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In the ever-evolving landscape of cybersecurity, staying ahead of vulnerabilities is crucial. Pauli Porkka, a Technical Security Lead at M-Files, has shared a Proof of Concept (PoC) project that enriches Microsoft Defender XDR data with live EPSS (Exploit Prediction Scoring System) data using KQL (Kusto Query Language). This project aims to provide a more dynamic approach to vulnerability management by integrating EPSS data into Defender XDR, allowing security teams to prioritize vulnerabilities based on their likelihood of exploitation.
You Should Know:
1. Understanding EPSS and Defender XDR Integration:
- EPSS is a scoring system that predicts the likelihood of a CVE (Common Vulnerabilities and Exposures) being exploited in the wild. It provides a probability score between 0 and 1, where a higher score indicates a higher likelihood of exploitation.
- Defender XDR is a comprehensive security solution that provides advanced threat protection across endpoints, identities, emails, and applications.
- By integrating EPSS data into Defender XDR, security teams can prioritize vulnerabilities that are not only critical but also have a high probability of being exploited.
2. KQL Query for EPSS Enrichment:
- The KQL query provided by Pauli Porkka fetches recent EPSS data (within the last 100 days) with a high EPSS score (>40%) and joins it with the `DeviceTvmSoftwareVulnerabilities` table in Defender XDR.
- This allows security teams to correlate Defender XDR’s vulnerability data with EPSS scores, providing a more nuanced view of the threat landscape.
let EPSSData = externaldata (cve: string, epss_score: double) [ h@"https://api.first.org/data/v1/epss?days=100&score=>0.4" ] with (format="json"); DeviceTvmSoftwareVulnerabilities | join kind=inner (EPSSData) on $left.CveId == $right.cve | project DeviceName, CveId, VulnerabilitySeverityLevel, epss_score | sort by epss_score desc
3. Key Observations:
- The `VulnerabilitySeverityLevel` in Defender XDR does not always align with the EPSS score. A high EPSS score does not necessarily mean the vulnerability is marked as “critical” in Defender XDR.
- This discrepancy highlights the importance of using multiple data sources to assess the risk posed by vulnerabilities.
4. Practical Steps to Implement the Solution:
- Step 1: Clone the repository containing the KQL query from Pauli Porkka’s GitHub:
git clone https://github.com/pporkka/DefenderXDRQueries.git
- Step 2: Navigate to the directory containing the KQL query:
cd DefenderXDRQueries
- Step 3: Open the KQL query in your preferred editor and customize it according to your environment.
- Step 4: Run the query in your Defender XDR environment to enrich your vulnerability data with EPSS scores.
5. Handling Rate Limiting:
- First.org’s API has rate limits, so ensure that your queries are optimized to avoid hitting these limits. Consider caching the data locally if you need to run frequent queries.
What Undercode Say:
Integrating EPSS data into Defender XDR is a powerful way to enhance your vulnerability management strategy. By combining the predictive power of EPSS with the comprehensive threat detection capabilities of Defender XDR, security teams can make more informed decisions about which vulnerabilities to prioritize. This approach not only improves the efficiency of your security operations but also helps in reducing the attack surface by focusing on the most exploitable vulnerabilities.
Expected Output:
- Enhanced Vulnerability Prioritization: By correlating EPSS scores with Defender XDR data, you can prioritize vulnerabilities that are both critical and likely to be exploited.
- Improved Threat Intelligence: The integration provides a more comprehensive view of the threat landscape, enabling proactive threat hunting and mitigation.
- Optimized Security Operations: With better prioritization, your security team can focus on the most pressing threats, reducing the time and resources spent on less critical vulnerabilities.
Additional Resources:
By leveraging this integration, you can significantly enhance your organization’s ability to predict and mitigate potential exploits, ensuring a more robust cybersecurity posture.
References:
Reported By: Porkkap Kql – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
