AI Chip & Hardware Security Professional (AICHSP) Certification Program by Tonex
AI accelerators now power critical workloads. Their silicon, firmware, and drivers create new attack surfaces. The AI Chip & Hardware Security Professional (AICHSP) Certification Program by Tonex prepares you to secure TPUs, GPUs, NPUs, and neuromorphic devices across design, deployment, and operations.
You will learn threat modeling for silicon, protection of roots of trust, and defenses for side-channels, fault injection, and malicious debugging. We cover secure boot, firmware integrity, attestation, and isolation for multi-tenant accelerators. We link security controls to supply-chain assurance and lifecycle governance. Guidance focuses on controls that are practical for production.
The impact on cybersecurity is direct. Hardened accelerators reduce lateral movement, protect model weights and data in use, and close gaps that bypass traditional endpoint defenses. You will map mitigations to policies and requirements vendors can meet, and design telemetry for detection and response. Case discussions translate complex chip behavior into clear risk decisions. By the end, you can evaluate vendor claims, set guardrails for cloud and edge deployments, and lead incident handling when hardware is targeted. The outcome is resilient AI infrastructure, safer workloads, and measurable risk reduction.
Learning Objectives:
- Model threats across AI silicon, firmware, drivers, and interconnects
- Architect secure boot, roots of trust, and attestation for accelerators
- Implement isolation for multi-tenant and shared-bus environments
- Mitigate side-channel, fault-injection, and debug-port abuse
- Assure supply chain provenance and counterfeit resistance
- Operationalize monitoring, logging, and incident response for AI hardware
- Align controls with policy, contracts, and third-party risk management
- Quantify residual risk and communicate trade-offs to stakeholders
Audience:
- Cybersecurity Professionals
- Hardware and semiconductor security engineers
- Cloud and platform security architects
- SOC and threat detection leaders
- Product security and V&V teams
- OT/Edge and embedded systems engineers
- Procurement, vendor risk, and compliance officers
Course Modules:
Module 1: Foundations of AI Hardware Security
-
- AI accelerator architectures and trust boundaries
- Threat landscape and attacker models
- Roots of trust and secure element concepts
- Side-channel and fault-injection fundamentals
- Debug/test interface risks (e.g., JTAG, SWD)
- Policy and baseline control framework
Module 2: Silicon, Microcode, and Boot Protections
-
- Secure boot chains and rollback prevention
- Microcode/firmware signing and update controls
- Anti-tamper, sensors, and zeroization strategies
- Hardware Trojans and detection approaches
- PUFs and device identity considerations
- Verification, validation, and assurance artifacts
Module 3: Runtime Isolation and Memory Safety
-
- DMA/IOMMU strategies and bus hardening
- Process, context, and partition isolation
- Scheduler and resource-sharing risks
- Leakage paths for model weights and keys
- Driver hardening and kernel attack surface
- Telemetry for anomalous runtime behavior
Module 4: Supply Chain and Lifecycle Assurance
-
- Provenance, traceability, and counterfeit detection
- Secure provisioning and key injection controls
- Chain of custody from fab to field
- Third-party IP blocks and licensing risks
- SBOM and firmware bill of materials (FBOM)
- Decommissioning, RMA, and sanitization
Module 5: Edge and Embedded AI Security
-
- Constraints in power, timing, and reliability
- Physical access, probing, and EM/thermal channels
- Secure update and remote attestation at the edge
- Safety-critical co-design with security
- Resilience for intermittent connectivity
- Hardening storage for models and secrets
Module 6: Governance, Detection, and Response
-
- Policies, contracts, and vendor due diligence
- Control baselines for cloud and on-prem accelerators
- Logging, metrics, and health signals to collect
- Playbooks for hardware-centric incidents
- Forensics considerations for accelerators
- KPIs, KRIs, and continuous improvement
Exam Domains:
- Silicon Threat Modeling & Attack Surfaces
- Cryptographic Roots of Trust & Key Management
- Firmware Integrity, Update Security & Recovery
- Supply Chain Provenance & Anti-Counterfeit Assurance
- Runtime Isolation, Memory Protection & Telemetry
- Incident Response & Forensics for AI Accelerators
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of AI Chip & Hardware Security Professional (AICHSP) Certification Program by Tonex. Participants will have access to online resources, including readings, case studies, and tools for practical 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 AI Chip & Hardware Security Professional (AICHSP) Certification Program by Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the AI Chip & Hardware Security Professional (AICHSP) Certification Training exam, candidates must achieve a score of 70% or higher.
Secure the silicon that powers your AI. Elevate your hardware security leadership with AICHSP. Enroll now and turn complex chip risks into clear, defensible controls.