📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Quantum Safe AI in Cybersecurity, from foundations to a certified capstone project.
Outline
Understand quantum threats and post‑quantum algorithms • Analyze security properties of lattice‑based schemes • Implement basic quantum‑resistant primitives in Python
Outline
Build and evaluate machine‑learning models for anomaly detection • Apply feature engineering to security telemetry • Deploy models using containerized pipelines
Outline
Encrypt AI model weights with post‑quantum cryptography • Secure inference pipelines against quantum attacks • Validate end‑to‑end confidentiality and integrity
Outline
Design threat models that include quantum adversaries • Craft adversarial examples against quantum‑safe AI systems • Mitigate attacks using robust training techniques
Outline
Ingest real network traffic datasets • Train an AI‑based intrusion detection model • Secure the model with lattice‑based encryption and evaluate performance
Outline
Map quantum‑safe AI practices to NIST and ISO standards • Explore emerging research and industry roadmaps • Prepare a strategic implementation plan for your organization
Earn government-registered certification in Quantum Safe AI in Cybersecurity
e-Certificate and e-Marksheet issued on successful completion.
