| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 8 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python PyTorch TensorFlow Qiskit Microsoft SEAL OpenFHE Docker Kubernetes |
About the Quantum Safe AI in Cybersecurity Course
In an era where quantum computing threatens traditional cryptographic safeguards, this program teaches you how to fuse AI with quantum‑safe techniques to protect critical assets.
You will explore post‑quantum algorithms, secure AI model deployment, and real‑world defense strategies against emerging quantum threats.
Program Highlights
• Comprehensive coverage of Quantum Safe AI in Cybersecurity from fundamentals to advanced applications
• Hands-on projects and real-world case studies in cybersecurity
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Python, PyTorch, TensorFlow, Qiskit
• Career-oriented training for academic and professional growth in cybersecurity
Course Curriculum
Module 1: Foundations of Quantum‑Safe Cryptography
- Understand quantum threats and post‑quantum algorithms
- Analyze security properties of lattice‑based schemes
- Implement basic quantum‑resistant primitives in Python
Module 2: AI Fundamentals for Security Engineers
- Build and evaluate machine‑learning models for anomaly detection
- Apply feature engineering to security telemetry
- Deploy models using containerized pipelines
Module 3: Integrating Quantum‑Safe Algorithms with AI Models
- Encrypt AI model weights with post‑quantum cryptography
- Secure inference pipelines against quantum attacks
- Validate end‑to‑end confidentiality and integrity
Module 4: Threat Modeling & Adversarial AI in a Quantum Context
- Design threat models that include quantum adversaries
- Craft adversarial examples against quantum‑safe AI systems
- Mitigate attacks using robust training techniques
Module 5: Hands‑On Lab: Building a Quantum‑Safe AI‑Powered IDS
- Ingest real network traffic datasets
- Train an AI‑based intrusion detection model
- Secure the model with lattice‑based encryption and evaluate performance
Module 6: Compliance, Standards & Future Outlook
- Map quantum‑safe AI practices to NIST and ISO standards
- Explore emerging research and industry roadmaps
- Prepare a strategic implementation plan for your organization
Tools, Techniques, or Platforms Covered
Python PyTorch TensorFlow Qiskit Microsoft SEAL OpenFHE Docker Kubernetes
Real-World Applications
- Apply Quantum Safe AI in Cybersecurity skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical cybersecurity competencies
- Solve industry-relevant problems using Quantum Safe AI in Cybersecurity methodologies and tools
- Contribute to open-source projects and collaborative research in cybersecurity
- Prepare for competitive examinations, interviews, and professional certifications in cybersecurity
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
