Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Artificial Intelligence Security Challenges
- Assessing unique security risks associated with AI architectures
- Distinguishing traditional cybersecurity from AI-specific security requirements
- Defining the attack surfaces inherent in AI models
Adversarial Machine Learning
- Categorizing adversarial attack vectors: evasion, poisoning, and extraction
- Implementing adversarial defenses and robust countermeasures
- Analyzing case studies of adversarial attacks across various sectors
Model Hardening Techniques
- Foundations of model robustness and hardening strategies
- Applying methods to mitigate model vulnerability to targeted attacks
- Applying defensive distillation and alternative hardening methods
Data Security in Machine Learning
- Securing data pipelines for both training and inference phases
- Mitigating data leakage and model inversion threats
- Adopting best practices for managing sensitive data within AI systems
AI Security Compliance and Regulatory Requirements
- Interpreting regulations governing AI and data security
- Ensuring alignment with GDPR, CCPA, and other data protection statutes
- Developing AI models that adhere to secure compliance standards
Monitoring and Maintaining AI System Security
- Establishing continuous monitoring protocols for AI systems
- Conducting logging and auditing for machine learning security
- Executing response plans for AI security incidents and breaches
Future Trends in AI Cybersecurity
- Evaluating emerging techniques in securing AI and machine learning
- Identifying opportunities for innovation in AI cybersecurity for government
- Preparing for future AI security challenges in public sector contexts
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning and artificial intelligence concepts
- Proficiency in cybersecurity principles and standard practices
Audience
- AI and machine learning engineers seeking to enhance security within AI systems
- Cybersecurity professionals specializing in AI model protection
- Compliance and risk management professionals engaged in data governance and security for government
Testimonials (1)
The profesional knolage and the way how he presented it before us
