Video description
Ethical development and responsible deployment of AI and ML systems.
- Learn the latest technology in AI and ML security to safeguard against AI attackers and ensure data integrity and user privacy.
- Navigate privacy and ethical considerations to gain insights into responsible AI practices and address ethical consideration.
- Explore emerging trends and future directions in AI, ML, security, ethics, and privacy focusing on key concepts including threats, vulnerabilities, and attack vectors.
- Recognize and understand the privacy aspects of AI and ML, including data protection, anonymization, and regulatory compliance
Get the essential skills to protect your AI system against cyber attacks. Explore how generative AI and LLMs can be harnessed to secure your projects and organizations against AI cyber threats. Develop secure and ethical systems while being mindful of privacy concerns with real-life examples that we use on a daily-basis with ChatGPT, GitHub Co-pilot, DALL-E, Midjourney, DreamStudio (Stable Diffusion), and others. Gain a solid foundation in AI and ML principles and be better prepared to develop secure and ethical systems while being mindful of privacy concerns. Authors Omar Santos and Dr. Petar Radanliev are industry experts to guide and boost your AI security knowledge.
Related Learning:
- Sign up for live training classes by Omar Santos.
- Read Beyond the Algorithm: AI, Security, Privacy, and Ethics by Omar Santos and Petar Radanliev.
About the Instructors:
Omar Santos is a Distinguished Engineer at Cisco focusing on artificial intelligence (AI) security, cybersecurity research, incident response, and vulnerability disclosure. He is a board member of the OASIS Open standards organization and the founder of OpenEoX. Omar’s collaborative efforts extend to numerous organizations, including the Forum of Incident Response and Security Teams (FIRST) and the Industry Consortium for Advancement of Security on the Internet (ICASI). Omar is the co-chair of the FIRST PSIRT Special Interest Group (SIG). Omar is the lead of the DEF CON Red Team Village and the chair of the Common Security Advisory Framework (CSAF) technical committee. Omar is the author of more than 25 books, 21 video courses, and nore than 50 academic research papers. Omar is a renowned expert in ethical hacking, vulnerability research, incident response, and AI security. His dedication to cybersecurity has made a significant impact on technology standards, businesses, academic institutions, government agencies, and other entities striving to improve their cybersecurity programs. Prior to Cisco, Omar served in the United States Marines focusing on the deployment, testing, and maintenance of Command, Control, Communications, and Computer and Intelligence (C4I) systems.
Dr. Petar Radanliev, Department of Engineering Science, University of Oxford. Dr. Radanliev is a highly accomplished and experienced cybersecurity professional with 10+ years of experience in academic and industry settings. He has expertise in cybersecurity research, risk management, and cyber defense, as well as a track record of excellence in teaching, mentoring, and leading research teams. Technical skills include new and emerging technical cyber/crypto technologies and algorithms, DeFi, blockchain, Metaverse, and quantum cryptography. Petar obtained a PhD at University of Wales in 2014, and continued with postdoctoral research at Imperial College London, University of Cambridge, Massachusetts Institute of Technology, and University of Oxford. His awards include the Fulbright Fellowship in the United States, and the Prince of Wales Innovation Scholarship in the United Kingdom.
Skill Level:
- Intermediate
Course requirement:
- None
Table of contents
- Introduction
- Module 1: Fundamentals of AI and ML
- Lesson 1: Overview of AI and ML Implementations
-
Lesson 2: Generative AI and Large Language Models (LLMs)
- Learning objectives
- 2.1 Introduction to generative AI
- 2.2 Delving into large language models (LLMs)
- 2.3 Exploring examples of AI applications we use on a daily basis
- 2.4 Going beyond ChatGPT, MidJourney, LLaMA
- 2.5 Exploring Hugging Face, LangChain Hub, and other AI model and dataset sharing hubs
- 2.6 Modern AI model training environments
- 2.7 Introducing LangChain, templates, and agents
- 2.8 Fine tuning AI Models using LoRA and QLoRA
- 2.9 Introducing retrieval-augmented generation (RAG)
- Module 2: AI and ML Security
-
Lesson 3: Fundamentals of AI and ML Security
- Learning objectives
- 3.1 Importance of security in AI and ML systems
- 3.2 OWASP top 10 risks for LLM applications
- 3.3 Exploring prompt injection attacks
- 3.4 Surveying data poisoning attacks
- 3.5 Understanding insecure output handling
- 3.6 Discussing insecure plugin design
- 3.7 Understanding excessive agency
- 3.8 Exploring model theft attacks
- 3.9 Understanding overreliance of AI systems
-
Lesson 4: How Attackers Are Using AI to Perform Attacks
- Learning objectives
- 4.1 Exploring the MITRE ATLAS framework
- 4.2 AI supply chain security
- 4.3 Automated vulnerability discovery and creating exploits at scale
- 4.4 Intelligent data harvesting, OSINT, automating phishing, and social engineering attacks
- 4.5 Exploring examples of deepfakes and synthetic media
- 4.6 Dynamic obfuscation of attack vectors
- Lesson 5: AI System and Infrastructure Security
- Module 3: Privacy and Ethical Considerations
-
Lesson 6: Privacy and AI Fundamentals
- Learning objectives
- 6.1 Understanding key privacy considerations in AI implementations
- 6.2 Bias and fairness in AI and ML systems
- 6.3 Transparency and accountability
- 6.4 Understanding differential privacy
- 6.5 Exploring secure multi-party computation (SMPC)
- 6.6 Understanding homomorphic encryption
- 6.7 Understanding the AI data lifecycle management
- 6.8 Delving into federated learning
- Lesson 7: AI Ethics
- Lesson 8: Legal and Regulatory Compliance
- Summary
Product information
- Title: AI Security and Responsible AI Practices
- Author(s):
- Release date: March 2024
- Publisher(s): Pearson
- ISBN: 0138361606
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