Certified GenAI and LLM Cybersecurity Professional (CGLCP) for Professionals
The Certified GenAI and LLM Cybersecurity Professional (CGLCP) for Professionals program is tailored for technical experts tasked with securing Generative AI models and LLMs. Participants will gain hands-on experience in adversarial attacks, model extraction, inversion, poisoning, and the security frameworks needed to defend AI models. The program emphasizes practical knowledge for implementing security measures in real-world AI applications.
Learning Objectives:
By the end of this program, participants will be able to:
- Identify and exploit common vulnerabilities in Generative AI and LLM models through adversarial attacks.
- Implement defenses against model extraction, inversion, and evasion attacks.
- Assess and mitigate risks related to privacy leaks, data poisoning, and ethical biases in AI models.
- Utilize tools and frameworks to evaluate AI model security and improve robustness.
- Develop and maintain secure AI systems, ensuring model integrity and compliance with security standards.
Target Audience:
- AI engineers and developers.
- Cybersecurity professionals working with AI systems.
- Data scientists responsible for AI model development and deployment.
- Technical leads in AI and machine learning.
- Security architects focusing on AI security.
Program Outline:
1. Fundamentals of AI Security
- Introduction to GenAI and LLM architectures (e.g., GPT, BERT).
- Overview of adversarial machine learning and threat modeling for AI systems.
- Exploring the AI security landscape: attack vectors and defenses.
2. Evasion and Adversarial Attacks
- Crafting adversarial inputs to deceive models.
- Defending against prompt injection and adversarial examples.
- Real-world examples of evasion attacks in LLMs.
3. Model Extraction and Privacy Attacks
- Reverse engineering LLMs through query-based extraction.
- Mitigating risks of model extraction and membership inference.
- Techniques to safeguard privacy in AI models (e.g., differential privacy, federated learning).
4. Model Inversion and Data Reconstruction
- How model inversion attacks work and their implications for privacy.
- Implementing defenses against inversion and sensitive data leakage.
5. Data Poisoning and Model Integrity
- Understanding poisoning attacks in LLMs and their impact on model reliability.
- Techniques to detect and prevent data poisoning and backdoor attacks in training pipelines.
6. LLM-Specific Security Challenges
- Securing large language models from prompt-based exploitation.
- Bias detection and mitigation in LLMs.
- Developing and deploying AI models with integrated security.
7. AI Security Tools and Frameworks
- Hands-on training with adversarial tools and frameworks (e.g., ART, CleverHans).
- Using security benchmarks and audit frameworks to assess AI model robustness.
- Implementing real-time monitoring and security measures for deployed AI systems.
Capstone Project:
- Conduct a security assessment of a provided AI model (e.g., LLM), identifying vulnerabilities and suggesting technical countermeasures.
Certification Exam:
- A 2-hour practical exam with scenarios focusing on adversarial attacks, model defenses, and hands-on AI security assessments.
Exam Domains:
- AI Security Fundamentals (15%): Basic understanding of AI and LLM vulnerabilities and threat models.
- Adversarial Attacks and Defenses (25%): Practical knowledge of evasion, extraction, inversion, and poisoning attacks.
- Privacy and Data Protection (20%): Focus on protecting AI models from privacy leaks, inversion attacks, and safeguarding sensitive data.
- LLM-Specific Security Challenges (20%): Tests knowledge of securing large language models, addressing prompt injection, and bias mitigation.
- Security Tools and Frameworks (20%): Evaluates the use of tools like ART, CleverHans, and others for AI security testing and implementation.
Exam Details:
- Number of Questions: 75 questions.
- Type of Questions: Multiple-choice, hands-on scenarios, and technical problem-solving questions.
- Duration: 2 hours.
- Passing Score: 75%.