Imagine security data and analytics like a carnival’s hall of mirrors. From convex mirrors that show you a shorter, squatter version of something to the concave mirrors that show a highly magnified image, you see the same object in multiple ways. Every view gives you a different insight and provides a unique vantage point.
Online Analytical Processing (OLAP) systems are different mirrors that allow security teams to create focused analytics models for different insights about your security posture. Security teams can leverage these data analysis capabilities to derive insights from complex security datasets to improve their threat detection and incident response functions.
The intersection of OLAP and security operations offers improved security outcomes, as long as organizations know how to overcome the challenges associated with these systems.
What Is Online Analytical Processing (OLAP)
Online Analytical Processing (OLAP) extracts and analyzes data generated from multiple, disparate sources, optimizing them for read-heavy activities, like data analytics and trend analyses. Engineered for rapidly handling complex queries, OLAP systems enable users to manipulate data without affecting online transaction processing systems. Their user-friendly interfaces and ability to integrate with existing databases allow people without advanced technical knowledge to create data visualizations that communicate data-driven insights.
Some key benefits of OLAP systems include:
- Supporting complex queries for performing aggregate calculations across large data sets.
- Enabling trend analysis for planning and decision making.
- Prioritizing analytical operations that support strategic business decisions.
When applying OLAP to security operations, organizations can optimize the value of their security analytics across:
- Threat detection: Complex calculations and trend analysis correlate data across diverse tools for higher fidelity alerts.
- Security incident analysis: Rapidly handling complex queries improves incident investigation and response times.
- Security posture improvement: Correlating data across diverse formats identifies security gaps and improves security controls across complex IT environments.
- Business continuity: Fewer false positives and improved issue identification reduce the likelihood of security incidents and their potential impact.
Where OLAP Intersects with Security Operations
Security operations (SecOps) focuses on improving an organization’s cybersecurity by taking a proactive approach to threat and risk management. By centralizing all security activities and data, security and IT teams can communicate and coordinate more effectively and minimize the potential business disruption that an incident can cause.
By embedding OLAP’s data analysis strengths into SecOps, organizations can adopt a more cohesive approach to defending against cyber threats. Since Online Analytical Processing enables security teams to analyze large datasets across diverse sources, security teams can take a more strategic approach to understanding normal environment behavior and detecting anomalies that may indicate a potential security incident.
Reduced False Positives
As organizations add to their security technology stacks, security analysts struggle with false positives that create alert fatigues. As security and IT technologies generate high volumes of data, security teams become overwhelmed with the number of alerts, leading to an inability to accurately risk-rate security alerts.
OLAP systems enable fast and flexible data analysis. Security analysts can ingest data from various sources then apply analytics models to them to analyze high volumes of security telemetry. OLAP systems support a security team’s need to segment data more efficiently, enabling greater precision for detections and alerts.
Enhanced Detection of Emerging Threats
As attackers increasingly deploy credential-based attacks, like credential stuffing attacks, security teams need visibility into potential threats with the identification of unusual behavior patterns across users, devices, and networks.
With OLAP systems, security teams can correlate threat intelligence with log data more effectively. Threat intelligence provides insight into real-world attack methods so that security teams can create more effective detections. For example, since OLAP systems can integrate structured and semi-structured data, security teams can map Sigma rules to the MITRE ATT&CK Framework’s tactics, techniques, and procedures (TTPs). These correlations enable the security analysts to detect threats faster and then investigate more efficiently.
Improved Incident Response
By providing sufficient access to historical data, security teams can improve incident response activities when analyzing past incidents. OLAP systems optimize data for read-heavy activities that security teams need when dissecting and reporting on the factors that contributed to a security incident.
By segmenting log data into multidimensional slices, OLAP makes performing detailed trend analyses easiest for improved insight into security incidents over time. Further, with all data aggregated in a single location, the security team can more effectively and efficiently deliver security incident reports to senior leadership and boards of directors.
Why Do Security Teams Struggle to Use OLAP Systems?
Despite the various benefits that OLAP systems provide, many security teams struggle to successfully implement them.
Slow Data Refresh Rates
To reduce costs, many organizations use a security data lake, like Amazon Security Lake. While these data stores can ingest large data volumes, conventional OLAP that processes historical data or trains machine learning models typically use long-running queries that take minutes or hours. While these timelines work for business-level reporting, they can have significant consequences when applied to security use cases that require real-time insights.
Diverse Data Formats
Security data is notoriously diverse. White traditional data may have limited formatting and schema options, security telemetry can be varied, including proprietary formats like Windows Event Logs. Further, the technologies may use or not include different fields. For security use cases, normalizing the data can lead to additional latency issues or undermine data’s quality by introducing errors like missing values or incorrect formats.
Interoperability and Scalability Concerns
Designing and implementing OLAP systems is complex, often requiring specialized skills and experience. If the security team seeks to integrate the OLAP system into its current security data infrastructure, it can struggle with compatibility issues and require customized data models for these use cases. Additionally, these interoperability challenges increase as the team adds more security tools that generate even more data, often further exacerbating the latency issues.
Graylog Security: Supporting Fast Queries To Improve OLAP Systems
Graylog Security allows you to directly enter a query across your connected security data estate. With the ability to query large amounts of data faster, you get the centralized data access and security analytics that you need to improve threat detection and incident response (TDIR).
By leveraging our cloud-native capabilities and out-of-the-box content, you gain immediate value from your logs. Our anomaly detection ML improves over time without manual tuning, adapting rapidly to new data sets, organizational priorities, and custom use cases so that you can automate key user and entity access monitoring.
With our intuitive user interface, you can rapidly investigate alerts. Our lightning-fast search capabilities enable you to search terabytes of data in milliseconds, reducing dwell times and shrinking investigations by hours, days, and weeks.
To learn how Graylog Security can help you implement robust threat detection and response, contact us today.
