Modern Cybersecurity: Traditional And AI-Powered Threat Intelligence

Modern Cybersecurity: Traditional And AI-Powered Threat Intelligence

In this article, we look at both traditional and AI-powered methods of threat intelligence. Cybercriminals continuously devise new tactics and exploit vulnerabilities. The need for robust security measures that can evolve as criminals do—and even get ahead of them—has never been more pressing. Staying ahead of emerging cyber threats requires timely access to accurate and actionable threat intelligence.

Threat intelligence provides organizations with critical insights into the tactics, techniques and procedures used by cyber adversaries. Keep reading for an explanation of traditional and AI-powered threat intelligence approaches, as well as a look at what industry giant Microsoft is doing in this realm.

Traditional threat intelligence explained

Threat intelligence is the process of gathering, analyzing and interpreting information about potential and existing cyber threats. This information is used to better understand the tactics, techniques and procedures  employed by threat actors, as well as their motivations and targets. Threat intelligence involves monitoring various sources, such as dark web forums, malware repositories, hacker chatter and other online platforms where cybercriminals communicate or share their tools and strategies.

The main objectives of threat intelligence are to:

  • Provide context and insight into the threat landscape
  • Enable proactive and informed decision-making
  • Enhance risk assessment and vulnerability management
  • Support the development of effective cybersecurity strategies and countermeasures

Threat intelligence can be classified into three main types:

  • Strategic: Focusing on long-term trends, potential threats and industry-specific risks
  • Tactical: Providing actionable information about specific threats and how they operate
  • Operational: Offering technical details and indicators of compromise that can be used for immediate defensive actions

AI threat intelligence explained

AI-powered tools and techniques are used to automate and augment various processes involved in gathering, analyzing and disseminating threat information. These include:

  • Data collection and enrichment: AI-driven tools can automatically gather data from various sources, including open-source intelligence (OSINT), social media, dark web forums and security feeds. They can also enrich this data by cross-referencing it with existing threat intelligence feeds, providing context and relevance to potential threats.
  • Data processing and analysis: AI algorithms can process and analyze large datasets, identifying correlations, patterns and anomalies that might be indicative of emerging threats or attack trends. Machine learning techniques, like clustering and classification, can help categorize and prioritize threats based on their severity and potential impact.
  • Predictive analysis: AI can analyze historical threat data and current trends to predict potential future attack vectors or targets. By recognizing patterns in previous attacks, AI can offer insights into likely future threats, allowing organizations to allocate resources effectively.
  • Natural language processing (NLP): NLP techniques enable AI systems to analyze text-based information from a variety of sources (news articles, research papers, hacker forums, etc.). This helps in understanding threat actors’ motivations, tactics and intentions.
  • Threat hunting: AI can assist in proactively seeking out threats that may not yet be widely known. It can help security teams discover new attack techniques or vulnerabilities by analyzing behaviors that deviate from the norm.
  • Automated indicator extraction: AI algorithms can automatically identify indicators of compromise (IoCs) and relevant metadata from various sources. This accelerates the process of extracting actionable threat information from raw data.
  • Malware analysis: AI-driven tools can aid in the automatic analysis of malware samples, identifying their behavior, capabilities and potential impact. This helps in understanding the malware’s purpose and how to counteract it.
  • Real-time threat detection: AI-powered security solutions continuously monitor network traffic, and log files and system behavior in real-time. These systems can identify unusual or suspicious activities that might indicate ongoing attacks.
  • Automated report generation: AI can assist in creating comprehensive threat intelligence reports by summarizing key insights, trends and recommended actions for security teams and stakeholders.
  • Vulnerability management: AI can assist in identifying and prioritizing vulnerabilities within an organization’s systems by analyzing data from vulnerability databases, exploit repositories and security advisories.

Microsoft’s threat intelligence

Microsoft’s approach to using AI-powered threat intelligence involves collecting and analyzing massive amounts of data to identify potential threats, predict emerging risks and respond effectively to cyberattacks.

In Microsoft’s March 2023 announcement of Security Copilot, (available through private preview at the time of this writing) they state:

Microsoft is uniquely qualified to help customers explore and adapt AI to boost their cybersecurity defenses. Microsoft Security is actively tracking more than 50 ransomware gangs as well as more than 250 unique nation-state cybercriminal organizations, and receives 65 trillion threat signals every day. Microsoft technology blocks more than 25 billion brute-forced password theft attempts every second, and more than 8,000 security professionals at Microsoft analyze more security signals than almost any other company — on average Microsoft’s Security Operations Center analysts utilize over 100 different data sources.

Security Copilot will simplify complexity and amplify the capabilities of security teams by summarizing and making sense of threat intelligence. Teams will be able to better see through the noise of web traffic and identify malicious activity.

Security Copilot isn’t the only AI-powered threat intelligence tool in Microsoft’s arsenal. Here are other ways Microsoft incorporates AI-powered threat intelligence:

  • Microsoft uses predictive analysis to aid in predicting the most appropriate times to roll out security patches. This minimizes the window of opportunity for attackers to exploit unpatched vulnerabilities.
  • Microsoft’s cloud-native Security Information and Event Management (SIEM) solution, Azure Sentinel, leverages AI and machine learning to collect, analyze and correlate data from various sources, enabling security teams to detect and respond to threats more efficiently. It can detect suspicious activities, perform threat hunting and provide insights into security incidents.
  • Microsoft Defender Advanced Threat Protection (ATP) employs AI-driven endpoint security to protect devices from advanced threats. It uses behavior-based detection and analysis to identify and respond to sophisticated attacks, including zero-day exploits and fileless malware.
  • Microsoft’s Security Graph is a centralized platform that aggregates and analyzes data from various sources, including endpoints, cloud services and networks. AI algorithms process this data to detect patterns and anomalies, enabling quicker threat detection and response.
  • Microsoft actively participates in security collaborations and shares threat intelligence across industries and communities. Through AI-enhanced data anonymization and aggregation, they can share threat insights without compromising individual organizations’ privacy and security.

As technology and cyber threats continue to evolve, Microsoft will push boundaries of AI innovation to enhance their security products further. As AI continues to advance, Microsoft’s threat intelligence capabilities will become increasingly sophisticated, enabling even more precise and actionable insights.

Threadfin’s SecOps

Threadfin’s experts implement traditional and AI-powered threat intelligence solutions for customers and partners alike. As businesses increasingly relying on the cloud for operations and resources, the security risks posed by applications, endpoints, infrastructure and users become more challenging to visualize and more difficult to respond to. Our human-centered SecOps is proactive and collaborative, ensuring the safety, privacy and trust of users and stakeholders. We address security considerations at the earliest stage of planning and development—and every stage thereafter. Contact us to learn more.

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