Threat Intel – a Crucial Part of Cybersecurity

What is thread detection? 

Threat detection is a key practice to information security. Identifying threats and detecting them on time helps to ensure enterprise security. If a threat is detected, then mitigation efforts must be enacted to properly neutralize the threat before it can exploit any vulnerabilities.

Some of the most common threat detection processes are implemented through Extended Detection and Response (XDR) solutions and Security Information and Event Management  

Threat detection techniques 

Cyber intelligence

Cyber threat intelligence is the process of identifying and analyzing threats that have targeted the organization in the past, and have the potential to do so in the future. It’s also used to compare activities in an environment to known possibilities of malicious activity.

Analysts use any threat intelligence from their organization, or from security groups to apply to their data. If a malicious attack happens to another organization, they can post those indicators of compromise (IOCs)  for any organization to use and uncover similar patterns in their security data.   

Cyber threat intelligence seeks to understand the methods attackers are using, vulnerabilities in the company’s network, systems, and applications, and the identity of attackers seeking to infiltrate networks. This information helps cybersecurity and threat mitigation efforts. and keeping business owners informed about potential risks. 


Modeling is a mathematical approach to detecting threats by defining and measuring deviations. It’s based on an underlying assumption that the detection engine can sufficiently distinguish malicious activity from legitimate activity. Modeling methods may include machine learning. 

Modeling is the evolution of configuration analysis and helps reduce false positives. There are many ways to model environments, most of them strive to understand the modeled assets. The benefit of this detection type is when done with a well-trained model it can detect unknown malicious actions. Modeling can also support other detection types by prioritizing malicious activity that may occur within the same period as other threat detections.  

The challenges that modeling has are a significant investment in understanding all aspects of the systems. Also requires constant training. Most models require significant training before adding value, and consistent maintenance will be required. 




Indicators are elements of information that identify a particular state and context. In information security, there are good and bad indicators used for different purposes. There can be indicators for legitimate files and illegitimate files. 

Indicators of Compromise (IOCs) are common references for indicators in information security.  Security teams can easily derive indicators after observing threat activity. Most indicators originate from existing investigations or analyses like malware analysis. Indicators made through automated methods are ineffective and can accidentally include legitimate activity. Indicators made through the analytic process are more effective. 

When properly created, indicators identify specific activity that gives the context to properly prioritize and respond to the activity observed. IOCs are widely used indicators. They can be used to indicate a wide range of activities –  anything with specific data and context.  

The main benefit associated with indicators is knowledge enrichment – it takes data or knowledge the security team already has and enriches it with new knowledge. This reduces false positives and adds context to existing information. As a scoping tool, the security team may create indicators specific to the malicious activity they observe in the environment. Indicators are not good threat detection tools on their own and must be used with other efforts. 


Threat detection tools and techniques are rapidly evolving to meet the changing threats to network and data security. These threat detection technologies belong to every organization’s security information. 

Network threat technology 

Network threat technology monitors traffic in an organization’s network, to actively scan for suspicious activities that may indicate malicious activity. This technology reduces response time for threat detection and reaction, making it a critical tool for countering the increasing number of hacker attacks. 

Endpoint threat technology 

Endpoint threat detection and response is an endpoint security solution that implements continuous monitoring and collection of endpoint data with rules-based automated response and analysis capabilities. This technology makes it possible to monitor and collect activity data in real-time. With this data, teams can quickly identify threat patterns, and generate an automatic response that removes or contains threats. 

SIEM detection technology  

Security event technology collects events like authentication, network access, and logs from important systems in one place. This simplifies tasks like comparing log data against potential issues. SIEM technology enables security analysts to gain a full view of all their endpoints, including firewalls, IDS/IPS devices and apps, servers, switches, OS logs, routers, and other applications. 


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