Skip to content Skip to navigation Skip to footer

Cybersecurity analytics involves aggregating data for the purpose of collecting evidence, building timelines, and analyzing everything to design a proactive cybersecurity strategy. 

With a normal security information and event management (SIEM) system, you have to depend on testing things as they exist in a singular moment within the network. Cybersecurity analytics applies to the network as a whole, including general trends that may not be evident in a given snapshot. 

Cybersecurity analytics uses machine learning (ML) and behavioral analytics to monitor your network, spot changes in how resources or the traffic on the network are used, and enable you to address threats immediately.

Need for Cybersecurity Analytics

Transitioning from Protection to Detection

Traditional SIEM does a good job of addressing threats as they pop up. With cybersecurity analytics, your network security can detect threats before they impact your system. This is because the system observes network behavior and data flows, looking for potential threats.

A Unified View of the Enterprise

With cybersecurity analytics, you gain a bird’s eye view of the entire enterprise's network activity. You can discover devices on the network, as well as outline their configuration and event data. You can also keep track of when new devices join the network and track their behavior.

Seeing Results and an ROI

An effective cybersecurity analytics solution provides results of the system’s efforts in real time, showing the potential threats that have been mitigated and the general health of the network. This makes it easier to see the impact of the system on your network’s general safety.

Benefits of Cybersecurity Analytics Tools

Prioritized Alerts

Even though the vast number of cyber threats can result in your system being inundated with alerts, with cybersecurity analytics, you can prioritize the most pertinent alerts. This reduces the amount of time spent chasing down false or less-than-critical alerts, freeing up more time for your IT team.

Automated Threat Intelligence

In some ways, cybersecurity analytics is like next-generation SIEM, particularly in how it automates your threat intelligence. With ML tools, threats can be detected, categorized, and filed away to be used to detect similar ones in the future.

Proactive Incident Detection

A reactionary approach to cybersecurity can leave your system open to novel or developing threats. Cybersecurity provides you with a proactive strategy to identify and address threats, giving you a global view of not just what your network is currently dealing with but likely future threat events. This provides you with an advanced profile of the intelligence threats your network faces.

Improved Forensic Incident Investigation

With security analytics, you can see where attacks come from, how they managed to get inside your system, and the assets they affected. You can also have a timeline of the events that transpired outlined for later analysis.

SIEM vs. Cybersecurity Analytics

While SIEM can collect log data from network devices and figure out what is happening in your system, it cannot handle the demands of continuous integration/continuous deployment (CI/CD). 

With CI/CD, code changes are deployed in a testing or production environment after the initial build of an application. Analyzing network events pertaining to each of these iterations requires an enormous amount of data processing and storage. Cybersecurity analysis uses cloud infrastructure to meet these intense storage and processing needs.

The Most Common Use Cases

Some of the typical use cases for cybersecurity analytics include:

  1. Analyzing traffic to identify patterns that may indicate attacks
  2. Monitoring user behavior
  3. Detecting threats
  4. Identifying attempts at data exfiltration
  5. Monitoring the activity of remote and internal employees
  6. Identifying insider threats
  7. Detecting accounts that have been compromised
  8. Demonstrating compliance to standards such as the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI DSS)
  9. nvestigating incidents
  10. Detecting the improper use of user accounts

Big Data Security Analytics

It is important to conform to governance regulations while ensuring your organization’s systems are secure and cyber risks are minimized. This requires processing loads of data—and quickly enough to make your findings actionable. 

With big data security analytics, you can automatically collect information regarding all the endpoints on your network, as well as the behavior of individual users, groups of users, and subnetworks, including software-defined wide-area network (SD-WAN) connections. Big data analytics can also aggregate these large storehouses of data and analyze them to identify threats.

How Fortinet Can Help

The Fortinet management and analytics solution gives your organization simplified yet powerful network orchestration, response to threats, and automation for a variety of architectures, including cloud, hybrid, and on-premises environments. In this way, Fortinet can provide an organization with a unified threat detection and response system.

One of the primary tools of the Fortinet management and analytics solution is its next-generation firewall (NGFW). NGFWs filters the traffic on your network, protecting your organization from threats coming from both the outside and from within. It incorporates stateful firewall features, Internet Protocol security (IPsec), and support for secure sockets layer (SSL) and virtual private network (VPN) monitoring. FortiGate also enables you to perform deeper inspections on network traffic.

Using ML, FortiGate can identify malware and other types of cyber threats, including zero-day threats, and then block them. In this way, FortiGate provides your organization with a proactive cybersecurity analytics tool. Further, FortiGate includes paths that allow for future updates, which empower FortiGate to stay on top of the latest developments on the threat landscape, protecting the network when new threats reveal themselves.

FAQs

What is cybersecurity analytics?

Cybersecurity analytics involves aggregating data for the purpose of collecting evidence, building timelines, and analyzing everything in order to design a proactive strategy for cybersecurity.

What is the need for cybersecurity analytics?

With cybersecurity analytics, your network security is able to detect threats before they impact your system. It can also manage large amounts of data and process it to identify and mitigate threats.

What are the benefits of cybersecurity analytics tools?

The benefits of cybersecurity analytics tools include prioritized alerts, automated threat intelligence, proactive incident detection, and improved forensic incident investigation.

What are the most common use cases of cybersecurity analytics?

The most common use cases of cybersecurity analytics include:

  1. Analyzing traffic to identify patterns that may indicate attacks
  2. Monitoring the behavior of users
  3. Detecting threats
  4. Identifying attempts at data exfiltration
  5. Monitoring the activity of remote and internal employees
  6. Identifying insider threats
  7. Detecting accounts that have been compromised
  8. Demonstrating compliance to standards such as the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI DSS)
  9. Investigating incidents
  10. Detecting the improper use of user accounts