Certified Quantum-AI Security Architect (CQASA) Certification Program by Tonex
Where AI meets quantum threat models. The CQASA certification equips professionals with advanced skills to secure AI systems against emerging quantum threats. Learn how to protect AI models using post-quantum cryptography, design quantum-resilient federated learning systems, and validate models over quantum-secure networks. The course blends foundational theory with practical strategies to future-proof AI architectures against quantum-enabled adversaries. Participants gain deep insights into quantum machine learning vulnerabilities, watermarking techniques, and QKD-secured AI inference methods. Ideal for professionals securing next-gen AI ecosystems.
Audience:
- AI security architects
- Quantum computing researchers
- Cybersecurity professionals
- Data scientists and ML engineers
- R&D specialists in AI/quantum domains
- Government and defense tech experts
Learning Objectives:
- Understand post-quantum cryptographic techniques for AI
- Identify vulnerabilities in quantum machine learning
- Design secure AI models using quantum-resistant methods
- Apply QKD for AI inference across quantum networks
- Implement watermarking and validation in quantum ML
Program Modules:
Module 1: Introduction to Quantum-AI Security
- Evolution of AI and quantum computing
- Security challenges at the intersection of AI and quantum
- Quantum threat models for AI systems
- Overview of quantum-resilient architectures
- Role of NIST in post-quantum AI security
- Key standards and compliance requirements
Module 2: Post-Quantum Cryptography in AI
- PQC algorithms and their AI applications
- Lattice-based cryptography and ML model encryption
- Quantum-resistant key exchange methods
- Integration of PQC into AI pipelines
- Risks of classical encryption in quantum contexts
- Performance trade-offs and optimization techniques
Module 3: Quantum-Secure Federated Learning
- Principles of federated learning in AI
- Quantum threats to decentralized training
- Secure aggregation with post-quantum keys
- Enhancing privacy with quantum channels
- Validation and update verification in QFL
- Use cases in healthcare, defense, and finance
Module 4: Quantum Machine Learning Threat Surfaces
- Vulnerabilities in quantum-enhanced ML models
- Adversarial quantum ML attacks
- Data poisoning in quantum model training
- Side-channel risks in quantum hardware
- Quantum AI system lifecycle threats
- Detection and response strategies
Module 5: Quantum Model Watermarking and Validation
- Need for watermarking in AI protection
- Watermarking techniques for quantum ML models
- Robustness against model theft and tampering
- Verifying model origin over quantum channels
- Embedding and decoding resilience techniques
- Legal and compliance aspects of watermarking
Module 6: Secure ML Over Quantum Networks
- Introduction to quantum key distribution (QKD)
- AI inference using QKD-secured links
- Protecting model parameters in transit
- Trust models in quantum communications
- Secure channel design for AI workloads
- Cross-domain QKD AI applications
Exam Domains:
- Fundamentals of Quantum-AI Security
- Post-Quantum Cryptographic Protocols
- Secure AI Architecture Design
- Quantum-AI Risk and Threat Assessment
- Policy, Compliance, and Governance in Quantum-AI
- Trust Models and Secure Communication in Quantum Environments
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Quantum-AI Security. 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 Certified Quantum-AI Security Architect (CQASA).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified Quantum-AI Security Architect (CQASA) Certification Training exam, candidates must achieve a score of 70% or higher.
Secure your future in the quantum-AI frontier. Enroll in the CQASA Certification Program by Tonex today.