Microsoft Copilot for Security - Threat Intelligence Use Cases - Part 1

March 11, 2024 | 4 min read

BlueVoyant

Missed the first blog post in this series? Read it here: Revolutionizing Security with AI: An Introduction to Microsoft Copilot for Security.

In this blog series, our AI experts are focusing on how Microsoft Copilot for Security can benefit different cybersecurity teams. In this blog post, we will look at threat intelligence teams and how Copilot for Security can help them with incident attribution, detecting zero-day threats, and sorting and distributing threat intelligence.

Microsoft Copilot for Security for Threat Intelligence Teams

Threat intelligence refers to the knowledge gained from analyzing various sources of data to identify and understand potential threats. It involves collecting, analyzing, and interpreting information about adversaries and includes their motives and methods. Threat intelligence is essential for predicting, preventing, finding, and eradicating threats more effectively.

Microsoft Copilot for Security and Cyber Incident Attribution

Incident attribution is the process of identifying and assigning responsibility to the individuals, groups, or entities behind a cyber attack. It involves investigating various technical indicators, such as malware signatures, network traffic patterns, and digital footprints left behind by the attackers, as well as their tactics, techniques, and procedures (TTPs). Attribution can be challenging since bad actors obfuscate their identity through techniques like using proxies, previously compromised systems, or false flags.

Copilot for Security can significantly enhance cyber incident attribution by analyzing large volumes of data, identifying patterns, and providing valuable insights into their origin. Here are some examples:

  • Behavioral Analysis - Analyze the behavior and TTPs of attackers and malware to identify patterns and characteristics that may indicate a specific threat actor or group
  • Threat Intelligence Integration - Integrate feeds such as open-source intelligence (OSINT), dark web forums, and proprietary databases to provide context and attribution information
  • Malware Analysis - Automatically analyze and classify malware samples based on their behavior, code structure, and similarities
  • Network Traffic Analysis - Use network traffic patterns to identify suspicious behavior and communication patterns. Correlate network logs, packet captures, and flow data to identify and trace the source
  • Machine Learning Models - Leverage historical attack vectors, victim profiles, and attack outcomes to train AI models and improve efficiency
  • Forensic Analysis Automation - Automate collecting, analyzing, and correlating digital evidence from multiple sources, including disk images, memory dumps, and log files
  • Predictive Analytics - Use historical cyber incident data, such as TTPs, to identify trends and predict future attack patterns to understand where defenses may require hardening

Microsoft Copilot for Security and AI in general can enhance cyber incident attribution and predictability by automating data analysis, integrating threat intelligence, and identifying patterns. Organizations can use that information to improve their ability to identify attackers, hunt threats, and defend against future attacks.

Microsoft Copilot for Security and Zero-day Threats

Zero-day vulnerabilities present potential threats that are unknown to the vendor and have no patches or signatures. Microsoft Copilot for Security can play a crucial role in identifying and eliminating zero-day vulnerabilities. Here's how:

  • Anomaly Detection - Analyze network traffic, system logs, and user behavior to identify subtle deviations from normal patterns
  • Behavioral Analysis - Analyze the behavior of software and systems to identify unusual system calls, API usage, or file access patterns that may indicate the presence of a zero-day threat
  • Machine Learning Models - Use historical data to identify similarities between past attacks and current system behavior
  • Vulnerability Scanning - Analyze code and configurations to flag suspicious code patterns or settings
  • Zero-Day Sandbox Analysis - Execute suspicious files or code in a controlled environment to analyze their behavior and identify potential zero-day exploits
  • AI-driven Fuzzing - Use AI to automatically generate large volumes of invalid, unexpected, or random data inputs to bombard systems and uncover vulnerabilities such as crashes, memory leaks, or security flaws

By identifying anomalous patterns and behaviors, incorporating historical data, and reviewing settings and configurations, Copilot for Security can help identify a zero-day vulnerability or an active threat. It can assist in finding, testing, and validating them so they can be controlled and not do harm.

Microsoft Copilot for Security and Threat Intelligence Distribution

Using AI to distribute threat intelligence involves leveraging Copilot’s machine learning and automation to disseminate relevant threat information to stakeholders. Here's how AI can be applied in the distribution of threat intelligence:

  • Natural Language Processing (NLP) - Normalize and format communication and include insights from unstructured data sources, such as threat reports, news articles, and social media feeds
  • Automated Prioritization - Prioritize threat intelligence based on factors such as severity and relevance to a stakeholder’s assets
  • Customized Alerts - Create profiles for stakeholders and tailor intelligence alerts and reports to their specific needs
  • Contextual Analysis - Analyze and sort threat intelligence within the context of the organization's environment such as the network infrastructure, business processes, applications, industry, compliance requirements, etc.
  • Automated Distribution - Implement automated distribution mechanisms to disseminate threat data to stakeholders promptly
  • Integrate Communication - Use APIs and integration capabilities to seamlessly include threat intelligence reporting within existing communication mechanisms
  • Feedback Loop and Continuous Improvement - Incorporate feedback from stakeholders to improve the relevance and effectiveness of threat intelligence gathering, sorting, formatting, and distribution

BlueVoyant is an early adaptor and member of Microsoft’s Design Advisory Council for Copilot for Security. Further, BlueVoyant was recognized by the Microsoft Intelligent Security Association (MISA) as the Security MSSP (Managed Security Service Provider) of the Year. Our commitment to our clients is to continually provide guidance on how and where to optimize security operations with Microsoft, including Copilot for Security.

Read the next blog post in this series, Microsoft Copilot for Security - Legal, Governance and Compliance Use Cases.