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AI Threats and Defenses

Course Summary

This course provides foundational knowledge and skills necessary to secure AI systems. Participants will explore the importance of AI security, key concepts in AI and cybersecurity, and practical approaches for threat modeling, defending against adversarial attacks, and securing AI models and data. Through theoretical knowledge, hands-on exercises, and real-world case studies, this course equips participants with the skills needed to identify, analyze, and mitigate security risks in AI systems.

Prerequisites
In order to succeed in this course, you will need:

  • Familiarity with fundamental AI concepts.
  • Experience with a programming language like Python or R.
  • Awareness of basic cybersecurity principles.
  • Practical experience in data analysis.

Purpose
Learn the skills needed to identify, analyze, and mitigate security risks in AI systems.
Audience
Engineers and Developers involved with the design and implementation of AI/GenAI applications and systems.
Role
Software Engineers | Developers | Data Engineers | Technical Managers
Skill level
Beginner
Style
Lecture | Hands-on Activities| Case Studies
Duration
3 days
Related technologies
Artificial Intelligence | GenAI |Python | R

 

Course objectives
  • Describe foundational AI concepts, including machine learning, deep learning, and neural networks.
  • Discuss the importance of AI security and identify common security challenges in AI systems.
  • Apply cybersecurity principles to AI, including threat modeling and defense mechanisms against adversarial attacks.
  • Apply best practices for securing AI models and data, including encryption, access control, and secure deployment.
  • Discuss strategies for securing generative AI systems, including RAG and chat agents, with role-based access control.

What you'll learn:

In this AI Threats and Defenses course, you'll learn:

Foundational Overview and Introduction to AI Security

  • Introduction
  • Importance of AI security
    • Impact on business operations and reputation
    • Regulatory and compliance requirements
  • AI Overview
    • Definition and scope of AI
      • Historical development of AI
      • Key differentiators between AI and traditional software
  • Key concepts
    • Machine learning, deep learning, neural networks
    • Supervised vs. unsupervised learning
    • Introduction to neural network architectures
  • Applications of AI in various industries

Foundational Concepts for AI Security

  • Overview of AI system components
    • Data, models, algorithms
    • Data collection and preprocessing
    • Model training and evaluation
  • Data pipelines and model lifecycle
    • Understanding the end-to-end process
    • Data ingestion
    • Model deployment and monitoring
  • Introduction to cybersecurity principles
    • Confidentiality, integrity, and availability
    • Common cybersecurity threats

Introduction to AI Security

  • Key security principles in AI
    • Confidentiality, integrity, and availability
    • Data integrity checks
    • Secure coding practices for AI models
  • Common AI security challenges
    • Bias, data poisoning, model theft
    • Techniques for identifying and mitigating bias
    • Prevention strategies for data poisoning
  • Importance of AI security in the real world
    • High-profile AI security incidents
    • Lessons learned from industry

Threat Modeling in AI

  • Understanding threat modeling
  • Techniques and methodologies for threat modeling
    • STRIDE framework
    • DREAD model
  • Identifying potential threats and vulnerabilities
    • Techniques and methodologies
    • Attack vectors in AI systems
    • Vulnerability assessment tools
  • Creating threat models
    • Building a threat model for an AI application
    • Documenting and reviewing threat models

Adversarial Attacks, Defense Mechanisms, and Best Practices

  • Adversarial Attacks on AI Systems
    • Types of adversarial attacks
      • Attack techniques: FGSM, PGD
      • Adversarial attacks in image classification
  • Real-world examples of adversarial attacks
    • High-profile case studies
    • NotPetya attack
    • Tesla autopilot adversarial examples
  • Analyzing the impact of adversarial examples
    • Effects on model performance
    • Performance degradation metrics
    • Methods for evaluating robustness
  • Discussing mitigation strategies
    • Preventative and responsive measures
    • Adversarial training
    • Defensive distillation
  • Defense Mechanisms Against Adversarial Attacks
    • Overview of defense techniques
      • Broad strategies for protection
      • Input preprocessing
      • Model hardening
  • Robust training methods
    • Adversarial training: Training models with adversarial examples
    • Defensive distillation: Techniques to make models less sensitive to adversarial examples
  • Detection and mitigation strategies
    • Anomaly detection: Identifying unusual patterns that indicate an attack
    • Input preprocessing: Techniques to sanitize inputs before they reach the model
  • Implementing Defense Mechanisms
    • Applying defense techniques to secure AI models
      • Practical implementation steps
      • Incorporating robustness into the training pipeline
      • Deploying defensive models in production
    • Evaluating the effectiveness of defenses
      • Methods to test and validate security measures
      • Adversarial robustness benchmarks
      • Performance metrics for secure models
  • Securing AI Models and Data
    • Best practices for model security
      • Keeping models and data secure over time
      • Scheduled security audits
      • Patch management
    • Encryption and access control
      • Protecting data and models from unauthorized access
      • Data encryption techniques
      • Role-based access control
  • Data Privacy and Protection in AI
    • Anonymization techniques
      • Protecting individual identities in datasets
      • Data masking
      • Differential privacy
    • Secure data handling practices
      • Ensuring data integrity and confidentiality
      • Secure data storage
      • Secure data transmission protocols
  • Secure Model Deployment and Monitoring
    • Secure deployment practices
      • Techniques to safely deploy AI models
      • Secure coding practices
      • Deployment checklists
    • Monitoring and logging
      • Keeping track of model performance and detecting anomalies
      • Real-time monitoring tools
      • Incident response strategies

GenAI and AI Security

  • Securing Retrieval-Augmented Generation (RAG) and Chat Agents
    • Understanding RAG and its applications
      • How RAG enhances search capabilities
      • Practical applications in various domains
    • Role-based access control for chat agents
      • Implementing RBAC in chat systems
  • Ensuring secure data access for chat agents
    • Data access policies
    • Monitoring and auditing access

Security for GenAI Systems

  • Threat modeling for GenAI
    • Identifying threats specific to generative models
    • Mitigation strategies for GenAI threats
  • Defense mechanisms for generative models
    • Robust training for GenAI
    • Techniques for detecting and mitigating attacks
  • Best practices for deploying GenAI securely
    • Secure deployment frameworks
    • Monitoring and maintenance best practices

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