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Impact of COVID-19 on Security Analytics

  • Rishab Krishanmurthy
  • Oct 12, 2020
  • Big Data
Impact of COVID-19 on Security Analytics title banner

The effect of the COVID-19 pandemic on businesses and markets is apparent. The impact has affected trillions of dollars of revenue worldwide.

 

The pandemic has resulted in a massive upheaval of the top hundred companies. Business leaders are attempting to adapt to this new world. We are seeing them change their strategies to focus more on survival rather than long-term growth. 

 

It has also resulted in the growth of several new sectors. One such field is security analytics.

 

 

What is Security Analytics?

 

Security analytics analyzes data to produce strong security measures. To work right, it requires the collection of data, filtering said data, using integration, and then linking to give a detailed security overview of an organization’s digital infrastructure.

 

The security analytics market gives businesses the ability to foresee security threats using analytics software. The value of security analytics has increased exponentially during the COVID-19 pandemic.  


 

There are multiple reasons for this:

 

  1. Multiple threats surface every single day

  2. Undetected weaknesses exist in every system

 

Both of these factors can completely wreck the infrastructure of the business and bring business operations to a grinding halt.


 

Most modern businesses have several devices connected to internal networks. Connections to an internal network are like a double-edged sword. While these connections increase the ability to communicate throughout the organization, they also create an increased risk to the infrastructure. The entire network comes under threat if one system is compromised.


 

Security analytics acts like a protective barrier that alerts your business to threats before they happen to your network. The following data points, combined with security analytics, generate the alerts:

 

  1.  Real-time data 

  2. Geolocation detection information

  3. Threat intelligence.

 

A predictive security mechanism gets created using this system.


 

SIEM v. Security Analytics

 

Security information and event management (SIEM) systems collect log data that gets generated by monitored devices. For example: 

  1. network equipment, 

  2. computers, 

  3. storage, 

  4. firewalls

 The log data helps identify specific security-related events occurring on individual machines. The data then gets aggregated and used to determine what happens across an entire system. The report enables organizations to identify any variations in expected behavior. The identification of this behavior can formulate and implement the necessary responses.

 

Drawbacks of SIEM:

 

  1. Legacy SIEM systems cannot handle modern continuous integration/continuous delivery (CI/CD) lifecycles based on frequent build and deployment cycles.

  2. Legacy SIEM cannot process large data sets.

 

Unlike legacy SIEM systems, security analytics takes advantage of cloud-based infrastructure. And, since cloud storage providers can provide almost unlimited data storage that can scale according to the needs of the organization, the company is not limited by the corporate data storage and retention policies. Also, security analytics can collect and store data more efficiently. It is better at handling modern DevOps practices and CI/CD systems.

 

While related to SIEM, security analytics software may provide more advanced tools for data mining or freeform analytics.

 

 

Why is this sector growing?

 

Fuelling the growth is the fact that cyber-attacks and threats are increasing exponentially per day. A large portion of these attacks will be successful. As a result of these successful attacks, Businesses turn to security analytics to help secure their systems.

 

An example of one such attack is the Capital One data breach, where one of the largest financial institutions on the planet had its firewalls breached by hackers, which led to the theft of critical data. The public relations backlash and the impact of this notable data breach made other enterprises stand up and take notice. 

 

The result was increased adoption of security analytics by businesses, which in turn set the security analytics market on a phenomenal growth trajectory.


 


This image shows networks, and how devices are interconnected.

Modern networks show a high degree of interconnectivity



There are several other drivers key to the growth of security analytics, including:

 

  1. Transitioning from protection to detection: 

    1. Hackers use a wide range of attack mechanisms that exploit multiple vulnerabilities. 

    2. Some threats can go undetected for months. 

    3. Security analytics tools can keep track of common threat patterns and send alerts the moment an anomaly gets discovered.

 

  1. A unified view of the enterprise: 

    1. Security analytics structures data in such a way that it offers both a real-time and historical view of events. 

    2. Security Analytics provides a unified view of threats and security breaches from a central console and allows for smarter planning, faster resolution, and better decision making.

 

  1. Seeing results and a return on investment: 

    1. There is mounting pressure on IT teams to communicate results to senior management and stakeholders. 

    2. Security analytics provides time-to-resolution metrics and fewer false positives, which allows for a prompt response. 

 

Now, this article will look at some security analytics techniques and processes. 

 

 

Security Analytics Process

 

Depending on the types of tools installed, security analytics solutions can incorporate large and diverse data sets into their detection algorithms. The collection of Security analytics data happens in several ways, including:

  • Network traffic

  • Endpoint and user behavior data

  • Cloud resources

  • Business applications

  • Non-IT contextual data

  • Identity and access management data

  • External threat intelligence sources

 

Recent technological advancements in security analytics include adaptive learning systems that fine-tune detection models based on experience and learnings, as well as anomaly detection logic. These technologies accumulate and analyze real-time data that includes:

 

  • Asset metadata

  • Geolocation

  • Threat intelligence

  • IP context

This data is collected and used for both immediate threat responses and investigations. 

 

Primary Use Cases

 

Companies can deploy security analytics for a wide variety of use cases. Some use cases include the following:

  • analyzing network traffic to detect patterns indicating potential attacks;

  • monitoring user behavior, including potentially suspicious activity;

  • detecting potential threats;

  • detecting data exfiltration;

  • monitoring employees;

  • detecting insider threats;

  • identifying compromised accounts;

  • identifying improper user account usage, such as shared accounts;

  • investigating malicious activity;

  • demonstrating compliance during audits; and

  • investigating cybersecurity incidents.

 

Security analytics software tools are tools that provide proactive or exploratory network security via behavioral machine learning or analytics techniques. Alternately described as SA Platforms or network traffic analytics software, these tools collect, normalize, and analyze network traffic for threat behavior. Vendors who specialize specifically in SA offer machine learning tools for applying security models to traffic across enterprise assets.


 

Features of Security Analytics Software

 

Security analytics platforms provide the following features or target for analysis:

  • Ingested data from SIEM or other sources

  • User and entity behavior analytics (UEBA)

  • Automated or on-demand network traffic analysis

  • Model observed behavior against threat intelligence

  • Configure analytics that analyses the data in the context of policy.

  • Application access and analytics

  • DNS analysis tool

  • Email activity

  • Network packets

  • Identity and social persona

  • File access

  • Geolocation, IP context


 

Benefits of Security Analytics

 

One of the benefits of security analytics is the sheer volume and diversity of information that gets analyzed at any one time. This data can include, but is not limited to:

 

  1. Endpoint and user behavior data

  2. Network traffic

  3. Business applications

  4. Cloud traffic

  5. Non-IT contextual data

  6. External threat intelligence sources

  7. Access and identity management data

  8. Proof of compliance during an audit

  9. Security incident and anomaly detection and response.

  10. Enhanced forensics capabilities 

 

By analyzing such a wide range of data, organizations can easily connect the dots between various alerts and events. The result is proactive security incident detection and faster response times that help the business to protect the integrity of systems and data.

 

Security analytics tools also assist in compliance with industry and government regulations. Regulations such as PCI-DSS and HIPAA require organizations to monitor data activity and log data collection for forensics and auditing purposes.

 

 

Conclusion

 

Cyber attacks and data breaches occur every day. The volume of incidents further stresses the need for security mechanisms. Security Analytics helps facilitate this.


You can check out more articles about information security here and here.

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