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Better Visibility, Faster Incident Response: Can Security Data Lakes Transform Your Log Monitoring?

This guest blog was contributed by Dana Torgersen, Senior Director of Security Product Marketing, Sumo Logic

Dana Torgersen, Senior Director of Security Product Marketing, Sumo Logic

Increasing cyberattacks and tightening regulatory requirements are driving the need for better security log processing and visibility. McKinsey reports that companies’ log visibility rose to 30-50% from 2019-2022, with a target of 65-85% by 2025. However, having data scattered across different tools and clouds represents a significant hurdle to achieving that target. In response, organizations are turning to security data lakes — centralized repositories that collect and analyze security data from different sources — to provide better attack vector insights and improved incident detection and response.

Data Security Lakes: Faster Data Access with Greater Insights

Security analysts need quick log file access to develop informed responses to potential cyber threats. However, many organizations generate terabytes of log files per month from devices and applications spread across the enterprise, making manual data analysis impossible.

Centralized security data lakes provide a comprehensive view of an organization’s security posture, enabling analysts to quickly identify potential threats and speed issue resolution. Also, by retaining data in its raw format, security data lakes give analysts a historical view of security events — ideal for forensic analysis and pattern identification.

Data lakes are highly flexible and can store many types of security logs, including:

  • Firewall logs help identify potential threats, including malware.

  • Proxy and web filtering logs can flag potential links to malicious sites.

  • Endpoint security solutions collect data from every network-connected device to help filter out false positives.

  • Network security products often generate standalone logs that provide greater value when centralized.

  • User access authentication data tracks a user’s activity and relationship to system events.

  • Threat intelligence allows analysts to access threat data from other organizations to identify recurring patterns or behaviors.

Enhancing Security Posture

By centralizing and analyzing log files, data security lakes can offer a transformative approach to enterprise security with many advantages, including:

  • Faster incident response. Having real-time visibility across the entire infrastructure and application stack can dramatically reduce response times.

  • Reduced attack surface. Centralized log storage minimizes the attack surface by concentrating log data in a secure location with restricted access.

  • Supports compliance requirements. Some security data lake solutions can automatically generate audit-ready compliance reports from cloud event logs.

  • Versatility. Data security lake solutions work for cloud-based environments and don’t require additional hardware or software. They can quickly scale from mid-sized businesses to large enterprises.

  • Resource efficiency. Collecting, managing and analyzing log data centrally takes less resources than manually handling data from multiple sources.

Implementing Your Security Data Lake Solution

Ensuring the optimal performance of your data lake requires a detailed plan covering all aspects of the project lifecycle, from needs analysis to policy development and operations. Some key considerations are:

  • Defining your data security goals. Determine what data types of logs you need to analyze (e.g., user activity and access logs) and from where — security tools, networks, applications, and endpoints.

  • Choosing a data lake solution. Consider your current performance requirements, types of data sources, and storage capacity. What is your anticipated growth, and can the solution scale?

  • Implementing data ingestion. Create a process for collecting data in your data lake. Your solution should provide high levels of security, compliance, and throughput performance.

  • Enforcing data security. Security controls, including best-in-class encryption techniques, are required to protect data in transit and at rest. You must also prioritize monitoring for regulatory compliance.

  • Searching your security data. Search query language tools can help analysts sift through vast amounts of data generated by security tools. Queries can also uncover patterns and potential security risks.

  • Establishing data governance. Data classification tools can easily identify and manage sensitive data and control authorized access.

  • Analyzing security data. Your security data lake solution should also offer various analysis options, including real-time threat detection, compliance reporting, and dashboards.

  • Monitoring your data. Implement a process to continuously monitor and analyze data to identify potential threats.

Unlocking the Capabilities of Centralized Security Data

The sheer volume and diversity of decentralized security log files create significant visibility challenges for security analysts. However, centralized data security lakes offer game-changing log visibility and search capabilities for improved incident response and remediation times. ###


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