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The Powerful Combination of Cyber Security and Data Analytics

Data is the Pollution Problem of the Information Age, and Protecting Privacy is the Environmental Challenge  – Bruce Schneier (American Cryptographer)

The massive proliferation of data over the years has provided unprecedented opportunities to business organizations. However, these interconnected systems of IoT devices and the availability of Big Data on multiple storage repositories have also increased the possibility of Cyber attacks and threats.

It takes a single vulnerability for the cyber criminal to break into the system and cause massive damage to the business enterprise concerned. With the spiraling complexity of Cyber attacks, it has become imperative to go beyond the conventional preventive approach towards the PDR (Prevent, Detect,  Respond) strategy. This is where Cyber Security Analytics comes in.

While the presence of data can be an invitation for a Cyber attack; combining Data Analytics and Cyber Security can also help in acquiring insights which can predict, identify and abate Cyber attacks.

In this blog, we will try to answer the question of what is Cyber Security Analytics, consider the different aspects of Data Analytics in Cyber Security, as well as look at the utility of Predictive Analytics in Cyber Security.

What is Cyber Security Analytics?

Cybersecurity Analytics is an advanced approach to Cyber Security which seeks to make use of the processes of data gathering, aggregation, attribution and analysis; for extracting valuable insights. These insights in turn help in performing crucial security functions which help in detecting and mitigating cyber attacks and threats.

Big Data Cyber Security Analytics solutions help in collating data from multiple sources like business applications, virus scanners, operating systems event logs, user behavior data and so on.

Organizations try to aggregate this data into a single data set which can be used by security experts for the application of appropriate searches and algorithms (data analytics) for the prior detection of cyber attack indicators.

Cyber Security and Data Analytics

Thus, Data Analytics in Cyber Security help security analytics experts to identify potential threats at an early stage and consequently provide them with the chance to abate them before they manage to creep into the network infrastructure, and result in data loss or breach or data leakage.

Machine Learning Technology forms an important part of Cyber Security Analytics. It helps in providing real-time threat and data analytics.

Cyber Security and Data Analytics: The Mighty Union

Cyber Security Analytics, Security Analytics or Big Data Cyber Security Analytics are different names for the single phenomenon of Cyber Analytics which is the offspring of the union between Cyber Security and Data Analytics.

Data Analytics deals with the management of data through its collection and storage as well as, with the techniques, processes and tools, which help in analyzing it. The main purpose is to extract meaningful insights which could help in undertaking business decisions, making predictions as well as improving its efficiency.

To know in detail about the field; check out our blog on “What is Data Analytics?

Cyber Security deals with providing protection and security to servers, computers, mobiles, programs, systems and networks; with the objective of defending them against malicious digital attacks.

To know in detail about the field; check out our blog on “What is Cyber Security?

Flowing from the above definition; Cyber Security Analytics can be understood as utilizing the power of Data Analytics for accomplishing a Cyber Security objective. When you use Data Analytics with the aim of comprehending data in a way which can diagnose vulnerabilities, describe risks, for security monitoring, make predictions for possible malicious attacks and suggest protective measures; you are indulging in Big Data Cyber Security Analytics.

There is a continuous exponential increase in the amount of data. Thus, there is an urgent need to comb and explore this data and conduct threat and data analysis for the detection of possible risks.

However, this is not possible without a robust Big Data Analytics framework which is made to fit within the Cyber Security system. Thus, Cyber Security Analytics help to forge a synergy between Cyber Security and Data Analytics in a way which could provide for a tough wall of safety.

Cyber Security and Data Analytics: The Mighty Union

Need for Cyber Security Analytics

In this section we will look at some of the factors which have propelled the emergence of Big Data Cyber Security Analytics.

  • According to BARC and KuppingerCole, Big Data and Information Security study, only 6% of companies say they aren’t exposed to any cyber security risks, and, 62% say digital security threats have increased in the past 12 months.
  • Cyber Security Analytics provides for the structuring of data in a way that it helps in a unified view of security breaches and threats which in turn allows for better planning and rapid resolution
  • Unprecedented advancement in the frequency and sophistication of Cyber attacks has been one the major propellers towards a Data Analytics Cyber Security mechanism. While it takes a single vulnerability for an attacker to bring down the system; the threat itself can go undetected for unusually long periods of time. A strategic policy of Cyber Security and Data Analytics taken together help in maintaining a track of threat patterns and thereby issue alerts on the event of any anomaly.

Data Analytics Cyber Security Strategy

Trying to incorporate Data Analytics in a Cyber Security framework entails the need to develop a proactive strategy. This plan of action can take the following path:

  • Data Gathering: It is important to collect all relevant data throughout the network of an organization into a single dataset. The data can be stored in cloud based repositories and at locations from where they could not be easily accessed by cyber criminals
  • Classifying and Sifting Data: You will be required to normalize data through the application of conventional security taxonomy. Grouping can be carried out for fields with common values which automatically have common names. This helps in streamlining search capabilities
  • Additional Data Gathering: It is important to expand the corpus of data available at your disposal. Collection of as much data as possible will help in unlocking new capabilities. It can provide for the application of stringent detection techniques as well as help in the extraction of contextual insights
  • Improve your Security Data: In this step, you can enrich and embellish your security data with data from internal sources like website data, business tools and so on; as well as from external sources like machine data, open source feeds and so on
  • Automating the process of Cyber Security Analytics: The accomplishment of the goals of Cyber Security and Data Analytics is dependent to a large extent on automation. This is because it is important to derive insights in real-time.
    Automation not only ensures timely extraction of data and insights from the same; but it can also be strategically used to issue a relevant response on the event of a cyber threat.
  • Identify and Detect: This is the action stage of Big Data Cyber Security Analytics. It involves the usage of different techniques and strategies for threat detection, refining queries and carrying out research. At the same time, it is important to identify the right form of detecting strategy. For instance, a statistical approach will be more suited to observe situations wherein there has been an unlikely spurt in network traffic
Cyber Security analytics process

Tools for Conducting Big Data Cyber Security Analytics

With the rising need for instituting a robust Cyber Security Analytics framework in place, an array of advanced Big Data Analytics Cyber Security Tools has come to the forefront. In this section, we will look at some of these Security Analytics solutions.

  • Security Orchestration, Automation and Response (SOAR): This Cyber Security Analytics solution can be considered as a nodal tool which seeks to establish connections between the processes of data gathering, the process of analysis and threat response applications.
  • Behavioral Analytics: This method seeks to indulge in Predictive Analytics in Cyber Security through examining and analyzing the behavioral patterns of devices and users. These patterns are studied in order to develop generalizations and consequently detect anomalies. For instance, this solution is widely used for identifying credit card fraud. Unusual high withdrawals can indicate a case of fraud.
  • Forensics: Forensic Big Data Cyber Security Analytics tools help in digging into attacks which are ongoing or have happened in the past. They also help in determining the ways in which the flaws of the system were exploited by cyber criminals as well as seek to detect potential vulnerabilities which could harm the organization in future.
  • Security Information and Event Management (SIEM) Platform: The SIEM platform helps in collaborating an array of Cyber Security Analytics tools for the purpose of collecting security data across the network infrastructure as well as aid in analysis and provisioning alerts on detection of possible threats.
  • Network Analysis and Visibility (NAV): As the name suggests, this tool is concerned with supervising traffic as it flows across the network. It deals with flow data analysis, network forensics, network discovery and network metadata analysis.
  • Threat Intelligence Software: These Cyber Security Analytics solutions help in providing valuable information about the most recent developments in the field of Cyber Security. These include information about zero-day attacks, new malware, other forms of unusual activity and so on. This helps security analysts to be better prepared for any impending attack or threat.

Cyber Security Analytics: Use Cases

Cyber Security and Data Analytics combined have significant applications in the pursuit of maintaining a robust Cyber Security system. In this section we will look at some of the use cases of Big Data Cyber Security Analytics.

  • Scrutinizing Network Traffic: Data Analytics in Cyber Security help you to acquire a glimpse of your network traffic and thus provide you with the ability to identify any kind of network anomaly. Simultaneously, you can also use cloud security tools in order to perceive threats in the cloud environment.
  • Detection of Insider Threats: Data breach or leakage can also be an intentional act of malicious insiders who possess access to sensitive data in the form of network credentials. Data Analytics Cyber Security tools can be utilized for sensing insider threats through keeping tabs on such activities as abnormal email usage, unsanctioned database requests, unusual login times and so on.
  • Unwarranted Data Access: Unapproved data exfiltration can happen as a result of data theft or data loss. Cyber Security Analytics solutions can help in obstructing unlicensed channels of communication and prevent individuals from giving in their credentials to barred sites.
  • Observe User Behavior in order to Perceive Threats: Data Analytics Cyber Security makes use of user and entity behavior analytics (UEBA) in order to develop algorithms which can help in discerning patterns of mischievous activity in user behavior.
Cyber Security Analytics: Use Cases

Benefits of Employing Data Analytics in Cyber Security

Combining Cyber Security and Data Analytics into a robust mixture helps in strengthening the digital security mechanism through early detection of threats and timely precautionary measures taken for the same. In this section, we will look at some of the advantages which accrue from employing Cyber Security Analytics strategies.

  • Threat Intelligence Automation: Automating the process of detecting threats is one of the significant benefits of Security Analytics. It not only helps in cutting down the amount of time spent in manual security activities, but also enhances accuracy as well as helps in monitoring large volumes of data.
  • Forensic Investigation: Cyber Security Analytics does not provide for random exposition of cyber threats and attacks. It helps in a detailed exploratory analysis of the origin of the attack, its severity in terms of impact, data affected as well as the way in which it happened. This kind of forensic investigation aids in wise security decisions in future.
  • Prioritizing Ability: The combination of Cyber Security and Data Analytics is a potent force which not only helps in early detection and resolution of security issues; but also provides for prioritization of alerts by ranking vulnerabilities. This helps security personnel in directing their attention to areas which are in need of immediate attention.
  • Ensuring Regulatory Compliance: There do exist industry standards and government compliance regulations such as HIPAA, GDPR, PCI-DSS and others. By way of overseeing access, behavior, and a unified view of data; Cyber Security Analytics help compliance managers to detect possible instances of non-compliance.


It is quite evident that Cyber Security Analytics has helped to bolster the security framework. The robust combination of Cyber Security and Data Analytics has helped to utilize the positive aspects of both domains.

Analyzing data not only helps in better preparedness, but also provides for early detection of threats as well as helps security analysts to come up with advanced response and mitigation strategies.

There has been an unprecedented escalation in the pace of Cyber attacks over the years. Increasing frequency and sophistication of these attacks have only propelled security analysts to search for newer and newer ways of dealing with the same. Given the dynamic nature of the domain, a career in Cyber Security is one of the most alluring fields within the tech industry.

We, at Syntax Technologies, provide you with an exciting opportunity to acquire skills in consonance with those of a Cyber Security expert. Read more about our Cyber Security course here:

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