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Document1.txt
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Introduction to AI and ML Security:
● Overview of AI and ML technologies
● Importance of security in AI and ML systems
● Distinction between traditional software security and AI/ML security
Threat Landscape:
● Common security threats and attacks targeting AI and ML systems ● Adversarial attacks: poisoning, evasion, data integrity attacks
● Privacy risks: data leakage, membership inference attacks
● Model stealing and intellectual property theft
Adversarial Machine Learning:
● Understanding adversarial examples
● Techniques for crafting adversarial attacks
● Defense mechanisms against adversarial attacks
Privacy-Preserving AI and ML:
● Privacy concerns in AI/ML systems
● Differential privacy and its application to ML
● Federated learning and secure multi-party computation for preserving
privacy Model Security and Robustness:
● Model security best practices
● Model explainability and interpretability
● Techniques for improving model robustness and reliability
Secure Model Deployment:
● Secure model deployment architectures
● Containerization and isolation for model serving
● Authentication and access control in AI/ML systems
Data Security and Governance:
● Data privacy and compliance considerations
● Secure data storage and transmission
● Data governance frameworks for AI/ML projects
Ethical Considerations:
● Ethical implications of AI and ML security ● Bias and fairness in AI/ML systems
● Responsible AI principles and guidelines
Introduction to Secure Development:
● Overview of software security and its importance
● Introduction to secure development lifecycle (SDLC)
● Understanding the role of Global Product Security (GPS)
Threat Modeling:
● Introduction to threat modeling
● Identifying assets, threats, and vulnerabilities
● Techniques for conducting threat modeling exercises
Secure Coding Practices:
● Principles of secure coding
● Common vulnerabilities in code (e.g., buffer overflows, injection
attacks)
● Secure coding guidelines for different programming languages (e.g.,
Java, Python, C/C++)
Authentication and Authorization:
● Understanding authentication and authorization mechanisms
● Secure implementation of authentication (e.g., password hashing,
multi-factor authentication)
● Role-based access control (RBAC) and least privilege principles
Input Validation and Output Encoding:
● Importance of input validation and output encoding
● Techniques for sanitizing and validating input data
● Preventing common injection attacks (e.g., SQL injection, XSS)
Secure Communication:
● Securing network communication (e.g., HTTPS/TLS) ● Implementing secure APIs and web services
● Transport layer security best practices
Data Protection:
● Encryption fundamentals
● Protecting sensitive data at rest and in transit
● Key management and secure storage practices
Secure Configuration Management:
● Secure configuration of servers, databases, and applications ● Hardening operating systems and network devices
● Configuration management tools and best practices
Secure Development Tools and Techniques:
● Introduction to security testing tools (e.g., static analysis, dynamic
analysis)
● Code review best practices
● Automated security testing and continuous integration (CI) pipelines
Secure Deployment and Operations:
● Secure deployment strategies (e.g., container security, serverless
security)
● Monitoring and logging for security incidents
● Incident response and handling security breaches
Security Awareness and Training:
● Importance of security awareness for developers
● Techniques for promoting a security culture within development teams
● Continuous learning and staying updated on security trends