Posted on Apr 3, 2020
The business sphere is undergoing a massive digital transformation that has seen IT networks become more complex and sophisticated. Organizations are quickly learning that in today’s increasingly digital world, a reactive approach is not enough to effectively manage cyber risk.
As a result, security teams now rely on security analytics tools to provide insights derived from data, to detect and inspect threat alerts as they’re happening.
Cybersecurity analytics involves the use of data aggregation, attribution, and analysis to extract the information necessary for a proactive approach to cybersecurity. Traditional security information and event management systems (SEIMs) rely on point-in-time testing, which leaves room for error as things are constantly changing within a network. Thus, assessing an organization’s cybersecurity posture at a particular moment will likely not be reflective of the true day-to-day security efforts. Security analytics leverages machine learning capabilities to help continuously monitor a network and identify changes in use patterns or network traffic so that threats can be addressed immediately.
Security analytics tools provide external threat intelligence and the additional context needed to identify correlations between alerts and events or changes. With the ability to combine massive amounts of data into one place, security analytics tools can operate in near real-time, allowing for quick detection.
Security analytics tools can provide specific information on potential vulnerabilities and rank alerts, prioritized by severity so that security teams can easily determine what should be addressed first.
Due to the volume of available data, automated threat intelligence is extremely valuable as it cuts down on time spent doing manual security tasks, improves accuracy, and can help you more easily identify vulnerabilities in your network.
Security analytics tools combine historical analysis and new data to identify anomalies or patterns in user behavior and network traffic. Abnormalities in these patterns can be indicative of a potential attack or negligent user behavior, allowing security teams to respond to threats proactively.
Forensic investigations are crucial for preventing similar incidents from happening in the future. Security analytics tools can help identify threat origin, the specific data or accounts that were compromised, the severity of the attack, and more. This information can then be used to make data-driven decisions about future security efforts.
Cybersecurity analytics can be applied in many ways and by many organizations, including technology companies, insurers, ratings agencies, compliance auditors, and of course, security teams.
Here are some of the most common use cases for security analytics:
Cybersecurity analytics enable security teams to take massive amounts of raw data and transform them into actionable insights that can drive future strategies and operations. SecurityScorecard’s API connectors make it possible to apply relevant context to alerts, allowing security teams to act quickly on the most important threats. Through automated threat intelligence gathered from hundreds of thousands of entities and accurate data attribution, the platform also provides organizations with security ratings, specific information on potential exploits and severity, number of findings, and more. This empowers organizations to proactively address cybersecurity and risk management in real-time, all in one place.
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