Optimize Your Threat Detection across Distributed Data Lake Architecture on Snowflake, Azure, Splunk and Beyond

Optimize Your Threat Detection across Distributed Data Lake Architecture on Snowflake, Azure, Splunk and Beyond

Detection Strategies
From Chaos to Clarity: How to implement efficient data & threat detection strategies to scale your SOC

By Kevin Gonzalez, Senior Director, Security & Operations, Anvilogic

Centralized security data allows for streamlined operations and deeper insights for data-driven decisions to be made. So it’s not surprising that 62% of organizations are at various stages of data lake implementation and 25% of organizations plan to implement a data lake within the next three years. 

While it’s great to see data lakes gaining so much momentum, in order to do their job, the Security Operations Center (SOC) requires access to the data across the entire enterprise to assess and analyze real-time security threats to the business. While data lakes are a step towards centralization, enabling organizations to store vast amounts of raw and diverse security data in their native format, they still face the challenge of siloed data.

Given that choosing the right data lake technology and architecture impacts an enterprise’s ability to see and address potential security threats, it is important to fully understand the different options. 

Rethinking Data Lake Architecture and Logging Strategies for Today's Threat and Economic Landscapes

Whether you decide on a mesh approach with distributed lakes, a cloud-based approach, or an on-premise strategy, implementing a data lake so that detection engineers can better protect the digital security of the business requires navigating data ingestion, integration, and governance complexities and all the disparate tools and platforms used to capture and organize that data. Additionally, the variety and volume of data in data lakes can pose data processing and detection development challenges. From detections written in SQL, KQL, or SIEM-specific languages like Splunk’s SPL, to the utilization of Python notebooks and various data science models for threat hunting, detection engineers have to be subject matter experts in detection languages themselves, and the threats they are tasked in detecting.

Unless you like to play Whack-a-Mole, this is not a tenable solution. Relying on data collection and organization tools like the traditional SIEM to analyze potential threats requires constant updating of the analysis methods and, more importantly, puts the onus of observability onto the security engineer. 

For detection engineers to efficiently identify and thwart potential threat actors, the data logging and analytics layers need to be decoupled. This allows SOC teams to work across distributed data lake architectures, streamline security operations, and improve response agility, while also reducing vendor lock-in giving CISOs flexibility for more cost-effective options.

Navigating Decentralization and Unifying Operations in a Distributed Data Lake Architecture

Even as businesses embrace data lake architectures, getting them to an ideal state is a constant struggle. Just throw another merger or acquisition, and you start from scratch again. And, if you are using a traditional SIEM architecture to analyze the various log data for threat detection, every new data source becomes a headache for the multiple teams required to collaborate together to get each data source in a usable state. 

But a decoupled, purpose-built threat detection platform that can work across distributed data lake architectures solves this problem. You’ll no longer need to modify detection logic, hunting notebooks, data science models or wait for IT to prepare data sources. Each data lake can be connected to the threat detection platform which can analyze and detect threats using a unified set of detection logic and advanced AI.

Decoupling allows the SOC to operate cohesively in decentralized data lake architectures. It also alleviates the cost and political implications associated with data migration. Additionally, it enables unified querying and analysis across multiple data lake architectures which streamlines operations.

Streamlining Security Operations with a Unified Detection Layer

SOC engineers are bogged down by non-security-related tasks, like managing the best approach to connecting and analyzing all the data across the organization. Even if your business already has a data lake approach, security engineers are still spending hours analyzing how different data elements relate and derive meaning from them. 

Implementing a unified detection layer simplifies the process of building detection content, even with diverse skill sets among security analysts. It also provides a standardized schema, enhancing the adaptability of security operations to different data storage scenarios.

When you decouple the activity of threat detection from tools for which it is not inherently designed, you free up those resources to do what they need to do: address and remediate threats. Detection engineers can now spend more time protecting the business than figuring out how to protect the business.

Data Access, Agility, and Cost Effectiveness

Decoupling enables rapid data access and flexibility in a distributed data lake architecture, meeting the demands of modern data management. SOC teams can leverage cost-effective data lakes while maintaining their effectiveness across various architectures, optimizing resources without compromising functionality. 

When detection engineers no longer have to spend time and energy configuring or managing data collection tools for threat detection, they can be more effective in doing what they are meant to do.

The KPIs here are simple: lower MTTD, MTTR, and costs.

Embracing User Expectations and Reducing Vendor Dependency

Splunk, Azure Sentinel, and ElasticSearch are great tools for aggregating data from disparate systems across the organization. While they each have very powerful ways to generate observability and insight, none of them are purpose-built to handle the various requirements for threat detections. So when you invest in using it in that capacity, you are actually doing your organization a disservice by locking your SOC into a solution that’s not a good solution to the threat detection problem. 

A unified threat detection and hunting platform, decoupled from the activity of collecting or aggregating data, ensures that you aren’t locked into a tool that doesn’t ultimately provide you with what you need.

By minimizing reliance on vendor-specific solutions in a distributed data lake architecture, they can expand data access and reduce concerns through alternative data storage options. At the same time, SOC teams can keep pace with user expectations of more SaaS-ified, agile data management. 

How to Start Future-Proofing Security with Decoupling

To achieve decoupling between logging and analytics layers, organizations need to implement a unified detection layer and adopt the right AI tooling. Your unified detection layer should act as a hub for all detection content which connects to and processes detections within each data lake (the spokes where data is collected and processed) regardless of query language.

A truly unified detection layer incorporates multiple detection methodologies such and AI-driven tools that enhance the overall efficiency of the detection engineering process. Here's how these tools can play a crucial role in achieving decoupling:

  1. Faster Search and Detection: AI-driven tools can analyze and process data at high speeds, enabling faster search and detection of potential security threats. By training these tools with threat knowledge and coupling them with high-fidelity detection outputs, they can efficiently sift through vast amounts of data, reducing the time it takes to identify potential security incidents with minimal noise.
  2. Across Various Tools and Cloud Workloads: The unified detection layer integrates with different data sources and tools used for data collection, regardless of whether they are on-premises or in the cloud. It provides a centralized interface to access and analyze data from diverse sources, streamlining the detection process.
  3. Normalization with Unified Query Languages: AI-driven tools often support unified query languages (such as SPL, SQL, KQL, etc.), allowing security analysts to use consistent and standardized queries across multiple data sources. This normalization simplifies the search, detection, hunting, and triaging process, making it more efficient and less error-prone as they utilize normal language to build the queries utilized to process data.

By leveraging a unified detection layer and AI, organizations can break free from the traditional constraints of logging and analytics layers being tightly coupled. This decoupling allows detection engineers to optimize data storage and analysis processes, leading to smarter and faster detection of security threats. Additionally, it promotes interoperability among different data sources and tools, ensuring a more seamless and flexible security infrastructure.

Decoupling your logging and analytics layers through an AI-driven unified detection layer will pave the way for a more agile and cost-effective cybersecurity approach.

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