## 6 Lab-Built Scenario and Benchmarking
### Quantitative Benchmarking Results
#### 6.3.1 System Technical Overview  
Figure 9 visually outlines the intricate internal security flow of a device, specifically an NVIDIA Orin, when hardened with the AZTRM-D framework. This diagram emphasizes the continuous, layered security posture applied directly at the device level. The sequence initiates with a rigorous Secure Boot Process, which is foundational for establishing a chain of trust from the very first software loaded. This process cryptographically verifies the integrity and authenticity of the bootloader, kernel, and initial operating system components before they execute. This crucial step prevents malware from tampering with the system at its most fundamental level, ensuring that the device isn't launching with compromised or malicious code. Following this, Continuous Device Health and Environmental Monitoring actively assesses the device's operational state and physical surroundings. Device health monitoring checks for signs of malfunction, resource exhaustion (like critically low memory or CPU overload), or unusual system behavior that could indicate a security issue, such as an unauthorized process consuming excessive resources. Environmental monitoring keeps track of physical conditions like temperature, humidity, and power supply. Drastic or unexpected changes in these parameters can signal physical tampering, cooling system failures, or even attempts to disrupt the device's operation, potentially leading to data corruption or denial of service. Simultaneously, Device Inventory Management is crucial for maintaining an accurate record of all hardware components and deployed software versions. This information is vital for tracking necessary updates, managing licenses, standardizing configurations, and identifying any unauthorized or rogue devices appearing on the network. Closely linked to this is the regular execution of Firmware Integrity Checks. Firmware, the low-level software controlling the device's hardware, is verified using cryptographic hashes or digital signatures to confirm it hasn't been altered by malware aiming for persistent control at a very fundamental level of the device. Security is deeply integrated into the software update process through Automated Scanning on the Git Server before Deployment. This step highlights the DevSecOps \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Performance Metric** & **Measurement** & **Significance** \\ \hline Resource Utilization (AI Scans) & \begin{tabular}{l} 12–18\% average CPU overhead \\ during CI/CD pipeline scans \\ (SAST/SCA). \\ \end{tabular} & \begin{tabular}{l} Demonstrates that continuous \\ security scanning is computationally \\ feasible on edge devices without \\ crippling performance. \\ \end{tabular} \\ \hline Policy Enforcement Latency & \begin{tabular}{l} \textless{}40 ms for ZT access policy \\ evaluation at the Policy Enforcement \\ Point. \\ \end{tabular} & \begin{tabular}{l} Ensures that stringent, real-time \\ access checks do not introduce \\ significant delays that would impact \\ user experience or system-to-system \\ communication. \\ \end{tabular} \\ \hline AI Model Training Time (Initial) & \begin{tabular}{l} 14 h to train the insider threat model \\ using three months of log data on a \\ standard cloud GPU instance. \\ \end{tabular} & \begin{tabular}{l} Quantifies the initial setup cost for \\ the AI components, providing a \\ baseline for resource planning. \\ \end{tabular} \\ \hline Scalability Test (Endpoints) & \
\begin{tabular}{l} Successfully managed and monitored \\ up to 1000 concurrent IoT devices per \\ orchestrator instance in a simulated \\ environment. \\ \end{tabular} &
\begin{tabular}{l} Validates the framework’s \\ architecture can scale to support \\ large enterprise deployments. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 5: AZTRM-D Resource and Scalability Benchmarking (NVIDIA Orin).