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A Definitive Guide to Security Analytics
Security analytics has become an increasingly popular field as more and more organizations take a different tact to cybersecurity. Historically, IT teams focused on prevention and protection, but today’s priority is detection. Hackers tend to use a wide range of ever-changing tools to exploit vulnerabilities. It can feel like whack-a-mole to constantly try to defend against evolving threats.
Security analytics track common patterns that don’t vary regardless of what form an incursion takes. By providing a structured view of real-time and historical data, security analytics tools can help security teams spot anomalies and quickly address them before the attack is successful. Here’s how security analytics works and how to take advantage of security analytics at your company.
What is security analytics?
Security analytics is the practice of using software, algorithms, and apps to analyze and detect security threats to IT systems. Security analytics can use historical and real-time data to find vulnerabilities or breaches in your cybersecurity.
Security analytics is often compared to security information and event management (SIEM) software. SIEM solutions collect and aggregate log data from everything from host systems to security devices, identify and categorize incidents, and analyze them to find potential security threats. SIEM is considered a complex, CPU-intensive process, and while it’s certainly useful, security analytics solutions may offer a more flexible and nimble approach.
Security analytics solutions differ in how they work, but each uses data to try to identify threats. Here are some common components of most security analytics tools.
Behavioral analytics attempt to identify and prevent instances of insider threat. These security analytics look at patterns of user and device behavior to find anomalies. Someone logging in from a new location, for instance, or accessing a system at an odd hour, could trigger an alert from behavioral analytics tools.
Network analysis and visibility (NAV)
Some analytics solutions analyze traffic from users and applications, using network discovery, flow data analysis, network metadata analysis, and other analyses to spot breaches. These types of tools also use behavioral analytics, coupled with a combination of machine learning, rule-based detection to spot anomalies or suspicious activities on the network.
Security orchestration, automation, and response (SOAR)
SOAR functionality refers to a hub that handles communication between data gathering, analysis, and threat response. Both security analytics and SIEM applications have a SOAR component. SOAR is necessary for helping teams prioritize their response to reviewing and reacting to security threats.
Benefits of security analytics
Security analytics looks at raw event data to detect data exfiltration threats, insider threat, suspicious behavior and potential attacks. The biggest benefit of security analytics is that it can process a massive volume and diversity of information at one time. This allows security teams to work smarter, not harder, to maintain information security.
Many security analytics tools also help organizations be compliant with industry regulations, Both HIPAA and PCI-DSS require organizations to safeguard and monitor data and log data collection for audition purposes. Security analytics tools typically provide data logs to help security teams link alerts and events. Reporting — as well as monitoring — is much easier with security analytics.
The true value of security analytics is these tools’ ability to capture and process a huge amount of information to provide one clean data set. With this information, security teams can better prioritize resources, address threats, and be compliant with applicable regulations.
How to get started with security analytics
Since security analytics is a broad category of tools, platforms, and applications, there are myriad ways to implement security analytics at your business. Here are some of the common manifestations of security analytics solutions.
Cloud data loss prevention
Nightfall is a modern cloud data loss prevention tool that utilizes AI and machine learning to scan IaaS and SaaS environments using over 150+ detectors. This security analytics tool notifies administrators when users have shared data in risky ways within your cloud applications. By scanning both structured and unstructured data, Nightfall helps IT teams eliminate the time spent tagging data manually, reviewing false positives, and grappling with alert fatigue.
Threat hunting tools are more proactive than the standard detection security analytics. Threat hunting tools and platforms enable teams to proactively search for potential data breaches, using automation to help identify hidden malware and spyware.
Security analytics tools can alert IT teams when data is moving in or out of your network, pinpointing potential instances of theft. These tools can help to prevent unauthorized data access by supplementing network data loss prevention solutions. Advanced tools can help discover data loss in encrypted communications, too.
There are dozens of security analytics tools that can help keep your information secure. To learn more about Nightfall, set up a demo using the calendar below.
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Nightfall is the industry’s first cloud-native DLP platform that discovers, classifies, and protects data via machine learning. Nightfall is designed to work with popular SaaS applications like Slack, Google Drive, GitHub, Confluence, Jira, and many more via our Developer Platform. You can schedule a demo with us below to see the Nightfall platform in action.
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