What is Cybersecurity Analytics? Definition & Use Cases

What Is Cybersecurity Analytics?

Cybersecurity analytics is the use of data collection, aggregation, (note: remove link) attribution, and data analysis to extract the information necessary for a proactive approach to cybersecurity. 

Traditional security information and event management systems (SEIMs) rely on (note: remove link) point-in-time testing, which leaves room for error as things are constantly changing within a network security environment. 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 can also help prioritize responses by identifying the most critical threats, ensuring security teams focus their efforts where they matter most.

Why Is Cybersecurity Analytics Important?

Cybersecurity analytics is important because it enables IT security teams to take cybersecurity monitoring into their own hands. Security analytics can help provide necessary visibility across your organization’s entire IT ecosystem, allowing for earlier threat detection and automation of your more manual security tasks.

As the business world undergoes a massive digital transformation, IT networks have become more complex and sophisticated. Organizations are quickly learning that a reactive approach is not enough to manage cyber risk effectively in today’s increasingly digital world.

Therefore, security teams now rely on security analytics tools to provide insights derived from data and detect and inspect threat alerts as they occur.

Behavioral Analytics in Cybersecurity

Behavioral analytics focuses on monitoring and analyzing the behavior of users, devices, and network activity to detect unusual patterns that might indicate a potential security threat. Unlike traditional signature-based detection methods, behavioral analytics uses machine learning and statistical algorithms to identify deviations from normal behavior. This helps security teams catch zero-day attacks, insider threats, and advanced persistent threats (APTs) that might go unnoticed.

For example, if a user account suddenly attempts to access large amounts of sensitive data outside of regular working hours or from an unusual location, a behavioral analytics system can flag this as a potential breach. This proactive approach reduces the time it takes to detect and respond to security incidents, significantly reducing potential damage.

Effective behavioral analysis is essential for detecting subtle signs of insider threats or compromised credentials that might otherwise be missed.

Behavioral analytics is especially powerful when integrated into a broader cybersecurity analytics strategy, providing deeper visibility into network security, user behavior, and device interactions.

The Benefits of Security Analytics Tools

Security analytics tools provide external (note: replace link) threat intelligence and the additional context needed to identify correlations between alerts and events, or changes. By combining massive amounts of data into one place, security analytics tools can operate in near real-time, allowing for quick detection and efficient security orchestration.

1. Prioritized Alerts

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.

2. Automated Threat Intelligence

Due to the volume of available data, (note: remove link) 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.

Machine learning and behavioral analysis can significantly enhance the accuracy of automated threat intelligence, reducing the time needed to detect and respond to incidents.

3. Proactive Incident Detection

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 (note: replace link) respond to threats proactively.

Advanced network traffic analysis is crucial for identifying abnormal data flows that might indicate malicious activity or unauthorized data exfiltration.

4. Improved Forensic Incident Investigation

Preventing similar incidents from happening in the future often relies on thorough digital forensics. 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.

Use Cases for Cybersecurity Analytics

Cybersecurity analytics can be applied in many ways and by many organizations, including technology companies, (note: remove link) insurers, ratings agencies, compliance auditors, and of course, security teams.

Here are some of the most common use cases for security analytics:

• Analyzing network traffic to identify patterns that indicate a potential attack
• Detecting (note: remove link) insider threats or malicious activity
• Incident response and digital forensics
• Managing third and fourth-party vendor risk
• Detecting (note: remove link) data exfiltration and which accounts may have been compromised
• Governance, risk, and (note: replace link) regulatory compliance
• Identifying threat indicators with threat hunting

Using analytics to detect early warning signs of insider threats or data exfiltration can significantly improve response times and reduce damage.

Cyber Detection and Proactive Threat Response

Proactive threat detection and response are essential components of a robust cybersecurity strategy. This approach focuses on identifying and neutralizing security threats before they cause significant damage. By leveraging real-time data and machine learning, security analytics tools can recognize early warning signs of potential attacks, such as unusual login attempts or sudden spikes in outbound network traffic.

Understanding an attacker’s tactics and techniques is critical for designing effective security defenses and improving overall threat response.

Proactive threat detection reduces the risk of data breaches, minimizes financial losses, and helps maintain customer trust by addressing security issues before they escalate into full-blown crises.

Using SIEM for Data Analytics and Security Monitoring

Security Information and Event Management (SIEM) systems are critical in modern cybersecurity analytics. They aggregate log data from multiple sources, including firewalls, servers, and endpoint devices, providing a centralized view of an organization’s security posture.

With built-in data analytics capabilities, SIEM tools can detect patterns and anomalies that might indicate a security incident, helping security teams respond faster to emerging threats. By integrating SIEM with advanced behavioral analytics, organizations can enhance their overall threat detection capabilities and reduce the time it takes to detect and respond to incidents.

Identifying Security Threats Through Data Analytics

Data analytics is at the core of modern cybersecurity. Organizations can identify trends, detect anomalies, and uncover hidden network threats by collecting and analyzing vast amounts of security data.

Data analytics can reveal subtle indicators of compromise that traditional security tools might miss, such as gradual data exfiltration or slow-moving attacks designed to evade detection. This approach provides a deeper understanding of the security landscape, enabling more effective threat hunting and risk mitigation.

How SecurityScorecard Can Help

Cybersecurity analytics enables security teams to take massive amounts of raw data and transform it into actionable insights that can drive future strategies and operations. (note: replace link) 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, the number of findings, and more. This empowers organizations to proactively address cybersecurity and risk management in real-time, all in one place.