spending on edge computing forecast by IDC to reach $378 billion in 2028, edge data lakes process IoT data closer to its source to reduce latency—requiring unified data protection frameworks that extend to decentralized edge environments.
• Multicloud Strategies: Enterprises increasingly distribute data lakes across multiple cloud providers to avoid vendor lock-in and improve resilience, demanding zero trust segmentation and unified threat visibility across platforms.

Data lakes and security data lakes represent a transformative approach to managing big data and security telemetry, offering unparalleled scalability, flexibility, and historical insights.

When executing your security data strategy, you shouldn't have to choose between cost optimization and comprehensive protection. Red Canary’s Security Data Lake helps security teams unlock high-fidelity insights and extended retention while optimizing storage costs. At the same time, Zscaler Data Security secures your enterprise data lakes across all channels, enforcing zero trust access, preventing data loss, and ensuring continuous compliance. Together, Zscaler and Red Canary empower organizations to harness the full analytical power of their data without compromising on security.