diff --git a/docs/guardrails.md b/docs/guardrails.md
index 72ec6f2..84decf6 100644
--- a/docs/guardrails.md
+++ b/docs/guardrails.md
@@ -9,13 +9,12 @@ applied around Large Language Model (LLM) inference.
 Their purpose is to ensure that LLM usage is **safe, auditable, and predictable**
 in enterprise environments.
 
-Guardrails are not about changing how models think — they are about controlling
-**how models are accessed and used**.
+Guardrails do not change how models reason or generate outputs — they control
+**how models are accessed, executed, and isolated** within the platform.
 
 In Cube AI, a *domain* represents an isolated workspace that groups users,
 permissions, configuration, and available models. Guardrails are applied
-**per domain**, ensuring strong isolation and access control between different
-workspaces.
+**per domain**, ensuring strong isolation and access control between workspaces.
 
 ---
 
@@ -31,6 +30,7 @@ Cube AI does **not**:
 
 - train models
 - fine-tune models
+- rewrite prompts
 - alter model outputs for ethical or policy reasons
 
 ---
@@ -38,46 +38,56 @@ Cube AI does **not**:
 ## What Guardrails Do
 
 ![Cube AI guardrails overview](/img/cube-ai-guardrails.png)
-This diagram shows how Cube AI guardrails enforce security and isolation at the
-platform level without interfering with application logic or model behavior.
+
+This diagram illustrates how Cube AI guardrails enforce security and isolation
+at the platform level without interfering with application logic or model behavior.
 
 Cube AI guardrails provide:
 
 ### Authentication & Authorization
 
-- token-based access control
-- domain-scoped permissions (workspace-level access control)
+- token-based access control (PATs and API tokens)
+- domain-scoped permissions
 - per-domain model visibility
+- enforcement of role-based access control (RBAC)
 
 ### Domain Isolation
 
 - strict separation between domains
 - no data or model leakage across domains
 - isolated execution contexts
+- domain-scoped configuration and model exposure
 
 ### Request Validation
 
-- validation of incoming requests
+- validation of incoming API requests
 - enforcement of API contracts
 - rejection of malformed or unauthorized calls
 
 ### Model Access Control
 
-- control which models are available per domain
-- backend-specific model exposure
+- control over which models are available per domain
+- backend-specific model exposure (e.g., vLLM, Ollama)
 - prevention of unauthorized model usage
 
 ### Secure Execution (TEE)
 
-- all inference runs inside **Trusted Execution Environments**
-- prompts and outputs remain confidential
-- memory isolation during execution
+When configured, model inference can execute inside
+**Trusted Execution Environments (TEEs)**.
+
+This provides:
+
+- confidential execution of prompts and responses
+- runtime memory isolation
+- attested secure connections between platform components
+- verifiable execution integrity
 
-### Auditing & Observability (Optional)
+### Auditing & Observability
 
-- request metadata logging
+- recording of security-relevant events
 - traceability of inference calls
-- integration with audit pipelines when enabled
+- integration with audit logs when enabled
+- support for compliance and forensic analysis
 
 ---
 
@@ -87,11 +97,11 @@ To avoid confusion, Cube AI guardrails do **not**:
 
 - rewrite or sanitize prompts
 - filter or censor model outputs
-- implement AI ethics policies
-- perform content moderation or alignment
+- implement AI ethics or content moderation policies
+- perform alignment tuning
 - replace application-level safety logic
 
-Guardrails ensure **platform safety**, not application behavior.
+Guardrails ensure **platform-level safety and isolation**, not application behavior.
 
 ---
 
@@ -102,14 +112,15 @@ Without guardrails, LLM deployments risk:
 - unauthorized access
 - data leakage between tenants
 - untraceable model usage
-- unpredictable behavior in production
+- insecure execution environments
+- unpredictable production behavior
 
 Cube AI guardrails make LLM usage suitable for:
 
 - enterprise deployments
 - multi-tenant environments
 - regulated industries
-- confidential workloads
+- confidential and sensitive workloads
 
 ---
 
@@ -117,19 +128,20 @@ Cube AI guardrails make LLM usage suitable for:
 
 Guardrails complement — but do not replace — application-level controls.
 
-Applications are still responsible for:
+Applications remain responsible for:
 
 - prompt design
 - output validation
 - business logic enforcement
 - user-facing safety mechanisms
 
-Cube AI ensures the **infrastructure layer is secure and controlled**.
+Cube AI ensures the **infrastructure layer remains secure, isolated, and auditable**.
 
 ---
 
 ## Next Steps
 
-- Learn how Cube AI executes models using **vLLM**
-- Explore **Models** to understand backend configuration
-- Use **Chat Completions** with guardrails enabled
+- Learn how Cube AI executes models using [vLLM](./vllm)
+- Explore available [Models](./api/models)
+- Use [Chat Completions](./api/chat-completions) with guardrails enabled
+- Review [Audit Logs](./security/audit-logs) for execution traceability