Upon completion of this course, participants will be able to:

• Evaluate and select appropriate **IoT and advanced sensor technologies** (e.g., thermal, acoustic, vibration) for multi-layered vault security.
• Design a secure **network architecture** for connected vault devices, adhering to strict cyber security and segmentation protocols.
• Implement and manage a **centralized security data fusion platform** to integrate alerts from diverse sensor types (physical, environmental, access).
• Utilize **AI and Machine Learning** for real-time anomaly detection and predictive alerting based on sensor data patterns.
• Develop and enforce robust **cyber security protocols** (e.g., patch management, device authentication) for all vault IoT devices.
• Establish detailed procedures for the calibration, maintenance, and continuous performance monitoring of sensor systems.
• Formulate a comprehensive **Emergency Response Plan** integrated with the intelligent alerting system for physical breaches or environmental failures.
• Understand the policy and legal requirements for data retention and privacy related to continuous surveillance and sensor data.

Target Audience:

• Physical Security and Surveillance Technology Managers.
• IT and Network Security Specialists for Cash Centers.
• Vault Managers and Operations Supervisors.
• Risk Management and Operational Technology (OT) Specialists.
• Internal Auditors focused on Integrated Security Systems.
• Security System Integrators and Procurement Managers.

• IoT Sensor Network Design and Cyber Security Threat Modeling Workshops
• Anomaly Detection Simulation Exercises using Sensor Data and AI Algorithms
• Group Activities on Developing a Policy for IoT Device Patch Management and Authentication
• Expert Lectures on Advanced Surveillance (Thermal/Acoustic) and Data Fusion
• Case Studies on Failed Vault Security Systems and Root Cause Analysis
• Individual Exercises on Designing a Centralized Security Data Fusion Dashboard

Unit 1: The Evolution of Vault Security Technology

• Defining IoT in the context of high-security physical environments (OT vs. IT).
• Overview of advanced sensor types: fiber optic perimeter, acoustic analysis, seismic, and thermal imaging.
• The concept of **Security Data Fusion**: integrating data streams from diverse, heterogeneous sensors.
• Analyzing the vulnerabilities of vault infrastructure and the role of sensors in detection.
• Case studies on the use of advanced sensors in preventing vault and perimeter breaches.

Unit 2: Network Architecture and Cyber Security

• Designing a highly secure, segregated **IoT/Sensor Network** architecture (e.g., air-gapped or heavily segmented).
• Implementing strict authentication, encryption, and secure boot protocols for all connected devices.
• Protocols for managing **vulnerability and patch management** for IoT devices (often unpatchable/embedded).
• Managing the cyber risk associated with remote access and maintenance of the security system.
• Establishing a continuous monitoring system for detecting unauthorized devices or network intrusions.

Unit 3: Real-Time Monitoring and AI Anomaly Detection

• Developing a centralized **Security Information and Event Management (SIEM)** system for sensor data.
• Utilizing **Machine Learning** algorithms to establish normal environmental and physical baseline patterns.
• Implementing **Anomaly Detection** (e.g., statistical thresholds, deep learning) to identify subtle, early signs of intrusion.
• Protocols for filtering and managing false positives to maintain operator trust in the alerting system.
• Designing intuitive visualization dashboards for real-time threat intelligence and environmental status.

Unit 4: System Implementation and Maintenance

• Technical requirements for sensor installation, optimal placement, and system calibration.
• Developing a comprehensive preventative and predictive maintenance program for all sensor hardware.
• Protocols for regular testing and simulation of security system failures and sensor response.
• Managing vendor contracts and service level agreements (SLAs) for continuous system support.
• The role of automated self-diagnosis and redundant power/communication sources.

Unit 5: Policy Integration and Emergency Response

• Integrating the intelligent alerting system with the overall **Emergency Response Plan (ERP)** for physical security.
• Protocols for escalating AI-driven security anomalies to internal security teams and law enforcement.
• Policy requirements for data retention, legal access, and forensic use of continuous sensor data.
• Training security personnel on interpreting complex sensor data and utilizing the SIEM system.
• The future of vault security: autonomous surveillance and predictive threat modeling.