Certified AI Hardware Security Specialist (CAIHSS) Certification Program by Tonex
Certified AI Hardware Security Specialist equips professionals to protect AI compute infrastructure, accelerators, and edge devices across the lifecycle. You will master secure AI hardware design, chip to model trust chains, and GPU TPU security patterns while aligning with emerging standards and supply chain realities.
The program highlights cybersecurity risks unique to AI silicon and memory architectures and shows how to reduce attack surface with hardware anchored controls. You will translate cybersecurity principles into practical safeguards for inference and training at scale, building confidence for regulated and mission critical deployments.
Learning Objectives
- Design secure boot and root of trust for AI hardware
- Implement chip to model integrity, attestation, and key management
- Harden GPUs TPUs and NPUs for training and inference workloads
- Apply threat modeling to accelerators firmware drivers and interconnects
- Map protections to compliance and industry standards
- Quantify cybersecurity impact on AI compute risk and resilience
Who Should Attend
- Cybersecurity Professionals
- Hardware and SoC Engineers
- AI Platform and MLOps Engineers
- Embedded Systems Architects
- Cloud and Data Center Security Engineers
- Compliance and Risk Managers
- Product Managers for AI Devices
Course Modules
Module 1: Secure Hardware Foundations
- Threat landscape for AI accelerators
- Root of trust essentials
- Secure boot sequencing
- Memory and DMA protections
- Cryptographic primitives in silicon
- Safety vs security tradeoffs
Module 2: Chip-to-Model Trust
- Supply chain authenticity
- Firmware signing and updates
- Measured boot and attestation
- Key provisioning and HSM use
- Model integrity verification
- Confidential deployment patterns
Module 3: GPU TPU Hardening
- Multi tenant isolation basics
- Driver and runtime attack paths
- VRAM leakage mitigation
- Side channel risk reduction
- Scheduler and MPS controls
- Secure telemetry and logging
Module 4: Edge AI Protection
- Secure elements and TPM choices
- Offline update hardening
- Physical tamper resistance
- Secure enclave inference
- Connectivity and OTA security
- Zero trust for edge fleets
Module 5: Data Center Safeguards
- Rack level trust boundaries
- PCIe CXL and fabric security
- Confidential computing overlays
- Secret management for jobs
- Air gap and restricted zones
- Incident response playbooks
Module 6: Governance and Assurance
- Policy for AI hardware lifecycle
- Compliance mapping overview
- Risk quantification methods
- Testing and validation strategy
- Third party assurance models
- Continuous improvement metrics
Exam Domains
- AI Hardware Threats and Risks
- Trust Anchors and Attestation
- Accelerator Platform Hardening
- Edge Device Security Controls
- Data Center and Fabric Protection
- Governance Compliance and Assurance
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, case-driven walkthroughs, and project-based learning, facilitated by experts in the field of Certified AI Hardware Security Specialist CAIHSS. Participants will have access to online resources, including readings, case studies, and tools for structured exercises.
Assessment and Certification
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Hardware Security Specialist CAIHSS.
Question Types
- Multiple Choice Questions MCQs
- Scenario-based Questions
Passing Criteria
To pass the Certified AI Hardware Security Specialist CAIHSS Certification Training exam, candidates must achieve a score of 70% or higher.
Secure your AI compute stack end to end. Enroll in the CAIHSS by Tonex today and become the trusted specialist your organization depends on.