How to test Exabeam's detection without running real attacks?

A common and entirely valid concern when evaluating a Security Information and Event Management (SIEM) platform like Exabeam is the efficacy of its behavioral analytics and detection rules. The prospect of "testing" these detections often leads organizations to consider running simulated attacks, which carries inherent risk and operational overhead. However, as an analytics engineer, I propose a more systematic, data-centric approach focused on the quality and structure of the input data pipeline, which is the true foundation of any detection.

The core premise is this: Exabeam's detections, particularly its UEBA and timeline features, are functions of the ingested log data. Therefore, a robust testing strategy should decouple the *logic* of the detection from the *execution* of an attack. We can achieve this by constructing a controlled, repeatable dataset that mimics the forensic signatures of malicious activity. The goal is not to test Exabeam's code, but to test *our deployment's* ability to surface anomalies given properly formed, suspicious data.

My recommended methodology involves three phases, executed in a development or lab instance:

**1. Data Pipeline Instrumentation & Baseline**
First, ensure you can reliably feed data into the test environment. Document the exact log sources, parsers, and normalization rules. Then, establish a baseline by ingesting a period of "clean" activity. This allows you to:
* Verify data quality (no parsing errors, correct field mapping).
* Confirm that normal user behavior is modeled without excessive false positives.
* Generate a benchmark for comparison.

**2. Synthetic Log Generation for Key Detection Scenarios**
Instead of launching a real brute-force attack, create a log file that represents it. For instance, to test an "Excessive Failed Logons" rule, generate a sequence of Windows Security events (Event ID 4625) for a single user account from multiple source IPs within a short time window. The key is to replicate the exact log schema Exabeam expects.

Here is a conceptual example using a CSV structure you could programmatically generate and feed via a Universal Forwarder:

```csv
timestamp,source_ip,destination_user,event_id,logon_type,failure_reason
2023-10-27T14:01:12Z,192.168.1.100,jsmith,4625,3,0xC000006A
2023-10-27T14:01:13Z,192.168.1.100,jsmith,4625,3,0xC000006A
2023-10-27T14:01:14Z,192.168.1.101,jsmith,4625,3,0xC000006A
... (repeated 50 times within 60 seconds)
```

**3. Targeted Detection Validation**
With synthetic logs ingested, you then validate specific outcomes:
* Does the session builder correctly group these events into a single, high-risk session?
* Does the relevant rule (e.g., "Brute Force Attack Detected") fire and create a notable incident?
* Does the user's risk score adjust appropriately?
* Are the events accurately reflected in the user's timeline?

**Key Advantages of This Approach:**
* **Safety:** No production systems or credentials are compromised.
* **Repeatability:** The same test can be run after every Exabeam version upgrade or pipeline change.
* **Precision:** You can isolate and test individual detection scenarios (data exfiltration, lateral movement, privilege escalation) by crafting the corresponding log artifacts.
* **Debugging:** If a detection does *not* fire, you can inspect the normalized data in Exabeam's Data Lake to see if your synthetic event lost critical context during parsing.

Ultimately, this shifts the testing paradigm from a black-box penetration test to a transparent data quality exercise. It allows you to answer the critical question: "Given a known-bad pattern in my logs, will my configured Exabeam instance find it?" This is the essence of analytics engineering applied to security operations.

I'm curious if others in the community have built frameworks for this, perhaps using tools like `logsynth` or custom Python generators, and how you've integrated such tests into a CI/CD pipeline for your SIEM content.

- dan