AI Threat Intelligence

Artificial Intelligence (AI)  in threat intelligence refers to the use of artificial intelligence technologies and techniques to improve the detection, analysis, and response to cyber threats. AI threat intelligence automates the process of collecting and analyzing information about threat actor behaviors and vulnerabilities across the clear and dark web, as well as chaînes Telegram illicites

What is Flare’s AI Threat Intelligence Capability?

How does Flare use AI for threat intelligence?

Flare’s AI threat intelligence capability takes inputs from various foreign languages to deliver actionable intelligence at scale. Flare’s AI Assist translates threat actor forum posts into English and enriches them with additional technical content. It seamlessly translates Russian, Arabic, Spanish, French, and other foreign languages, enabling actionable dark web and illicit Telegram channel monitoring

How does Flare’s AI enable security analysts?

FlareAI Assist enables senior security professionals to work faster and empowers junior security professionals by explaining complex technical exposure in meaningful ways. The AI threat intelligence capabilities automates parsing infected devices logs and identifies high-risk resources that can be accessed by the device being sold. 

What are the key benefits of Flare’s AI threat intelligence solution?

  • Automatically scan the clear and dark web, including illicit Telegram channels, for any mentions of the organization’s name, domains, or user login credentials. 
  • Translate threat actor posts so security analysts can read and understand the scope of the risk. 
  • Reduce the impact of the cybersecurity skills gap by making technical information approachable and actionable regardless of the security analyst’s experience level. 

AI Threat Intelligence: A Brief Overview

What is AI in threat intelligence?

AI threat intelligence technologies process large volumes of data to identify patterns and anomalies indicating potential security threats. Data analytics and algorithms can analyze datasets from various sources including:

  • Forums du darkweb
  • Chaînes de télégrammes illicites
  • Stealer logs

AI, especially large language models (LLMs), can scan structured and unstructured data to identify for information like:

  • Mentions de marque
  • Informations d'identification divulguées
  • Corporate domains
  • Data on sites like Pastebin
  • Sensitive information hardcoded and stored on GitHub

How does AI fit into the threat intelligence cycle?

AI is most effective when security analysts can use it to enhance their decision making capabilities and optimize resource allocation across all part of the threat intelligence lifecycle:

  • Planning and collection: After defining intelligence requirements, security analysts can use AI to identify relevant information using keyword and pattern recognition that filters out irrelevant data to focus on extracting actionable intelligence. 
  • Processing and analysis: By removing duplicate or redundant entries in large datasets, AI enhances data quality so that security analysts can spend time on more critical activities like analyzing data and providing insights to reduce cybersecurity risks. 
  • Production and dissemination: Since AI can synthesize data efficiently, security teams reduce human error risks and provide more actionable reports with insights into the threat’s nature, potential impact, and mitigation measures.
  • Rétroaction et amélioration: By fine-tuning their AI solutions, security teams can “teach” them how to prioritize threats based on factors like industry, size, and geographic region. 

How does AI-powered threat intelligence work?

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AI threat intelligence automates various time-consuming, previously manual tasks associated with gathering and analyzing security information. The process typically works like this:

  • The solution ingests information from various sources, like social media posts, news articles, dark web forums, and illicit Telegram channels. 
  • The algorithms, often using natural language processing techniques, extract valuable data points, like keywords, brand names, and malicious actor objectives. 
  • The solution analyzes this data to identify patterns and relationships that indicate potential threats to the organization’s networks, systems, and data, like targeted phishing attacks or malware campaigns. 
  • The technology generates threat intelligence reports, that include insights and actionable threat prevention or mitigation steps, including details like threat type, source, potential impact, and recommended security measures. 

Why is AI Threat Intelligence Important in Today’s Cybersecurity Landscape?

What are the benefits of AI in threat intelligence?

In a dynamic digital world, organizations need rapid insights that help them keep pace with threat actors who always seem to be one step ahead of security teams. Using AI in the threat intelligence gathering, analysis, and reporting process provides several benefits, including:

  • Vitesse: AI can process and analyze vast quantities of data much faster than people, often providing real-time or near-real-time insights. 
  • Évolutivité: With AI, organizations can leverage more data than they would be able to review with manual processes. 
  • Précision: AI provides more accurate results, reducing human error risks and their impact. 
  • Perspectives: AI generates insights by correlating and finding patterns in data that enable people to use the results to make data-driven decisions. 
  • Productivité: Security analysts can focus their talents on responding to detected threats rather than sifting through piles of difficult to correlate data. 

What are the use cases for AI in threat intelligence?

When trying to determine where security teams can use AI threat intelligence, some typical examples include:

  • Chasse aux menaces: identifying and extracting indicators of compromise (IoCs) from unstructured data. 
  • Analyse des données historiques: identifying the underlying patterns in an organization’s security data based on information about threat actor tactics, techniques, and procedures (TTPs)
  • Digital brand protection: scanning for and identifying questionable listing that misuse a brand to perpetrate fraud or sell fake products
  • Protection contre les risques numériques: reducing the external threat and attack surface by protecting digital assets and brand from threat actors
  • Data leakage monitoring: scanning for and identifying leaked corporate information, like hardcoded keys stored in public GitHub repositories or corporate credentials being sold online after a data breach
  • External threat detection: proactively monitoring for external threat actors targeting the organization, like malware, hacking, and social engineering attacks

What to look for in an AI threat intelligence solution?

When looking to integrate AI threat intelligence into their security monitoring, organizations should consider:

  • Data quality and breadth: While more data improves the analytics model, it can create too much noise so the solution should ensure data quality as well. 
  • Les sources de données: Solutions should include clear and dark web sources, like dark web forums, dark web marketplaces, source code repositories, leak paste and dump sites, cloud buckets and documents, illicit Telegram channels. 
  • Intégration dans les flux de travail existants: To make threat intelligence part of the security team’s daily activities, the AI solution should integrate with the technologies that the team currently uses for monitoring, like security information and event management (SIEM) and ticketing technologies.  

AI Threat Intelligence and Flare

Flare est le leader Gestion de l'exposition aux menaces (TEM) solution for organizations. Our technology constantly scans the online world, including the clear & dark web, to discover unknown events, automatically prioritize risks, and deliver actionable intelligence you can use instantly to improve security. With Flare’s AI threat intelligence capabilities, security teams gain real-time insights that include context and actionable mitigation activities to improve key metrics like mean time to detect (MTTD) and mean time to respond (MTTR). 

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