What Is Cyber Security Analytics?

Security analytics is a proactive approach to cybersecurity that correlates and analyzes data to detect anomalies and unusual user and entity behavior that may indicate cyber threats.

As cyber threats grow more sophisticated and prolific, the traditional means using of rules, signatures, and patterns to detect and deter threats is no longer viable. Attacks based on zero-day vulnerabilities or polymorphic malware can flow right past the rules intended to detect and stop them. What’s more, perimeter tools like firewalls and intrusion prevention are ineffective against insider attacks—the most difficult type of attack to detect. Thus, the discipline of security analytics emerged to take event and activity data that has been collected from across an organization’s total computing environment and analyze it for threat detection and security monitoring.
Security analytics combines big data capabilities with threat intelligence to help detect, analyze, and mitigate insider threats, as well as persistent cyber threats and targeted attacks from external bad actors. It provides real-time visibility, automated contextual detection, prioritized investigation, and risk-driven response.

Types of Cyber Security Analytics

The tools for managing and analyzing data have become much more powerful and readily available in the cloud, enabling companies to use their data to understand the past, make good decisions in the present, and predict what may happen in the future.

There are two types of cyber security analytics. The first attempts to detect and identify what has happened and is currently happening in the computing environment. This reactive approach is simply known as data analytics in cybersecurity. The other form attempts to use historical information and patterns to predict what is likely to happen next. We call this proactive approach predictive analytics.

Data Analytics in Cybersecurity

Everything that happens within a computing environment, whether good or bad, leaves a sign of the activity—a breadcrumb, if you will. This crumb might be an entry in a log file, indication of a file configuration change, a modified audit trail, or the like. These crumbs all become input for data analytics in cybersecurity, which we define as the pursuit of detecting potential cyber risk hidden within raw network, cloud, and user data from across an organization’s environment.

The process involves collecting the raw datasets from a variety of sources; preparing the data for analysis; analyzing the data using various techniques and tools; and interpreting the results of the analysis. Specialized computer systems (engines) transform, organize, and model the data to identify patterns and anomalies and to draw conclusions. The goal is to detect threats before they can cause significant harm as well as to create useful intelligence that can then be used to improve security and data privacy.

Predictive Analytics in Cybersecurity

What if you could fight your adversary by knowing in advance how they are going to behave? Predictive analytics is a proactive approach to cybersecurity that leverages historical data and statistical algorithms to gauge future outcomes, monitor network activity, and report real-time data. Predictive analytics can help identify and prevent cyberattacks before they become apparent or damaging, without needing to know the exact signature of the attack. This approach is a potential game-changer that shifts an organization’s security stance from reactive to proactive. The need for predictive analytics is more critical than it has ever been as attacks become more sophisticated and the people behind them become more determined.

Cyber Security Analytics Technology and Automation

The ability to detect and respond to cyber threats now exceeds human capacity and can only effectively be addressed through technology and automation. More and more companies are investing in big data capabilities, machine learning (ML) and artificial intelligence (AI) algorithms, and automated response and mitigation solutions.  These technologies, among others, have brought a great deal of innovation to a new generation of cybersecurity platforms and tools.

Big Data Security Analytics

The term “big data” refers to large, diverse, and complex sets of information that are created and collected at high speeds from various sources. The data can be structured or unstructured. Big data can be analyzed for insights that improve decisions and enable advanced analytics applications such as machine learning and predictive modeling.

Big data security analytics is the process of collecting, normalizing, correlating, and analyzing large amounts of data from across the environment specifically for the purpose of looking for insights, patterns, and anomalies that are indicative of potential risk or a genuine threat. This information is quickly and efficiently made available to security analysts who can investigate the incident, or, in cases where a response can be automated, a process kicks off to mitigate the threat.

Cloud Security Analytics

Many organizations today take a “cloud first” approach to computing, and this is forcing a new reconning with security. With few exceptions, legacy security tools do not work well in the cloud, yet it’s critically important to detect threats in cloud environments and across multi clouds. A new generation of security tools is necessary to collect and analyze the cloud metrics that can reveal the presence of a threat. These metrics come from sources such as cloud generated applications, event logs like CloudTrail, asset inventories, device configurations, vulnerability databases, access logs and permissions databases, and more. Complicating matters is the fact that so many cloud assets are ephemeral, spinning up and down to live only a short time, as workloads dictate.

Cloud security analytics is the most effective means to detect threats in this complex and ever-shifting environment. Once the appropriate metrics have been collected, the analytical process is still to normalize the data, correlate it, analyze it using machine learning algorithms, interpret the results, and alert on suspicious events.

How Machine Learning is Applied to Cyber Security Analytics

The discipline of analyzing data inherent to a system promotes data-driven decision-making. This is especially critical for an organization looking for risks or threats within the computing environment. Machine learning automates the entire data analysis workflow to provide deeper, faster, and more comprehensive insights.

Machine learning is a subset of AI that leverages algorithms to analyze vast amounts of data. These algorithms operate without human bias or time constraints, computing every data combination to understand the data holistically. Applying these techniques to cybersecurity, algorithms can quickly discover insights that looks suspicious or risky. For example, a user logs in from an unusual geographic location, a device is communicating with an unknown URL, a worker tries to access a file they are not authorized to access, a person attempts to turn off logging, a new device connects to the network, and so on. A large organization can have millions of events happening every day, making it impossible for humans to monitor everything, but a machine learning system can ingest and process that amount of data without a problem.

How Cyber Security Analytics Can Help with Threat Detection and Response

Cyber security analytics sorts through millions or perhaps billions of data points to discover what may be risky activity or genuine threats. It does so by monitoring the entire environment for abnormal traffic patterns, watching user behavior and access requests for anomalous activity, detecting data exfiltration, being attuned to unusual communications, identifying compromised accounts and abuse of credentials, looking for the presence of known indicators of compromise, and the like.

Once these types of threats are identified, the system gathers additional information to provide context around the suspicious activity and automates the prioritization of alerts. Some security analytics platforms not only provide a description of the threat, but also include the method of the attack and a recommended response. This helps the security team to quickly assess threats with a degree of confidence and respond as appropriate.

Benefits of Cyber Security Analytics Tools

There are numerous benefits to using cyber security analytics tools. For instance, use of such a tool helps to reduce security analyst fatigue and burnout. It has become humanly impossible to manually sift through the amount of security data that organizations have today. Analytics based on machine learning is the only reasonable approach to detecting and responding to cyber threats.

Another benefit of these tools is that they address the dearth of skilled security analysts. Organizations simply can’t hire enough people with security expertise to do threat detection on their own. A cyber security analytics platform does the hard work of sifting through data and surfacing the prioritized threats that should be investigated by the security analysts on hand.

These platforms have the ability to identify a suspicious event and then collect contextual information that provides specific details to help analysts understand not only what has happened, but how it has happened (i.e., the path of the attack) and what assets were affected. This forensic information aids in mitigation in the present and prevention for the future. What’s more, the platforms often recommend a remediation plan to guide the security analyst to a resolution for the alert.

Cyber security analytics tools can help an organization comply with government and industry regulations by offering a unified view of data events, particularly around regulated data such as health information records (HIPAA) and payment card data (PCI DSS).

The Difference Between Security Analytics and SIEM

Security information and event management (SIEM) solutions and security analytics platforms feature similar capabilities and have similar priorities and security goals. Many comparisons consider “legacy SIEM” solutions that fault the solution for being limited to on-premise installations, having inflexible architectures, and providing limited threat hunting capabilities. However, those comparisons are outdated. Today’s “next-gen SIEM” has overcome those limitations and closes the gap between security analytics and SIEM. In fact, virtually all SIEM tools today have incorporated a security analytics function to aid in surfacing unknown threats. For most practical purposes, there is little difference between modern SIEMs and security analytics platforms.

Gurucul’s Industry-leading Cyber Security Analytics Platform

Gurucul has created a purpose-built, cloud-native Security Analytics and Operations Platform that goes beyond current extended detection and response (XDR), SIEM and other security operations center (SOC) solutions to empower security analysts. With a consolidated set of capabilities, the platform helps to automate tasks beyond just collection and correlation and provides a full set of capabilities for threat detection, investigation, and response (TDIR).

The Gurucul Platform is powered by Gurucul Risk Analytics (GRA), our set of the most advanced and comprehensive analytics and trained machine learning and artificial intelligence models. While other solutions use rule-based ML/AI, we are focused on ingesting as much data as possible, applying a wide area of analytics, and using true ML/AI to adapt and learn to newer threats.

Gurucul provides the most comprehensive set of security analytics, out-of-the-box threat content, a trained (not rule-based) ML engine, the highest number of ML models, and a full enterprise risk engine. This empowers security teams to confidently take on the evolving threat landscape with a unified and analytics-driven approach. Our cloud-native platform and solutions are built to lower deployment, management, and operational costs while preventing damage to business due to cyber attacks.

The major functions of the platform include:

• Observability– Get full context and deeper insights without escalating costs. Ingest, correlate, normalize, and link telemetry across the entire environment for full security observability based on users or devices.
• Detection– Gurucul provides real-time automated detection of current and new attacks. With out-of-the-box content, comprehensive advanced analytics, and a trained machine learning engine and models, you can immediately detect threats and identify attack campaigns.
• Investigation– Consolidate efforts and increase operational efficiency with Gurucul’s solution. Reduce investigation time and prioritize threat hunting with improved context, linked event visualization, and machine learning-powered models.
• Response– Leverage risk to focus on immediate, high impact response. Mitigate the full attack campaign before it impacts the business with a risk-driven set of dynamic and targeted playbooks.

Conclusion

Cyber security analytics is a must-have component of a modern-day threat detection and response system. It is the most efficient and effective way to detect and deter threats—especially those unknown threats based on zero-day vulnerabilities or polymorphic malware. Machine learning and artificial intelligence models can sift through millions of metrics to quickly discover anomalous activity or behavior that may be indicative of an active or predicted attack. Risk-driven alerts and contextual background information accelerate investigations and responses.

Learn about Gurucul’s industry-leading cyber security analytics platform which empowers security teams to confidently holistically address the ever-evolving threat landscape.