How AI Can Improve Threat Intelligence Gathering and Usage
Published Nov 10 2023 12:00 AM 3,205 Views
Microsoft

Cybersecurity is one of the most pressing challenges in the digital age. Cyberattacks can cause significant damage to organizations and individuals, compromising their data, reputation, and operations. To prevent and mitigate cyberattacks, organizations need to adopt a proactive and informed cybersecurity strategy that allows them to make strategic decisions about their threat model and ability to effectively respond to ever-evolving threat actors. One of the key components of such a strategy is threat intelligence. 

 

Threat intelligence is the process of collecting, analyzing, and sharing information about cyber threats, their actors, and their techniques. This information can help organizations anticipate and defend against cyberattacks, as well as improve their security posture and resilience. However, threat intelligence requires a lot of data, skills, and resources to perform effectively. This is where artificial intelligence (AI) can play a vital role.  

 

AI can help automate and enhance various aspects of threat intelligence gathering and usage, such as data collection, in-depth analysis, smart sharing, and cutting-edge technology.  

 

You can watch our latest livestream with our guest Jessica Sapucaia, here:

 

 

You can learn more about best practices and standards for developing and sharing threat intelligence among different stakeholders and how to get started in the cybersecurity world. 

 

Getting to the Sources of Threat Data 

 

One of the biggest challenges facing defenders is having actionable threat data to work with. Gathering relevant data on cyber threats, their actors, and their techniques from various sources is non-trivial. Data sources such as network logs, security alerts, open-source intelligence (OSINT), dark web forums, social media platforms, and more need to be analyzed and prioritized to allow defenders to make timely decisions to defend the data estate. To put it in perspective, Microsoft alone collects over 65 trillion signals daily that must be synthesized using sophisticated data analytics and AI algorithms to understand and protect against digital threats and criminal cyberactivity. When coupled with the 300+ known threat actors that are active at any point in time, all with unique motivations and tactics, it’s easy to understand why having meaningful threat intelligence is critical to protecting and defending against increasingly sophisticated attacks. 

 

AI can help automate this process by using machine learning algorithms to identify patterns, trends, and suspicious behaviors from large amounts of data. AI can also help identify new threats by analyzing data from different sources and finding correlations and anomalies. For example, AI can detect phishing emails by analyzing their content, sender, attachments, links, etc., and flagging them as malicious. 

 

Digging Deeper into the Data 

 

AI can also help enrich the collected data by adding context and metadata, such as geolocation, timestamps, and indicators of compromise (IOCs) providing security operators and incident responders with greater knowledge for making security decisions. This in-depth analysis works to extract valuable insights from the collected data allowing it to classify the threats according to their severity, impact, and criticality, prioritizing them accordingly. 

 

AI assists in this step by using machine learning algorithms to identify patterns and trends that are not visible to humans. AI can also use natural language processing (NLP) to understand the meaning and intent of textual data, such as threat reports, blogs, news articles, etc., and extract relevant information from them. 

 

AI can also use reasoning and inference algorithms to draw conclusions and recommendations from the analyzed data. For example, AI can suggest possible mitigation strategies or countermeasures based on the identified threats. 

 

Sharing Responsibly 

 

The adage that “Cybersecurity is everyone’s responsibility” holds true in threat gathering and the responsible sharing of threat data. Sharing threat intelligence information responsibly with relevant stakeholders within or outside the organization can help protect not only the organization but also the entire cybersecurity community from cyber threats. 

 

The 2023 Microsoft Digital Defense Report touches on the need for sharing data to improve the security posture of organizations:  

“Close collaboration between the public and private sectors to formulate, enforce, and harmonize these requirements is crucial to improve global cybersecurity and foster innovation.” 

 

AI can help facilitate this process by using machine learning algorithms to assist in determining the: 

  • Type of information to share. 
  • The most relevant source(s) of information. 
  • The recipient(s) of information
  • The level of trust between parties 
  • The legal and ethical implications of sharing information 

Ensuring that responsible AI best practices are incorporated into the decision-making process for sharing threat intelligence is critical to building boundaries for what information should be shared. For example, AI can help to anonymize or encrypt sensitive or confidential information before sharing it to protect the privacy and security of the parties involved. It can also help monitor and evaluate the effectiveness of threat intelligence sharing by using feedback mechanisms and metrics to measure the impact and value of shared information. 

 

Cutting-Edge Technology 

 

Tool suites continue to evolve to take advantage of capabilities of AI, using it to enhance the efficiency and accuracy of threat intelligence gathering and usage. New offerings such as Microsoft Security Copilot leverage generative AI to help protect organizations at machine speed and scale while allowing defenders to upskill quickly to meet the security needs of their users. 

 

These AI-powered tools help analysts identify and prioritize threats by providing suggestions and recommendations based on historical data, known cyberattack techniques, emerging threats and established best practices. They also help analysts automate repetitive or tedious tasks such as data collection or analysis, working to lower the time to action and remediation of an incident. 

 

These complementary capabilities are an example of how AI can augment human capabilities rather than replace them. By using AI as a “copilot”, analysts can leverage the strengths of both human intelligence and artificial intelligence to achieve better results. 

 

Conclusion 

 

AI is transforming the way we approach cybersecurity. Threat intelligence gathering and usage are critical components of any cybersecurity strategy. AI can help improve threat intelligence gathering by automating data collection, extracting valuable insights through meticulous data analysis, sharing threat intelligence information responsibly, and using advanced tools to enhance the effectiveness of threat intelligence strategies. 

 

As organizations continue to look for ways to strengthen their cybersecurity defense, AI will be looked to more frequently to improve threat intelligence gathering and usage. 

1 Comment
Co-Authors
Version history
Last update:
‎Nov 07 2023 01:29 PM
Updated by: