Not to mention the ever-growing variety of data types: structured logs, unstructured text, network packets, JSON feeds, and binary files.

Traditional Security Information and Event Management (SIEM) systems can’t handle the load, and scaling to keep up with the volume is cost-prohibitive.

Fortunately, today’s security data lakes offer a solution.

This article examines security data lakes: the benefits, challenges, and best practices, and how they’re making cybersecurity teams more efficient and effective.

What is a security data lake?

A security data lake is a centralized repository that can store vast amounts of structured and unstructured security data. They’re essential when the volume and variety of data exceed what traditional systems can handle, offering a cost-effective solution that’s flexible and scalable.

Unlike traditional security information and event management (SIEM) systems, a data lake can hold any type of security data regardless of format or structure. It can also store raw, unprocessed data.

Core architectural principles

Traditional systems use a schema-on-write model, which means the structure of any incoming data must already be defined for it to be stored.

But modern security lake architectures use a schema-on-read model. They store the raw data exactly as received. This allows security teams to define data structures only when they need to query or analyze data security.

These data lakes have 3 key components that work together to properly store, organize, and secure organizational data.

Integration and platforms

Security data lakes don’t replace existing systems, but support them as a central storage and analytics platform. The integration often follows a hub-and-spoke model, with the data lake as the central repository. Around it, specialized security tools continue to handle their specific functions.

There’s also a choice between hosting a security data lake in the cloud or on-premises. Cloud implementations offer unlimited storage space, managed services, and built-in security. But on-premises deployments offer total control over sensitive security data and may be required for regulatory reasons or in air-gapped environments.

Why security data lakes are needed

Traditional SIEMs have fundamental limitations. They were designed for structured log data and correlation rules, which makes them poorly suited for modern security analytics that use machine learning, behavioral analysis, and unstructured data sources.

There are also architectural constraints, as older SIEM architectures rely on predefined schemas, and valuable context is often lost in data collection.

High storage costs also make long-term data retention very expensive. Performance degradation can occur as data volumes grow, and the rigid correlation rule creates security blind spots.

Security data lakes help teams overcome these limitations by providing the storage scalability, processing flexibility, and analytical depth for modern cybersecurity operations.

Implementation benefits and business value

Security data lakes offer many benefits for organizations looking to strengthen their security measures.

Real-time threat detection

Security data lakes can detect cyber attacks much faster than traditional rule-bound systems — this speed results from searching all security data simultaneously, rather than querying each system separately.

When hackers strike, faster detection means less damage and lower recovery costs.

With today’s automated pipelines and metadata catalogs that organize incoming logs by source and type, teams can immediately use the pre-sorted data to link failed logins, access patterns, and network anomalies.

Simplified compliance with detailed audit trails

Many businesses must comply with strict data protection regulations like GDPR, HIPAA, and SOC 2. Security data lakes aid in these compliance efforts by keeping a detailed record of all security events. When auditors come knocking, companies can produce the reports they request.

The system automatically logs who accessed what data and when, leaving behind a clear audit trail, saving hours of manual work, and reducing the risk of expensive breaches.

Unified analytics across systems and teams

Many companies' security information is spread across systems that don’t talk to each other.

Fivetran's pre-built connectors let organizations automate and unify data across systems and services into a centralized security data lake.

This gives security teams a complete view of what’s happening across the organization. It helps them connect the dots between unrelated events and catch sophisticated attacks that'd otherwise slip through the cracks.

Cost savings

Traditional security systems can get very expensive as operations scale, and adding more data storage or processing power can cost hundreds of thousands of dollars.

Today’s security data lakes use cloud storage and smart data management. Consumption-based pricing and free tiers give teams more affordable options for managing growing data volumes and scaling their pipelines.

Better analysis and flexibility

Modern security systems generate many different data types, from log files to network traffic data. Security data lakes can store and analyze all of it without converting data.

Data lakes offer new ways to analyze security data. They can run complex searches, find unusual patterns, and use machine learning to predict future threats. Unlike rigid traditional systems, data lakes adapt to whatever questions security teams need to answer.

Today's security teams can investigate new types of attacks without needing additional software, and they no longer have to discard valuable security information just because their current systems can’t handle it.

Challenges and considerations

Although new technology provides several benefits, it always comes with challenges — security data lakes are no exception. However, as with many challenges, knowing about them can help mitigate them.

Data quality and access control

Security data lakes can quickly turn into swamps without proper management.

Data from different systems comes in different formats, making it hard to compare and analyze. Organizations need clear rules about who can access what information to avoid this, especially when dealing with sensitive security data.

Setting up proper permissions and data governance to meet applicable privacy and compliance standards requires ongoing attention and resources.

Tools like Fivetran can help support and automate these compliance efforts through column blocking, column hashing, and role-based access control.

Integration and governance issues

Connecting a security data lake to existing systems requires careful planning and analysis.

Legacy security tools may not easily integrate with modern data lake platforms, creating technical headaches. Organizations need strong governance policies to manage their data properly.

Companies risk fines and penalties for noncompliance and security breaches without clear rules about data retention, privacy, and usage.

Avoiding the data swamp problem

The most significant risk with security data lakes is their gradual devolution into “data swamps.” These cluttered dumping grounds result from poor organization, planning efforts, or both.

To keep lakes in working order:

• Define transparent processes and use cases up front.
• Determine what questions need answers and what data is required to answer them.
• Tag and catalog data as it comes in to be found later.
• Schedule regular cleanups to remove outdated or irrelevant data.
• Appoint a data lake manager.

Skills and tooling gaps

Security data lakes require skills that are different from those of traditional security systems. Teams need people who understand cybersecurity and data analytics, a rare combination.

The most cost-effective way to find qualified staff is to invest heavily in training current employees and give them the tools they’ll need to manage and analyze the data lake.

Performance challenges

As security data lakes grow, they can become slower to search and analyze. To avoid this, it is essential to plan data architectures carefully and optimize systems regularly.

Without these performance tuning and monitoring efforts, security teams may wait too long for critical threat intelligence, undermining the system's effectiveness.

Key use cases and applications

Security data lakes support various critical functions, making them indispensable tools for modern cybersecurity operations and use cases.

Advanced threat hunting and behavioral analytics

Security teams use data lakes to hunt for hidden threats that automated systems might miss actively. By analyzing patterns in user behavior, network traffic, and system activities, they can spot unusual activities that may reveal vulnerabilities or signal possible attacks.

For example, if employees suddenly access files they’ve never worked with or network traffic suddenly spikes during off hours, these patterns can indicate ongoing security breaches requiring immediate action.

Compliance and long-term storage

Many industries must keep security logs for years to meet regulatory requirements. Security data lakes provide cost-effective storage for this historical data, making it easily searchable when auditors knock.

Whatever the regulations, properly storing and organizing all security events helps companies pass audits and avoid expensive fines and penalties.

Incident response and forensics

When security incidents occur, investigators need a way to determine what happened.

Security data lakes give them a complete timeline of events across all systems. They can trace an attacker’s steps from initial entry to data theft, helping them understand the full extent of the damage and prevent similar attacks in the future.

AI-powered security insights

Machine learning algorithms can analyze massive amounts of security data to predict and prevent attacks.

They learn what regular activity looks like and flag anything suspicious. As they process more data, they’ll improve and eventually start catching threats that human analysts may miss.

Cross-platform analysis

Modern organizations use many different security tools and systems. Security data lakes combine all this information, allowing teams to see connections between events across various platforms.

An attack might start on email systems, move onto the network, and end in databases. Data lakes help security teams see the whole picture.

Real-time monitoring and alerting

Security data lakes support continuous event monitoring, flagging suspicious or unusual events as they happen.

Teams can set up automated alerts for specific threats and investigate historical patterns that might indicate long-term, sophisticated attack campaigns.

Security data lake best practices

Organizations should adhere to certain standard operating procedures and best practices to harness the power of a security data lake and avoid the pitfalls of a data swamp.

Strategic planning and use case definition

This is getting repetitive, but it’s important and worth repeating:

• Start with clear goals before building your security data lake.
• Define specific problems you want to solve, such as faster threat detection or better audit logs.
• Write down exactly what questions you need to answer and what data will answer them.

This focused approach prevents you from collecting everything “just in case” and helps justify the investment to business leaders.

Data classification and lifecycle management

• Create rules for categorizing information based on its sensitivity and value.
• Set up automatic processes to move older, less critical data to cheaper storage while keeping recent high-priority details easily accessible.
• Establish clear retention policies that balance compliance requirements with storage costs.
• Delete data that no longer serves a purpose to reduce clutter and security risks.

Smart tool selection and integration

• Choose tools that work together and fit your existing security infrastructure.
• Don’t buy everything at once; start with core capabilities and add features as you learn what works.
• Make sure new tools can connect to your current systems without major changes.
• Consider cloud-based solutions that can grow with you and reduce maintenance overhead.

Team training and skill development

Security data lakes require skills that are different from traditional security tools.

• Invest heavily in training your security team on data lake technologies and analytics techniques.
• Create clear documentation and procedures so knowledge isn’t trapped in a few people’s brains.

Governance and security controls

• Establish strong access controls and audit trails for your security data lake itself.
• Set up monitoring to detect unauthorized access or unusual activity within the lake.
• Create backup and disaster recovery plans to protect your security data from loss or corruption.

Performance monitoring and optimization

• Automate data integration for efficient, governed, and analytics-ready data lakes.
• Monitor query performance and system health regularly.
• Set up alerts for slow searches or system issues impacting threat detection capabilities.
• Schedule regular maintenance to optimize storage and improve response times as your data grows.

Secure your data with Fivetran

Security data lakes are a significant shift in cybersecurity analytics and threat detection. They address the limitations of traditional SIEM systems with scalability, flexibility, and analytical depth.

While there’s a fine line between building a valuable security asset and funding an expensive data swamp, teams can find it with careful planning and execution.

Learn more about how Fivetran’s Managed Data Lake Service can elevate your security data lake.

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