Enhancing Wazuh Efficiency with AI: Meet the New AI Analyst in SOCFortress CoPilot
As cybersecurity analysts, we constantly face the challenge of making sense of a flood of alerts. Whether it’s identifying false positives, evaluating the severity of threats, or crafting exclusion rules to fine-tune our SIEM, the workload can be overwhelming. That’s why I’m thrilled to introduce a game-changing feature: the AI Analyst, now integrated directly into SOCFortress CoPilot.
This AI-driven capability is designed to help you evaluate alerts more efficiently, provide deeper context, and streamline exclusion rule creation. Let’s explore how this feature can revolutionize your SOC workflows.
Analyzing Alerts with AI
Example 1: Reconnaissance for Cached Credentials
Let’s start with an alert for potential reconnaissance for cached credentials triggered by the cmd.exe command.
1. Viewing the Alert Context
Upon opening the alert, I accessed the Assets tab to locate the affected hostname. CoPilot displayed key details, including the command line parameters that invoked cmd.exe and subsequent commands like net local group. These commands suggest attempts at user enumeration.
2. Leveraging the AI Analyst
• It identified the alert as a Sysmon Event ID 1 (process creation) triggered by suspicious use of the cmdkey.exe utility, often associated with credential harvesting.
• Contextual insights revealed a sequence of commands indicative of reconnaissance and potential privilege escalation.
3. Key Takeaways
While the cmdkey.exe binary was verified as legitimate using VirusTotal, the way it was invoked raised suspicions. This alert highlighted an elevated-risk action, warranting further investigation into user activity and process context.
Example 2: Decoding Encoded Commands
In another alert, Powershell was flagged for executing an encoded command. This is a classic technique used in cyberattacks to obfuscate malicious activities.
1. Context Analysis
The AI Analyst immediately identified that the alert stemmed from a base64-encoded Powershell command, commonly used for automation or scripting but also a frequent vector for malicious payloads.
2. Decoding with AI
After invoking the AI Analyst, the encoded command was translated into human-readable text. In this example, the command aimed to execute calc.exe — a benign action for demonstration purposes. However, the decoded command could just as easily have pointed to malware or data exfiltration scripts in a real-world scenario.
3. Actionable Insights
The decoded information provided full visibility into the encoded command’s intent, enabling analysts to decide whether the behavior was legitimate or malicious.
Streamlining Exclusion Rule Creation
Example 3: Generating Wazuh Exclusion Rules
Managing false positives is a crucial part of fine-tuning your SIEM. For example, an alert flagged a Visual Basic Script (VBS) that I knew was legitimate. Instead of manually crafting an exclusion rule, I used the AI Analyst to generate one.
1. Automation with Precision
The AI created a draft exclusion rule based on the alert’s parameters. While I reviewed the syntax to ensure completeness (e.g., adding specific directory paths), the pre-built rule saved time and effort.
2. Improved Efficiency
With the AI’s assistance, creating exclusion rules becomes faster and more accurate, reducing alert fatigue and allowing analysts to focus on genuine threats.
Final Thoughts
The AI Analyst is more than just a feature; it’s a tool designed to empower SOC analysts to work smarter, not harder. By integrating this capability into CoPilot, we’re providing the tools to enhance threat detection, streamline investigations, and simplify SIEM management.
If you’re ready to take your SOC operations to the next level, start using the AI Analyst today.
Need Help?
The functionality discussed in this post, and so much more, are available via the SOCFortress platform. Let SOCFortress help you and your team keep your infrastructure secure.
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