Genai And Cybersecurity – Frameworks And Best Practices 2024

Build and Secure Enterprise GenAI, Tools, Frameworks and Production Case Studies from Leaders

What you'll learn

Master the foundational principles and best practices for integrating Generative AI in cybersecurity.

Become aware about AI, ML, and deep learning, focusing on their applications in various industries, including a case study on Tesla Autopilot.

Study the intersection of AI/ML and cybersecurity, understanding ethical considerations and potential risks with examples from real-world scenarios.

Explore the latest trends as per industry reports like those from Gartner.

Delve into typical cloud-based and AI-specific cybersecurity architectures, learning how they differ and why they're essential.

Develop strategies for managing AI data privacy, including data quality, governance, and lifecycle management.

Learn about AI risk management frameworks like NIST AI RMF, and explore case studies on navigating AI risks.

Understand key AI controls and policies, including the CIA Triad, OWASP AI vulnerabilities, and AI governance frameworks.

Gain knowledge about auditing AI systems, understanding components of compliance, and readiness comparisons.

Explore various AI regulatory frameworks, including the EU AI Act, GDPR, and ethical AI frameworks by OECD.

Understand the security implications of Generative AI, exploring defenses, future challenges, and opportunities.

Learn about innovative GenAI solutions and opportunities, including custom LLM implementations and industry-specific applications.

Understand how AI can be used to enhance governance practices and develop frameworks for low-risk AI adoption.

Study key controversies and ethical issues in AI, as outlined by UNESCO and other bodies, to inform responsible AI practices.

Requirements

Basic understanding of AI/ML tech is required

A keen interest in staying updated on the latest developments in the world of AI

Description

Welcome to the Future! Welcome to the <Course Name>The rapid advancements in Generative AI are transforming industries, and there's never been a better time to upskill and update yourself. From creating art to automating code, AI is now a crucial part of our everyday lives, also making it very essential for businesses and individuals alike to understand its potential. Tech companies are harnessing AI to streamline operations, boost productivity, and drive innovation with AI assistants that continuously learn and improve.AI tools are streamlining processes, enhancing productivity, and fostering innovations once thought impossible.But here’s the best part: This technology isn’t just for the big players anymore.Small businesses and startups now have access to AI tools that level the playing field.As we navigate this exciting new landscape, keeping up with the latest AI developments isn’t just a nice-to-have; IT’S A MUST!Understanding the latest developments in Generative AI can open up a world of opportunities:Revolutionize Industries: Discover how AI is being used in groundbreaking applications, from Tesla's autopilot system and its sophisticated anomaly detection to the latest in computer vision technology.Enhance Problem-Solving: Learn how AI can tackle complex problems with unprecedented efficiency and accuracy, providing innovative solutions that were once unimaginable.Future-Proof Your Skills: With AI continuously evolving, staying updated on the latest trends and technologies ensures you remain relevant and competitive in your field.Comprehensive Learning: Our course covers everything from foundational concepts in AI, machine learning, and deep learning to practical use cases and best practices in cybersecurity.By the end of this course, you will have learned the following:Generative AI and Cybersecurity: Frameworks and Best PracticesUnderstand the fundamental concepts of AI, ML, DL, and GenAI.Explore key use cases and applications in cybersecurity.Analyze case studies like Tesla Autopilot for anomaly detection and common sense challenges.Cybersecurity in AI/ML and AI EthicsGrasp the importance of cybersecurity in AI/ML.Evaluate AI tech risks at different stages with practical examples.Study emerging technology trends and their impact on cybersecurity.Solution & Infrastructure SecurityExamine typical cloud-based cybersecurity architectures.Compare AI-specific cybersecurity solutions and their unique challenges.Discuss why cybersecurity needs to be redefined for AI and the role of AI regulation.AI Data & Privacy StrategyUnderstand data breaches and develop a robust AI data strategy.Learn about data intelligence modeling and data quality management.Explore data lifecycle management, data ethics, security, and governance.AI Privacy StrategyNavigate the AI privacy paradox and associated factors and concerns.Assess privacy concerns related to data, identity, sensitivity, and surveillance.Review laws, policies, and tools designed to protect AI privacy.AI Risk Management & Threat ManagementInvestigate AI risks and threats through detailed case studies.Implement AI frameworks like NIST AI RMF for effective risk management.Examine emerging AI threat landscapes and future risk management trends.AI Frameworks & PoliciesDive into NIST AI RMF core, roadmap, playbook, and taxonomy.Explore early adoption of AI frameworks and policies with relevant use cases.Review cybersecurity references, AI frameworks, and governance policies.AI ControlsUnderstand AI controls and the CIA Triad.Redefine BIA and ISBIA evaluations.Learn about OWASP and MLSecOps top vulnerabilities.AI Audit & ComplianceRecognize the need for auditing AI systems and their components.Compare audit and compliance readiness for AI systems.AI Laws & RegulationsAnalyze the EU's AI framework and risk-based approach to AI regulation.Study the Ethical AI Framework by OECD, EU AI Act, and GDPR AI.Understand the implications of the UK Data Protection and Digital Information Bill.Generative AI and LLM SecurityIntroduce generative AI risk implications, biases, and defenses.Explore the future of secure AI and emerging challenges and opportunities.Generative AI Case StudiesAssess risks and opportunities in LLM systems.Examine enterprise privacy at OpenAI and LLM security tools.Learn from case studies on LLM adoption and cybersecurity strategies.Solutions and OpportunitiesDiscover the latest tools and platforms like Raga LLM Hub and Giscard.Study practical case studies in retail, customer service, and healthcare.Explore best practices and frameworks for low-risk AI adoption.You'll have lifetime access to:Comprehensive video lessonsDetailed case studiesUp-to-date industry insightsPractical projects and exercisesHow will our course benefit YOU?Here’s how our course can benefit you:Career Advancement: AI skills are in high demand across various industries. Mastering Generative AI can propel your career, opening doors to new opportunities in tech, marketing, cybersecurity, and more.Innovative Solutions: Equip yourself with the knowledge to create cutting-edge solutions like chatbots, personalized customer experiences, and enhanced data security measures.Efficiency and Productivity: Learn how to implement AI tools to streamline processes, boost productivity, and foster innovation within your organization.Ethical and Secure AI: Gain insights into managing AI ethics, handling model biases, and ensuring robust cybersecurity practices to mitigate risks associated with AI adoption.Hands-On Experience: Our course combines theory with practical exercises, ensuring you gain real-world skills. From building AI models to implementing secure AI architectures, you'll have the expertise to tackle real challenges.What makes this course unique!Expert Trainers: Learn from industry veterans with years of experience in Generative AI and cybersecurity, providing you with deep insights and practical knowledge.Real-World Case Studies: Gain a thorough understanding of AI applications with real-world case studies, showcasing how Generative AI is transforming various industries.Latest Industry Updates: Stay current with the most recent advancements and trends in the AI industry, ensuring that you are equipped with up-to-date knowledge and insights relevant to today’s dynamic tech landscape.Actionable Skills: Acquire skills that are relevant and directly applicable to your career, enabling you to drive innovation and efficiency in your workplace.Who Will This Course Benefit?This course on Generative AI and Cybersecurity equips professionals of all industries. It offers a deep dive into the latest developments in AI, providing valuable insights for enhancing product development and customer experiences through advanced AI solutions.Professionals will benefit from learning about AI and cybersecurity, including ethical practices, risk management, and security measures essential for maintaining AI system integrity. This focus is crucial for those managing AI risks and ensuring robust security protocols. The course also covers AI frameworks and policies, such as the NIST AI RMF, helping professionals align their AI implementations with industry standards and regulatory requirements.Data security and privacy strategies are another key area, addressing data breaches, governance, and privacy. This is vital for managing and securing data, ensuring its quality, and protecting sensitive information in an AI context. The course also explores large language models (LLMs), offering practical insights into their deployment.Through real-world case studies, such as Tesla’s autopilot system and AI applications in various other sectors, the course provides practical examples of AI technology in action. By mastering these topics, professionals will be well-equipped to leverage AI for innovation, improve security measures, and uphold ethical practices, thereby enhancing their career prospects and driving advancements in their fields.Instructor spotlight (To be added by the Instructor)We'll see you inside!Enroll today and unlock your full potential!All the best and see you inside the course.

Overview

Section 1: AI / ML / GenAI - Overview and Examples

Lecture 1 GenAI and Cybersecurity – Frameworks and Best Practices

Lecture 2 AI in 5 mins

Lecture 3 ML vs Deep Learning vs GenAI vs Human Intelligence

Lecture 4 AI Automation Levels

Lecture 5 ML / DL / GenAI

Lecture 6 AI Use Case

Lecture 7 Computer Vision Applications

Lecture 8 Tesla Autopilot

Lecture 9 Test Autopilot Updated

Lecture 10 Tesla Autopilot - Anamoly

Lecture 11 Detection vs Common Sense

Lecture 12 GenAI Adoption Trends Analysis

Section 2: Cybersecurity in AI ML

Lecture 13 Cybersecurity in AI ML and AI Ethics (2 mins)

Lecture 14 AI Risk Sample

Lecture 15 AI Tech Risks at Different Stages

Lecture 16 Ethical AI

Lecture 17 Why AI and Cyber Security?

Lecture 18 Emerging Technology Trends - Gartner Theme1

Lecture 19 Emerging Technology Trends - Gartner Theme2

Lecture 20 Emerging Technology Trends - Gartner Theme3

Lecture 21 Emerging Technology Trends - Gartner Theme4

Section 3: GenAI

Lecture 22 Introduction

Lecture 23 GenAI – Foundational LLM Models

Lecture 24 AI enables a new platform: Intelligence-as-a-service

Lecture 25 ChatGPT

Lecture 26 Building Custom LLM

Lecture 27 The Top 100 Gen AI Consumer Apps

Lecture 28 What Is the Cognitive Transformation Era?

Lecture 29 Evolution of Marketing

Lecture 30 Case Study – Amazon / Swiggy

Lecture 31 Case Study – LLM Implementation - Failure Lessons

Lecture 32 Metrics in Healthcare Domain

Lecture 33 Current LLMs in medicine

Section 4: Solution & Infrastructure Security

Lecture 34 Introduction

Lecture 35 Typical Cloud based Cyber Security Architecture

Lecture 36 Typical Cyber Security Architecture Updated

Lecture 37 Typical AI Cyber Security Architecture Updated

Lecture 38 Typical AI Cyber Security Architecture

Lecture 39 Typical Cloud based Cyber Security Architecture - Azure Updated

Lecture 40 How are AI solutions and Infrastructure different

Lecture 41 Key AI controversies as listed by UNESCO

Lecture 42 Key AI controversies as listed by UNESCO - Continued

Lecture 43 Why cyber security needs to be redefined for AI?

Lecture 44 Additional Areas of Focus for Solution/Infrastructure Security

Lecture 45 Trust AI Regulation?

Section 5: GenAI Model Security and Challenges

Lecture 46 Model Security

Lecture 47 GenAI Model Security Challenges

Lecture 48 Security in GenAI

Lecture 49 LLMSecOps

Section 6: AI Controls

Lecture 50 Introduction

Lecture 51 AI Controls - CIA Triad

Lecture 52 Redefining BIA and ISBIA Evaluations

Lecture 53 AI Controls Part 1

Lecture 54 AI Controls Part 2

Lecture 55 AI Controls Part 3

Lecture 56 OWASP / OWASP AI / MLSecOps - Top 10 Vulnerabilities

Section 7: AI Data & Privacy Strategy

Lecture 57 Introduction to AI Data & Privacy Strategy

Lecture 58 Data Breaches

Lecture 59 AI Data Strategy

Lecture 60 Data Intelligence Modeling

Lecture 61 Data Intelligence Modeling - Part 2

Lecture 62 Data Quality Updated

Lecture 63 Data Quality

Lecture 64 Data Quality - Part 2

Lecture 65 Data Ethics

Lecture 66 Data Security

Lecture 67 Data Governance

Lecture 68 Data Privacy

Section 8: AI Privacy Strategy

Lecture 69 Introduction

Lecture 70 The AI Privacy Paradox

Lecture 71 AI Privacy - Factors & Concerns

Lecture 72 AI Privacy - Factors & Concerns - Laws, Policies, Tools

Lecture 73 AI Privacy - Factors & Concerns - Data

Lecture 74 AI Privacy - Factors & Concerns - Identity

Lecture 75 AI Privacy - Factors & Concerns - Sensitivity

Lecture 76 AI Privacy - Factors & Concerns - Surveillance - V1

Lecture 77 AI Privacy - Factors & Concerns - Laws, Policies, Tools

Section 9: AI Risk Management & Threat Management

Lecture 78 Introduction

Lecture 79 Case Studies: Navigating AI Risks

Lecture 80 Emerging AI Threat Landscape

Lecture 81 Emerging Trends in Risk Management and Innovations

Lecture 82 AI Risk & Threat Management

Lecture 83 AI Frameworks - NIST AI RMF

Lecture 84 Redefining Risk Management for AI

Lecture 85 Integrating AI Risk Management

Lecture 86 AI Actors across Lifecycle Stages

Lecture 87 AI Trustworthiness & Risk

Lecture 88 AI Risk & Threat Management Updated

Lecture 89 The Future of AI Risk Management

Section 10: AI Frameworks & Policies

Lecture 90 Introduction

Lecture 91 NIST AI RMF Core, Roadmap, Playbook Updated

Lecture 92 NIST AI RMF Core, Roadmap, Playbook

Lecture 93 NIST AI RMF Taxonomy Updated

Lecture 94 Early Adoption of AI Frameworks and Policies

Lecture 95 NIST Early Adoption - Use Cases

Lecture 96 Cyber Security - References, AI Frameworks, Governance Policies

Section 11: AI Audit & Compliance

Lecture 97 Introduction

Lecture 98 Need for Auditing AI Systems

Lecture 99 Components of Auditing AI systems

Lecture 100 Audit & Compliance - Comparision & Readiness

Section 12: AI Laws & Regulations

Lecture 101 Introduction

Lecture 102 EU's AI framework - Risk-Based Approach to AI Regulation

Lecture 103 Ethical AI Framework by OECD, EU AI Act, GDPR AI

Lecture 104 EU AI Act

Lecture 105 GDPR's Coverage of Artificial Intelligence

Lecture 106 UK Data Protection and Digital Information Bill

Section 13: GenAI & LLM Security

Lecture 107 Intro to the section

Lecture 108 Generative AI Risk Implications

Lecture 109 BIAS in Distribution

Lecture 110 GenAI Attacks & Defenses

Lecture 111 The Sources of Threats for LLM Agents

Lecture 112 Agents vs Responsible AI Adoption

Lecture 113 GenAI Emerging Defenses

Lecture 114 The Future of Secure AI: Challenges and Opportunities

Section 14: GenAI Models, Risks, Adoption Strategies and Recommendations

Lecture 115 Introduction

Lecture 116 Advancements of the attack methods, defense mechanisms in LLMs, their limitation

Lecture 117 Taxonomy for the risks of LLM systems

Lecture 118 LLM Security

Lecture 119 LLM Vulnerabilities

Lecture 120 Assessment of LLM opportunities, risks and EU AI Act compliance

Lecture 121 LLM Models and Compliance

Lecture 122 Enterprise privacy at OpenAI

Lecture 123 LLM Security Tools & Google - Security Command Center protection - VertexAI

Lecture 124 LLM Risk / Use Cases / Mitigation

Lecture 125 Risk Analysis of RAG, Prompts, and Agents

Lecture 126 Generative AI Red Teaming Challenge

Lecture 127 LegalBench vs CorpFin

Lecture 128 LLM Adoption Strategy

Lecture 129 LLM Cyber Security Strategy

Lecture 130 Leveraging Read Teaming for AI Governance

Section 15: GenAI Case Studies - Production Lessons

Lecture 131 Vision Adoption Analysis

Lecture 132 GenAI Model Accuracy / Consistency Challenges - Experiments

Lecture 133 Celebrating a Year of GenAI Use Case Success in Retail!

Lecture 134 LLMs Adoption Summary and Perspectives

Section 16: GenAI Audit, Security Case Studies, Tools, Solutions and Opportunities

Lecture 135 Introduction

Lecture 136 Deep Dive Into The Security for AI Ecosystem

Lecture 137 AuditOne Case Study

Lecture 138 AuditOne Case Study Deep Dive

Lecture 139 Guardrails Lifecycle and Vulnerabilities

Lecture 140 Raga LLM Hub - LLM Evaluation and Guardrails

Lecture 141 Giskard

Lecture 142 Case Study - Dropbox – LLM Guardrails / Moderation Adoption

Lecture 143 Content Moderation APIs

Lecture 144 Azure Guidance

Lecture 145 Case Study #1 LLM - Context Summary Enrichment Use case (Amazon, Swiggy)

Lecture 146 Case Study #2 – Instore Associate Product Help Bot

Lecture 147 Case Study #3 – Customer Facing chatbot - Checklist Intent, Context, Guardrails

Lecture 148 How to be Migrate chatbots from AI/ML to GenAI

Lecture 149 Data Relevance - Importance of Domain / LLM base data

Lecture 150 With Apple Intelligence being rolled out onto MacOS 15.1

Lecture 151 Best Practices - Success Stories - Klara

Lecture 152 GenAI Low Risk Adoption Framework

Lecture 153 GenAI Recomendations

Lecture 154 Generative AI, the American worker, and the future of work

Lecture 155 The Leadership Imperative: Vision Beyond the Hype

Lecture 156 Summary

Product Managers: Ideal for those adopting LLM-based solutions, this course will help you enhance product development and ensure secure implementation.,Data Scientists: Perfect for professionals aiming to integrate GenAI with data-driven projects, manage biases, and mitigate security risks effectively.,Cybersecurity Teams: Essential for cybersecurity and CISO teams involved in AI/ML and GenAI adoption, focusing on securing AI initiatives and understanding emerging threats.,Business Leaders and Executives: Beneficial for business leaders and executives targeting GenAI-based use case adoption, driving innovation while maintaining compliance and security.,Innovative Problem Solvers: Suited for creative thinkers who enjoy tackling complex challenges with cutting-edge technology and AI-driven solutions.,IT Professionals: Crucial for IT experts responsible for managing and securing AI infrastructure, ensuring robust cybersecurity measures are in place.,AI Enthusiasts and Tech Innovators: Great for individuals passionate about AI, looking to stay updated with the latest trends and advancements in Generative AI and cybersecurity.,Compliance Officers and Legal Experts: Valuable for professionals overseeing compliance and regulatory aspects, providing insights into AI frameworks, policies, and ethical considerations.