Cybersecurity analytics is an emerging field featuring analysis and machine learning techniques to identify and respond to cyber threats.

Cyber security analytics is a process that uses AI, ML, and data analysis to detect and respond to complex cyber threats. It involves collecting and analyzing large amounts of data, identifying patterns and anomalies, and predicting potential cyber-attacks. It enables professionals to better understand their systems' vulnerabilities and respond to threats in a more effective and timely manner, reducing the risk of data breaches and other cyber attacks.

According to statistics and a report, cybercrime is expected to cause $10.5 trillion in damage globally by 2025. The USA is leading when it comes to sharing of organizations worldwide that are at risk of a material cyber-attack within a year. These alarming statistics highlight the need for robust cybersecurity measures and the importance of cybersecurity analytics in protecting against cyber threats.

We have put together this comprehensive guide on cybersecurity analytics, how it works, the tools and techniques used in it, its benefits, and much more. Keep on reading to learn more.

Data Analytics in Cybersecurity

In cybersecurity, people use tools and techniques to collect information from different places such as computer logs, virus scanners, and other sources. They look at how people and machines behave, what programs are being used, and whether there are any potential threats. They also use outside sources of information to help them understand what's going on. All this helps them to keep systems and data safe from harm.

The tools and techniques in data analytics in cybersecurity are used to gather data from various sources. For instance, endpoint and user behavior data, operating system event logs, business applications, virus scanners, routers, contextual analysis, and external threat intelligence sources.

The collected information using the tools and techniques is a  large amount of data, and combining and comparing such data allows organizations to create a single data set. It enables cybersecurity experts to perform appropriate algorithms and fast searches to detect early signs of an attack.

How do you combine and compare such enormous amounts of data? With the help of machine learning methods, security experts can easily conduct threat and data analysis in real time. 

In addition to detecting early signs of an attack, security analytics can also help organizations to prioritize and respond to security incidents promptly. With the increasing sophistication of cyber threats, security analytics platforms offer businesses the ability to analyze vast amounts of data quickly and accurately. 

They can also provide valuable insights into the effectiveness of current security measures and identify areas where improvements can be made. According to the Center for Cybersecurity Analytics and Automation (CCAA) , by adopting security analytics solutions, businesses can stay one step ahead of cybercriminals and protect themselves against ever-evolving cyber threats.

History of Cybersecurity Analytics

Cybersecurity is a continuous back-and-forth battle between attackers (cyber criminals) and defenders (security experts). Initially, defenders used basic security measures such as file hashes and IP addresses to detect malicious activity, but as attackers became more knowledgeable, they adapted their methods to evade detection. 

To counter this, security experts implemented more advanced techniques such as deep packet inspection and identifying binary sequences within files. As cybersecurity evolved, it started using heuristics to provide a better understanding of the detected activity. 

Today, cyber analytics rely on anomaly detection (analyzing tons of data) and statistical techniques to identify any deviations from past behavior. The integration of machine learning and deep learning has further strengthened detection and mitigation capabilities, making it difficult for attackers to breach security systems. 

Innovative solutions such as Advanced Network Detection and Response utilize the power of cyber analytics to detect and respond to cyber threats based on threat behavior patterns.

Before we go ahead and learn all about cybersecurity analytics, there are two major things that you need to understand first. They are “Big Data” and “Machine Learning (ML)”. Let’s understand them first.

Big Data Analytics in Cybersecurity

Throughout this guide, you will see the word “Big Data”. Let’s understand what it is. How does it work? What issues does it resolve when it comes to cybersecurity analytics?

Generally, in IT, big data is the next big thing. It has the potential to provide solutions to complex problems. Today, data is the biggest asset. Everything on the internet is just a big chunk of data. Big data is about gathering information from all of this unstructured big chunk of data and organizing it so that it’s readable and makes sense.

In cybersecurity, big data is used to process and analyze massive volumes of data to extract valuable insights and patterns that can help organizations make better decisions.

• Data collection: Big Data Analytics relies on the collection of large amounts of data from multiple sources, such as network logs, system logs, user behavior data, and threat intelligence feeds. This data is then processed and analyzed to identify patterns and anomalies. 
   
• Data processing: Once the data is collected, it is processed to remove irrelevant or redundant data and to structure it into a format that can be analyzed. This may involve cleaning, filtering, and transforming the data. 
   
• Data analysis: The processed data is then analyzed using various statistical and machine learning algorithms to identify patterns and anomalies that may indicate security threats. This analysis may involve clustering, classification, and regression techniques. 
   
• Threat detection: The insights generated from data analysis are used to detect security threats in real-time or near-real-time. This may involve identifying anomalous behavior or detecting known attack signatures. 
   
• Response and mitigation: Once a threat is detected, Big Data Analytics can also help in responding and mitigating the threat. This may involve isolating the affected system or network, blocking traffic, or patching vulnerabilities.

So, this is how big data helps security experts and organizations in staying firm against cyber threats. 

Machine Learning in Cybersecurity Analytics

Machine Learning is yet another big thing in the IT world. We mentioned big data is the field of organizing tons of unstructured data, so how do we do that? The answer is different techniques and machine learning is definitely one of them as it helps identify patterns in vast amounts of data in real time. Here’s how ML helps in cybersecurity analytics:

• Predictive Analysis: Machine learning algorithms can be used to analyze user behavior to identify anomalies that may indicate malicious activity. This can help to detect insider threats and other types of attacks that may be more difficult to detect using traditional security measures.
   
• Anomaly Detection: When the data from different sources is collected and organized, security experts use different machine learning algorithms to thoroughly examine that to recognize any anomalous behavior. The continuous analysis of vast amounts of data helps in differentiating between normal and malicious behavior. That’s how ML is used for the early detection of potential threats.
   
• Threat and Malware Detection: If there is any anomaly in the data, it can lead to threat detection. Any suspicious behavior and known threats can be detected and blocked before they can cause any damage.

From this point forward, everything mentioned in this guide is under the direct or indirect use of big data and machine learning.

Advanced Network Detection and Response

Network Detection and Response (NDR) is a cybersecurity technology that provides comprehensive visibility and analysis of network traffic to detect and respond to threats. It involves continuous monitoring of all network activity and analyzing it in real-time to identify potential threats. 

NDR platforms use advanced analytics and machine learning algorithms to detect suspicious patterns, behaviors, and anomalies in network traffic. Benefits of NDR include quick detection and response to threats, improved network security, reduced risk of data breaches, and improved incident response times. 

NDR also provides comprehensive threat intelligence and visibility into network activity, helping security teams to identify and prioritize security risks. It also provides automated response capabilities to enable faster and more efficient incident response. 

Some common features of NDR platforms include advanced threat detection capabilities, automated incident response, network traffic analysis, and visibility and reporting. NDR platforms can also integrate with other security technologies such as Endpoint Detection and Response (EDR), Security Information and Event Management (SIEM), and Threat Intelligence Platforms (TIP). 

Predictive Analytics in Cybersecurity

AI and Machine Learning techniques can be used to predict potential cybersecurity threats. In cybersecurity, predictive analytics involves using advanced statistical algorithms to analyze large amounts of data from different sources such as network traffic, web proxies, and log files. 

Such advanced algorithms and machine learning methods are used to find hidden patterns and relationships in the data that can lead to a cyber threat.

Applications of predictive analytics in cybersecurity can include the following:

• Threat detection: Predictive analytics can help identify patterns and anomalies in network data that may indicate the presence of a cyber threat. By detecting threats early, security teams can take action to prevent attacks and protect their organization's data and assets. 
   
• Risk assessment: Predictive analytics can be used to identify areas of an organization's network that are most vulnerable to cyber-attacks. By assessing risk, security teams can prioritize their efforts to protect critical systems and data. 
   
• Incident response: Predictive analytics can help security teams identify the likely source of an attack and develop an effective incident response plan. By predicting the behavior of attackers, security teams can quickly and effectively respond to incidents and minimize the impact of an attack.

Additionally, advanced security solutions can use predictive analytics to present new discoveries and insights through visualizations such as heat maps and chronological timelines, which can help analysts understand complex connections among various types of events.

One of the key benefits of applying machine learning methods to big data security centers (SOCs) is the ability to automatically identify new attacks and create visualizations for more effective investigation.

Analytics Technology and Automation

As the term suggests, automation and cybersecurity analytics technology refer to the use of automated approaches. It allows security experts to identify and prioritize security incidents continuously aiming to maintain a real-time observation of network environments.

Tools and Technologies Used in Automation

One of the advanced automation techniques in cybersecurity is Breach and Attack Simulation (BAS). This particular technique is used to detect any gaps, vulnerabilities, and weaknesses in the system to reduce the risks of potential cyber threats.

Automation involves utilizing advanced technologies like big data platforms and machine learning algorithms to help them in foreseeing any cyber threat in the future. Automation keeps on running in the backend and keeps analyzing big amounts of data in real-time. This way, organizations will have the upper hand in responding to and mitigating potential security threats.

Big data platforms help in collecting, processing, and storing big chunks of data in a single location. It makes it easier to monitor any suspicious activity continuously without spending too much time on manual analysis. 

Big data platforms are combined with machine learning algorithms to analyze multi-dimensional datasets quickly. It makes sure that security experts will catch hidden relationships among diverse types of events like endpoint/user behaviors.

Faster detection, response, and recovery times are crucial in the face of cyber threats, as organizations that implement AI and automation can shorten the breach lifecycle by 74 days and save an average of $3 million more than those without. This is according to recent statistics, which highlight the value of cybersecurity analytics technology and automation in safeguarding business environments.

Proactive Cybersecurity and Real-time Threat Detection

Proactive cybersecurity involves using strategies and technologies to identify and predict cyber attacks. Such plans include the use of intrusion detection systems, firewalls, and endpoint protection. Once again, all of these methods are aiming to monitor and block potential threats. 

Real-time threat detection is like automation. In this method, security experts are continuously monitoring networks and systems to ensure a quick and effective response if any unfortunate situation occurs. 

It involves the use of technologies like SIEM (Security Information and Event Management) and machine learning to monitor network traffic, detect anomalies, and respond in real time.

Benefits and Importance of Proactive Cybersecurity and Real-time Threat Detection

Both of these techniques are basically behavioral analytics and machine learning combined with contextual analysis. With the help of such methods, companies can better understand their current stats regarding both known and unknown threats.

Security experts use proactive and real-time threat detection to catch any suspicious activity. For instance, social engineering attacks, corrupted credentials, targeted malware, phishing, and the use of expired accounts. 

Real-life examples of proactive cybersecurity and real-time threat detection can be seen in the case of Equifax's massive data breach in 2017. The breach, which exposed the personal data of 147 million Americans, was caused by a failure to patch a known vulnerability in the company's software.

Cybersecurity Threat Analytics Platform

It is yet another system/platform that allows security experts to interpret the organization’s current risk state. It consolidates data from multiple sources and provides insightful reports on the current situation of the system.

CTA platform works by monitoring a global network of over one million sensors that capture and quantify billions of daily events on the internet. Once the data is read, it is then compared and correlated with the known cyber threats and trends. It helps the company to have a complete picture of its cyber risks in real time.

If we go back to the Equifax stat mentioned earlier, after the attack, the said company implemented a threat analytics platform. It assisted them in quickly identifying and mitigating cyber threats, resulting in a 40% reduction in alerts and a 50% reduction in response time.

The platform allows organizations to prioritize their cyber risk mitigation efforts by identifying the most critical vulnerabilities across their organization. It also helps organizations identify compromised systems without having to wait for their security or IT teams to investigate. 

Overall, the Cybersecurity Threat Analytics platform provides a proactive approach to cybersecurity that helps organizations stay ahead of potential threats and protect their critical assets.

Final Thoughts

All businesses/corporations must opt for cybersecurity analytics. If they can’t implement such methods themselves due to their complexity, they can always opt for different cybersecurity providers or they can subscribe to third-party cyber security services such as outsourced cybersecurity.

Every point of this guide is mainly focusing one thing which is using cybersecurity analytics to predict and act against potential cyber-attacks. In easy words, foreseeing a cyber threat and getting ready in advance. Only this point alone showcases how beneficial and important cybersecurity analytics are.

The future of cybersecurity analytics is likely to involve a greater emphasis on automation and artificial intelligence. As threats become more complex and numerous, security teams will need to be able to respond more quickly and effectively.