An Introductory Guide to Security Analytics

  • Bhumika Dutta
  • Oct 22, 2021
  • General Analytics
An Introductory Guide to Security Analytics title banner

When it comes to organizations and corporations, security is a critical consideration, particularly if the company relies on the internet. Because it is nearly difficult to foresee future assaults or dangers to the company, the best course of action is to take measures beforehand. 

 

Security analytics is a set of software, algorithms, and analytic procedures that are used to detect possible risks to computer systems. 


 

Organizations can use security analytics tools to analyze all the possible or potential threats that might impact the revenues or infrastructure of the company. In this article, we are going to discuss security analytics solutions, use cases, benefits, and the role of big data.

 

(Also read: 6 Advantages of AI in Cyber Security)


 

Security Analytics

 

The practice of employing data gathering, aggregation, and analysis tools for security monitoring and threat detection is known as security analytics. 

 

Security analytics systems may combine huge and different data sources into their detection algorithms, depending on the sorts of instruments deployed. 

 

According to Verizon's 2018 Data Breach Investigations Report, a cybercriminal may acquire critical data in minutes, if not seconds. However, IT teams may not be aware of the breach for hours, days, or even weeks. A third party, such as law enforcement or a consumer, is often the one who discovers the breach. 

 

(Recommended read: 7 Best Data Security Practices)

 

There are many ways for the data to be collected for security analytics. According to this blog written by the digital guardian, some of them can be the network traffic, endpoint and user behavior data, cloud resources, business applications, non-IT contextual, identity and access management data, external threat intelligence sources, and many other ways. 

 

Adaptive learning systems that fine-tune detection models based on experience and learnings, as well as anomaly detection logic, are examples of recent technical breakthroughs in security analytics. 

 

Many real-time data like asset metadata, geolocation, threat intelligence, and IP context are analyzed with the help of security analytics. 

 

Why do we need Security Analytics?

 

  • The cybersecurity industry is expanding due to advancements in cyber attackers' strategies and tactics, which may corrupt a system in seconds and go undiscovered for months. 

  • Attacks are notoriously difficult to detect because they occur rapidly and the signs are scattered over many data sources such as network servers, endpoints, and apps. 

  • Organizations may use security analytics to see how complicated attack tactics like compromised credentials, lateral movement, and data exfiltration are used. It aids in the early identification of attackers by analyzing user account actions for insider threat behaviors, unlike standard security technologies. 

  • Security analytics may also feed information back into the security ecosystem of a company, allowing other systems to respond to suspicious activity.

 

(Related read: Cybersecurity in IoT)

 

What is a security analytics solution?

 

To detect and diagnose risks, security analytics systems employ both real-time and historical data. Different techniques of data analysis may be used by a security analytics solution. 

 

Traditional rule-based approaches, as well as statistical analysis and machine learning, are among them. Other components can be added to the program to automate and coordinate events. 

 

Information may be obtained from a variety of sources, including:

  • Workstations, servers, sensors, mobile devices, and other endpoints send real-time alerts.

  • Other IT security apps' real-time feeds (firewalls, intrusion prevention, endpoint detection and response, etc.)

  • Volumes and kinds of network traffic

  • Logs from the server

  • Threat intelligence inputs from third parties.

 

(Must read: Understanding Why Securing AI Is Important)

 

Advantages of Security Analytics:

 

There are many benefits of security analytics, some of the key benefits are given below:

 

  1. Security analytics tools are successfully able to detect any potential future threat as they collect data from various sources and connect dots to reach a possible conclusion. This alerts the company beforehand and gives them ample time to prepare for any upcoming mishaps.

 

  1. As discussed earlier, regulatory compliance of the government and industry with security analytics tools is one of the main advantages. Security analysis tools can integrate a wide range of data types to give companies a single, unified view of all data events across devices, as required by regulations like HIPAA and PCI-DSS. 

 

Regulations like HIPAA and PCI-DSS require measures like data activity monitoring or log collection for auditing and forensics, and security analysis tools can integrate a wide range of data types to give companies a single, unified view of all data events across devices.

 

  1. For performing forensic investigations into events, security analytics tools are quite useful. Security analytics tools can reveal where an attack came from, how it was compromised, what resources were hacked, what data was stolen, and more, as well as a chronology for the occurrence.

 

(Also read: What are Strategic Analysis Tools?)

 

 

Use Cases of Security Analysis:

 

Many use cases are implemented during cybersecurity analysis. As mentioned in exabeam, some of them are listed below:

 

  1. Employee Monitoring:

 

With the help of security analytics, companies can monitor and analyze user behaviors for suspicious activity on important and sensitive systems, including privileged users via metadata, keystrokes, and forensics capabilities.

 

 

  1. Network analysis and visibility(NAV):

 

As traffic from end-users and apps passes over the network, a NAV application or device analyses it. 

 

NAV is a suite of technologies that encompasses network discovery, flow data analysis, network metadata analysis, packet capture and analysis, and network forensics, according to the Forrester Wave for Security Analytics Platforms 2018 (source). 

 

The business can correlate events and discover trends that may signal a possible attack by monitoring network data.

 

 

  1. User behavior monitoring:

 

User and entity behavior analytics (UEBA) is used by the analytics platform to profile suspicious behavior by utilizing algorithms to find trends and detect signs of harmful activities in user behavior.

 

 

  1. Data exfiltration detection:

 

By restricting illegal communication routes and preventing users from providing their credentials to non-enterprise sites, security analytics prevents unauthorized data downloads and copies, avoiding credential theft from phishing attempts.

 

 

  1. Compliance regulation:

 

The security analytics platform enables the compliance team to discover compliance violations by automating compliance obligations such as log data collecting, controlling personal data flow, monitoring data activity, and producing reports.

 

Big Data in Security Analytics:

 

  • For threat analysis, big data analytics use complex statistical and data science models to detect abnormalities in real-time. 

  • To identify and respond to cyberattacks, these technologies produce security warnings and integrate them with other forensic data. 

  • Organizations can monitor for insider risks using big data analytics, which can activate automated procedures if dangerous behaviors are discovered.

  • These technologies employ machine learning and data analysis to forecast assaults and build baselines for what is a normal activity to highlight unusual behavior. Anomaly user behavior alerts security teams, who may then build timelines to properly comprehend the end-to-end effect.

 

(Also read: 10 Companies that Uses Big Data)

 

Machine learning in Security Analytics:

 

Machine learning is the process of teaching computers to recognize patterns in data sets. Machine learning is paired with extra data science-driven statistical modeling skills in security analytics to discover both known and unknown patterns with high accuracy.

 

Advanced threat analysis using machine learning can:

 

  • To model anomalies, provide a baseline of typical behavior. This is true for both user activity and network traffic.

  • Analyze malware activities that are undetected by antivirus software to determine whether they are a danger.

  • To discover trends and detect intrusions, compare historical data of incursions and assaults.

 

(Related read: Top 6 Machine Learning Techniques)

 

 

Conclusion

 

Security analytics can assist an IT department in making sense of the massive amounts of data coming in and out of its network, as well as detecting possible attacks promptly. A security analytics program can safeguard a company against a potentially expensive data breach or cyberattack by providing real-time intelligence and a historical record of prior threats. 

 

(Similar read: Web Analytics: Tools and Advantages)

 

Above all, via the correlation of actions and alarms, security analytics aims to convert raw data from diverse sources into actionable insights to identify situations that require rapid attention. Security analytics technologies achieve this by adding a vital filter to the massive amounts of data provided by users, apps, networks, and other sources.

 

In this article, we have discussed the definition, need, and solution to security analytics along with its use cases, advantages, and use of big data and machine learning. 

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