diff --git a/Document/content/4.0_Appendix_and_References.md b/Document/content/4.0_Appendix_and_References.md
index c51804b..e327867 100644
--- a/Document/content/4.0_Appendix_and_References.md
+++ b/Document/content/4.0_Appendix_and_References.md
@@ -1,7 +1,7 @@
 
 # **4. Appendixes and References**
 
-## **Introduction**
+### **Introduction**
 
 This chapter provides all supporting materials that complement the main body of the OWASP AI Testing Guide.
 The appendixes offer structured frameworks, threat models, risk lifecycles, and domain-specific guidance that reinforce the methodology proposed in the guide.
@@ -14,9 +14,7 @@ These resources serve three primary goals:
 
 The chapter concludes with a complete **References** section that documents all sources used throughout the guide.
 
----
-
-## **4.1 Appendix A: Rationale for Using SAIF (Secure AI Framework)**
+### **4.1 Appendix A: Rationale for Using SAIF (Secure AI Framework)**
 
 Appendix A introduces the rationale for adopting the **Secure AI Framework (SAIF)** as a foundational model for trustworthy AI development and testing.
 
@@ -29,9 +27,7 @@ SAIF provides:
 
 This appendix explains why AI requires a framework beyond traditional software testing paradigms.
 
----
-
-## **4.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification**
+### **4.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification**
 
 This appendix presents the **DIE model**—Distributed, Immutable, Ephemeral—as a lens for identifying threats in cloud-native and modern AI environments.
 
@@ -44,9 +40,7 @@ AI systems often include:
 These characteristics create unique attack surfaces.
 The DIE framework helps testers recognize threats such as: supply-chain injection, poisoned artifacts, workflow manipulation, and cloud environment exploitation.
 
----
-
-## **4.3 Appendix C: Risk Lifecycle for Secure AI Systems**
+### **4.3 Appendix C: Risk Lifecycle for Secure AI Systems**
 
 Appendix C describes the **AI-specific risk lifecycle**, reflecting the dynamic and evolving nature of AI systems.
 
@@ -60,9 +54,7 @@ The lifecycle includes:
 
 Special attention is given to phenomena unique to AI systems, such as data drift, model drift, and feedback-loop risks.
 
----
-
-## **4.4 Appendix D: Threat Enumeration to AI Architecture Components**
+### **4.4 Appendix D: Threat Enumeration to AI Architecture Components**
 
 This appendix provides a structured mapping of **threats across AI architectural components**, including:
 
@@ -79,9 +71,7 @@ For each component, the appendix details:
 
 This enumeration forms the basis for the testing procedures defined earlier in the guide.
 
----
-
-## **4.5 Appendix E: Mapping AI Threats Against AI System Vulnerabilities (CVEs & CWEs)**
+### **4.5 Appendix E: Mapping AI Threats Against AI System Vulnerabilities (CVEs & CWEs)**
 
 Appendix E connects AI-specific threats to established vulnerability taxonomies such as:
 
@@ -92,9 +82,7 @@ Appendix E connects AI-specific threats to established vulnerability taxonomies
 This mapping demonstrates how threats like model extraction, prompt injection, and training data leakage relate to traditional software weakness classes.
 The goal is to integrate AI-security testing with existing enterprise vulnerability management workflows.
 
----
-
-## **4.6 Appendix F: Domain-Specific Testing**
+### **4.6 Appendix F: Domain-Specific Testing**
 
 This appendix outlines considerations for **testing AI systems in specific industry domains**, including:
 
@@ -107,9 +95,7 @@ This appendix outlines considerations for **testing AI systems in specific indus
 Each domain presents unique risks, regulatory frameworks, and operational constraints.
 The appendix provides guidance on tailoring AI testing strategies to sector-specific requirements and workflows.
 
----
-
-## **4.7 References**
+### **4.7 References**
 
 The final section compiles all sources cited throughout this guide, including standards, academic research, industry papers, and open-source projects.
 These references provide the foundational material supporting the frameworks, methodologies, and recommendations outlined in the OWASP AI Testing Guide.