diff --git a/Document/content/tests/AITG-MOD-03_Testing_for_Poisoned_Training_Sets.md b/Document/content/tests/AITG-MOD-03_Testing_for_Poisoned_Training_Sets.md
index 4da54db..a3715b7 100644
--- a/Document/content/tests/AITG-MOD-03_Testing_for_Poisoned_Training_Sets.md
+++ b/Document/content/tests/AITG-MOD-03_Testing_for_Poisoned_Training_Sets.md
@@ -30,7 +30,7 @@ This test identifies vulnerabilities associated with poisoned training datasets,
 - **Regular Audits and Data Drift Monitoring**: Continuously monitor the statistical distributions of your training data. Sudden changes or drifts can indicate that new, potentially malicious data has been introduced into your collection pipeline.
 - **Enforce MLOps Security**: Secure the entire MLOps pipeline. Use strict access controls on data storage, version control for data and code, and require reviews for any changes to the data preprocessing or training scripts.
 
-### Suggested Tools for this Specific Test
+### Suggested Tools
 - **Cleanlab**: A data-centric AI package that automatically detects and corrects label errors, outliers, and other issues in datasets - [Cleanlab on GitHub](https://github.com/cleanlab/cleanlab)
 - **Adversarial Robustness Toolbox (ART)**: Provides tools for crafting data poisoning attacks (for testing defenses) and implementing detection methods like activation clustering - [ART on GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox)
 - **Data Version Control (DVC)**: An open-source tool for data versioning, which is crucial for maintaining data integrity and reproducibility - [DVC Website](https://dvc.org/)