Articles Cyber Security Threat Intelligence Automation

Threat Intelligence Automation

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Cyber Threat Intelligence (CTI) is a framework and technology that generates intelligence that can, or will respond to cyber threat attacks that are now taking place, based on heterogeneous and large amounts of information such as data, images, and other data. It gains attention for its ability to gather meaningful knowledge to better understand the intentions of attackers and ultimately predict future attacks. This offer comprises a single solution that brings together all the different aspects of cyber threat intelligence, from threat analysis to threat detection, in a single point of contact.

Tactical use cases of threat detection services include threat detection, threat analysis, and threat prevention and response. To build a more enriching context around a threat, the tip must allow threat data to be complemented by third-party threat analysis and applications – intelligence analysts can. It is based on the Gartner Cyber Threat Analysis (CTI) concept and the Threat Assessment Frameworks.

When implemented correctly, the Cyber Threat Intelligence Framework can significantly improve and complement the overall security position of the organization by enhancing the ability to automate workflows and improve incident response, enabling you to make informed and accurate decisions. It can also help stakeholders to provide automated feedback on sightings and observations, which allows them to validate threat intelligence and assess the breadth and impact of cyber threats. It can also help to support an overall stronger security mindset by automating processes and equipping administrators with tools to better prevent and respond to potential threats. Whitepapers, threat analysis reports, and more can be found in the Resources section or follow the blog.

First of all, we know that some threat-detection processes still involve manual steps that can be automated, such as data collection, analysis, reporting, and report analysis.

Modern threat intelligence must also include machine deep learning, which can be extended to isolated security platforms. Threat information must be relevant and coupled with the right context, and it must enable organizations to stay one step ahead of attackers and monitor important security alerts and events. By automating threats, intelligence agencies can ensure that important events come out on top in security alerts. Just as criminals automate the vulnerability process, financial institutions must focus on automating threat information to inform security updates as quickly as possible.

Machine learning and artificial intelligence can serve as enablers for automating security, improving the ability of automated cybersecurity systems to deliver clear analyses, recognize patterns, understand behavior, and solve problems. The implementation of the findings outlined above can help ensure the success of the automation of cyber threats. Each organization may have its own requirements and needs, but we encourage you to review your own use of threat information and see if it has the potential for your organization to improve and automate your own processes. While automation will initially focus on more static processes, it will lead to the next level of threat detection, helping analysts move from a more traditional approach to a much more dynamic and flexible approach.

Threat intelligence automation is an important step in the maturation of the industry and must be seen as a vital area. In addition to discussing how to integrate automation of intelligence into existing systems, we will also address two technology standards that play a role in CTI automation: machine learning and artificial intelligence.

While the cybersecurity industry as a whole is still working to improve the automation of cyber threat intelligence, we can start to focus our efforts and improvements here. The Threat Intelligence Platform is an emerging technology discipline that helps organizations collect, correlate, and analyze threat data from multiple sources in real time to support counter-measures. These capabilities are complemented by intelligence on threats to provide continuous data on potential threats, including indicators of compromise, to give security experts a better understanding of known threats and potential for future threats. Threat information is information in itself, and often requires connectivity to security applications, software, and SIEM tools to build a more complete picture of a threat to effectively combat potential cyber attacks.

As this is becoming more common in security solutions, an integrated security architecture will be needed to leverage these insights and tools. Threat-sensing solutions, based on machine learning to automate large-scale data collection, can solve many of these problems when they attempt to turn threats into effective operational threat sensing.

Organizations can benefit from providing an end-to-end solution with SOAR that leverages automation and integration of security tasks and tools to improve their threat intelligence and escape attackers. Threat deployment – Intelligence tools can strengthen network security and are critical to maintaining strong, proactive security operations. By properly operationalizing threat information, you can achieve scale and efficiency, shorten the overall time to implement threat information, and be more proactive in your security program.

SOAR solution enables security analysts to focus on high-priority incidents by automating time – managing tasks such as threat information collection, enriching compromise indicators (IOCs) with context, and reducing low-level threats. Making SOAR available as an AI-driven solution in a variety of security tools and tools will allow your security team to work on more strategic issues, develop threat information, and focus less on detecting unknown threats and more on proactive security operations. [Sources: 5, 14]

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