How to Unlock Hypothesis-Based Threat Hunting with Microsoft Sentinel

In today's fast-paced digital environment, threat hunting has become a crucial element in any cybersecurity strategy. With cyber threats evolving rapidly, organizations must take a proactive approach to identifying and mitigating potential security risks. One of the leading tools in this domain is Microsoft Sentinel, an advanced SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) platform.

As of September 2024, Microsoft Sentinel's Threat Hunting Blade stands out for enabling hypothesis-based threat hunting, giving security teams the tools to anticipate and neutralize threats before they cause significant damage.

The Importance of Threat Hunting

In recent years, cyberattacks have become more sophisticated, often bypassing automated defenses and traditional security measures. Attackers are leveraging AI, advanced persistent threats (APTs), and fileless malware that evade conventional detection. To counteract these sophisticated methods, organizations must adopt a proactive cybersecurity posture.

By implementing threat hunting, security teams are empowered to:

• Uncover hidden threats that have evaded traditional defenses.
• Reduce the dwell time of attackers within a network.
• Mitigate the damage of potential breaches.
• Enhance overall security resilience by identifying vulnerabilities and gaps in security configurations.

Microsoft Sentinel’s Threat Hunting Blade: An Overview

Microsoft Sentinel’s Threat Hunting Blade is specifically designed to enhance the capabilities of security operations teams. As part of Microsoft’s cloud-native SIEM platform, this blade enables analysts to manually investigate potential threats using Sentinel’s integrated intelligence, detection, and automation tools.

Key features of the Threat Hunting Blade include:

• Interactive Query Language: Sentinel’s hunting queries are powered by Kusto Query Language (KQL), allowing users to perform deep forensic searches across their cloud and on-premises environments.
• Customizable Dashboards: The Threat Hunting Blade provides interactive dashboards that display real-time alerts and findings from custom hunting queries.
• Seamless Integration: Sentinel integrates seamlessly with other Microsoft security solutions, such as Defender for Endpoint and Azure Security Center, enabling a comprehensive view of the threat landscape.
• AI-Driven Insights: With AI and machine learning algorithms powering its analytics, Sentinel continuously improves its ability to detect anomalous behavior patterns, helping hunters refine their hypotheses.

Steps to Hypothesis-Based Threat Hunting with Microsoft Sentinel

Here’s how to utilize Microsoft Sentinel’s Threat Hunting Blade to conduct hypothesis-driven threat hunting effectively:

1. Develop a Hypothesis

   The first step in hypothesis-based hunting is to identify potential threats. This starts by analyzing known attack patterns, threat intelligence, or system anomalies to form a hypothesis. For example, if you observe unusual login patterns at odd hours, you could hypothesize that an attacker might be attempting to gain unauthorized access.

2. Construct Queries in the Threat Hunting Blade

   Once a hypothesis is formed, use Kusto Query Language (KQL) to search for indicators of compromise (IoCs) or anomalous behavior. Queries might include searching for:

   • Unusual login attempts from foreign IP addresses.
   • Abnormal process executions.
   • Data exfiltration activities.

   By crafting queries tailored to your hypothesis, you can narrow down potential threats.

3. Analyze Results

   The next step is to analyze the results produced by your queries. This can involve sifting through log data, network traffic, and endpoint activity to identify patterns that match or disprove your hypothesis. Microsoft Sentinel’s Threat Hunting Blade allows for deep dives into raw logs and correlating events across multiple systems.

4. Adjust and Iterate

   If the results do not confirm your initial hypothesis, refine your hypothesis based on the available data. Threat hunting is an iterative process that requires security professionals to be flexible and adjust their approaches. Use the built-in dashboards and insights within the Threat Hunting Blade to guide this process.

5. Automate Response with Playbooks

   Once a threat has been identified, you can use Sentinel’s automation capabilities to mitigate risks quickly. Playbooks, which are Sentinel’s automated workflows, allow you to respond in real time by isolating affected systems, disabling compromised accounts, blocking IP addresses, or notifying key stakeholders.

Real-World Example: APT Detection Using the Threat Hunting Blade

Let’s consider a scenario where an organization suspects an advanced persistent threat (APT) has infiltrated their network. The initial hypothesis is based on suspicious outbound connections to command-and-control servers in Eastern Europe.

The security team forms a hypothesis that specific user accounts are compromised.

They utilize KQL to search for unusual outbound connections from those accounts over a set period.

The search reveals that several accounts have indeed been making connections to known malicious IP addresses.

Based on this discovery, the team escalates their investigation, isolating affected machines and triggering automated playbooks to contain the threat.

This example demonstrates how hypothesis-based hunting in Sentinel allows teams to proactively identify, investigate, and respond to sophisticated attacks.

Best Practices for Using the Threat Hunting Blade

To maximize the effectiveness of Microsoft Sentinel’s Threat Hunting Blade, organizations should follow these best practices:

• Leverage Threat Intelligence: Use Microsoft’s built-in threat intelligence to enhance your hypotheses. Sentinel integrates with several threat intelligence feeds, including Microsoft Threat Intelligence, which continuously updates with the latest IoCs.
• Create Custom Dashboards: Customize the Sentinel dashboard to display the most critical metrics and alerts based on your organizational needs. This will help streamline threat-hunting activities and ensure you have real-time visibility.
• Collaborate with SOC Teams: Ensure that threat hunters are working closely with security operations center (SOC) teams to provide actionable insights that can lead to faster incident response.
• Automate Repetitive Tasks: Use SOAR capabilities within Sentinel to automate routine detection and response tasks, freeing up time for more complex threat-hunting activities.
• Stay Informed on Threat Trends: Continuously update your hypotheses based on the latest cybersecurity trends and attack vectors. The cyber landscape in 2024 is rapidly changing, and staying informed is key to effective threat hunting.
• Normalize Log Schema: Leverage the built-in Microsoft Sentinel Information Model (MSIM) functions to automatically normalize schema across disparate vendor log sources, saving substantial time. Sample use cases include standardizing Next-Gen Firewall, DNS, or Sysmon logs.
• Cross Tenant Queries: Threat hunters can execute queries across multiple tenants at the same time with Azure Lighthouse. This is a critical skill to master as it eliminates the need to individually query across each separate Sentinel instance.

Conclusion: Boosting Security Resilience with Hypothesis-Based Threat Hunting

As cyber threats continue to evolve, the importance of proactive threat hunting cannot be overstated. Utilizing Microsoft Sentinel’s Threat Hunting Blade empowers security teams to stay ahead of attackers through hypothesis-based hunting, KQL-powered investigations, and real-time automated responses.

By developing strong hypotheses, leveraging cutting-edge tools, and continuously refining hunting methods, organizations can significantly reduce their risk of being blindsided by advanced cyber threats.