diff --git a/Document/content/2.0_Threat_Modeling_for_AI_Systems.md b/Document/content/2.0_Threat_Modeling_for_AI_Systems.md
index f25ff5d..f775a46 100644
--- a/Document/content/2.0_Threat_Modeling_for_AI_Systems.md
+++ b/Document/content/2.0_Threat_Modeling_for_AI_Systems.md
@@ -105,4 +105,4 @@ The data layer can be decomposed in the following sub-components:
 - **Data Sources (SAIF #18) (note):** An AI system’s data may originate from internal operational databases, user-generated inputs, IoT sensors, or third-party providers. Internal sources are governed by organizational policies and monitored for access anomalies.  
 - **External Data Sources (SAIF #19):** These sources can be external data feeds, such as purchased market data or public APIs that require additional vetting for quality, licensing compliance, and security. Organizations enforce contractual and technical controls (e.g., encrypted channels, mutual authentication) to secure these external connections, and continuously audit feed health and integrity.  
   - Note: The dotted arrow from SAIF #4 (Application) to SAIF #18 (Internal Data Sources) in the SAIF architecture represents a feedback loop, where data generated during application runtime such as user inputs, interaction logs, or model outputs may be captured and stored internally. This data can later be used for fine-tuning or retraining the model.
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+