Supercharge Cybersecurity with Superior Data Analysis

December 28, 2023
1 min read

Superior Data Analysis Needed for the Cybersecurity Industry

– Data-driven decision-making is crucial for a successful cybersecurity architecture
– All data can be valuable for cybersecurity, including traditional and non-traditional sources
– Increasing data visibility and management is essential for effective cybersecurity
– Collaboration between data scientists and cybersecurity analysts is necessary for efficient data analysis
– Security teams must redefine how they source, manage, and analyze data to strengthen organizational resilience

In the cybersecurity industry, having the right tools is essential, but it’s equally important to have the skills to carefully analyze and classify data. Data-driven decision-making is crucial for a successful cybersecurity architecture. Traditional sources of cybersecurity data include computer telemetry, network and endpoint logs, and cloud logs. However, any data that leads to patterns exposing potential threats can be considered cybersecurity data. It’s important to recognize that seemingly disparate data can be used to build profiles of end-users and prompt investigations in case of data variance.

In addition to traditional sources, external data sources such as data from crypto wallets, network flow from ISPs, and data from data brokers can be useful for cyber defenders. Open Source Intelligence (OSINT) research can also provide valuable data to help analysts identify malicious activity. Therefore, it’s important to understand the potential of different data sources and increase data visibility.

Managing and coordinating data efficiently is a challenge for cybersecurity teams. Manual processes are too cumbersome and cannot match the rate at which data is received. Artificial intelligence (AI) and machine learning (ML) tools, as well as data pipelining and cleaning tools, are essential for managing data effectively. Data lakes that provide storage and compute power for analyzing data in near real-time are also crucial.

While tools are important, cybersecurity teams must also have the right skills to analyze and classify data. There needs to be more recognition of the crossover between data scientists and cybersecurity analysts. Both roles are crucial for a successful cybersecurity program, with data scientists focusing on making data usable and available to analysts, and cybersecurity analysts looking for patterns and anomalies. Collaboration between these roles can enhance cybersecurity programs by providing direction on data usage and identifying inconsistencies.

To strengthen organizational resilience, security teams must redefine how they source, view, manage, and analyze data. This will enable them to fully identify the potential of data for cybersecurity decision-making and perform their jobs more efficiently. Overall, superior data analysis is needed in the cybersecurity industry to effectively protect organizations from threats.

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