
Cybersecurity in Artificial Intelligence: Attacks Defenses and Real World Application
Anshuman Mishra
This audiobook is narrated by a digital voice.
In the era of rapid digital transformation, artificial intelligence (AI) has emerged as one of the most disruptive technologies across every conceivable industry. From healthcare and finance to smart cities and national defense, AI has dramatically reshaped the landscape of data processing, decision-making, and automation. However, as organizations and governments increasingly adopt AI, they must also confront an alarming reality: the rise of complex cybersecurity threats targeting and leveraging AI systems.
"Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications" is a comprehensive academic and professional course book that serves as a foundational and advanced guide to understanding the intersection of artificial intelligence and cybersecurity. This book is not merely a theoretical exploration—it is a hands-on, practical resource enriched with case studies, industry practices, emerging tools, and policy discussions that bridge the gap between AI development and cybersecurity preparedness.
AI, by its nature, is designed to learn, adapt, and make decisions based on data. However, this learning capability is also what makes AI systems vulnerable to manipulation, data poisoning, adversarial attacks, and systemic biases. While traditional cybersecurity mechanisms focus on securing data networks and IT infrastructure, AI systems introduce unique risks that require novel approaches, innovative frameworks, and intelligent countermeasures.
This book equips readers with the knowledge, tools, and skills necessary to understand and defend against the growing threats targeting AI systems. Whether you are a student, researcher, IT professional, ethical hacker, or corporate strategist, this book provides invaluable insights into building robust, ethical, and secure AI-driven systems that can withstand both current and future threats.
Purpose and Importance of the Book
Cybersecurity and artificial intelligence are no longer separate domains. The growing integration of AI into critical infrastructure and consumer products has given rise to new categories of threats—ranging from adversarial machine learning and model inversion to AI-driven malware and autonomous threat actors. Simultaneously, AI is also transforming the cybersecurity industry by enabling proactive threat detection, predictive analytics, and adaptive security policies.
The purpose of this book is threefold:
To EducateTo EquipTo Empower
Duration - 20h 32m.
Author - Anshuman Mishra.
Narrator - Digital Voice Madison G.
Published Date - Tuesday, 07 January 2025.
Copyright - © 2025 Anshuman Mishra ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. In the era of rapid digital transformation, artificial intelligence (AI) has emerged as one of the most disruptive technologies across every conceivable industry. From healthcare and finance to smart cities and national defense, AI has dramatically reshaped the landscape of data processing, decision-making, and automation. However, as organizations and governments increasingly adopt AI, they must also confront an alarming reality: the rise of complex cybersecurity threats targeting and leveraging AI systems. "Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications" is a comprehensive academic and professional course book that serves as a foundational and advanced guide to understanding the intersection of artificial intelligence and cybersecurity. This book is not merely a theoretical exploration—it is a hands-on, practical resource enriched with case studies, industry practices, emerging tools, and policy discussions that bridge the gap between AI development and cybersecurity preparedness. AI, by its nature, is designed to learn, adapt, and make decisions based on data. However, this learning capability is also what makes AI systems vulnerable to manipulation, data poisoning, adversarial attacks, and systemic biases. While traditional cybersecurity mechanisms focus on securing data networks and IT infrastructure, AI systems introduce unique risks that require novel approaches, innovative frameworks, and intelligent countermeasures. This book equips readers with the knowledge, tools, and skills necessary to understand and defend against the growing threats targeting AI systems. Whether you are a student, researcher, IT professional, ethical hacker, or corporate strategist, this book provides invaluable insights into building robust, ethical, and secure AI-driven systems that can withstand both current and future threats. Purpose and Importance of the Book Cybersecurity and artificial intelligence are no longer separate domains. The growing integration of AI into critical infrastructure and consumer products has given rise to new categories of threats—ranging from adversarial machine learning and model inversion to AI-driven malware and autonomous threat actors. Simultaneously, AI is also transforming the cybersecurity industry by enabling proactive threat detection, predictive analytics, and adaptive security policies. The purpose of this book is threefold: To EducateTo EquipTo Empower Duration - 20h 32m. Author - Anshuman Mishra. Narrator - Digital Voice Madison G. Published Date - Tuesday, 07 January 2025. Copyright - © 2025 Anshuman Mishra ©.
Language:
English
Anshuman Mishra
Duración:00:00:07
Book Title: | “Cybersecurity in Artificial Intelligence: Attacks, Defenses, and Real-World Applications”
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Table of Contents | 🔹 Unit 1: Foundations of AI and Cybersecurity
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🔹 Unit 2: Threats and Vulnerabilities in AI Systems
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🔹 Unit 3: AI in the Hands of Attackers
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🔹 Unit 4: Defense Mechanisms for Securing AI
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🔹 Unit 5: Advanced Applications and Industry Tools
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🔹 Unit 6: Ethics, Policies, and Future Trends
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Introduction
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Purpose and Importance of the Book
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Benefits of Studying This Book
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Real-World Applications
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The Reader’s Journey
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Target Audience
Duración:00:00:47
Final Thoughts
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1. Evolution and Branches of AI (ML, DL, NLP, RL)
Duración:00:21:43
2. Why AI Needs Cybersecurity
Duración:00:11:22
3. Attack Surface in Intelligent Systems
Duración:00:31:58
4. Case Study: Microsoft Tay Chatbot Shutdown (Adversarial User Input)
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4. Case Study: Microsoft Tay Chatbot Shutdown
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5. Learning Resources and Staying Updated
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1. CIA Triad and Its Relevance in AI
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2. Common Cyber Attacks (Malware, Phishing, DoS, Man-in-the-Middle)
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3. Role of Cryptography and Hashing
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4. Case Study: Equifax Data Breach – Weak AI-Driven Security Detection
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1. Introduction to Data Poisoning and Training-Time Attacks
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2. Types of Data Poisoning Attacks
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3. General Mitigation Strategies for Training-Time Attacks
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1. Data Quality Issues
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2. Overfitting and Underfitting
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3. Model Complexity and Generalization
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4. Adversarial Attacks and Robustness
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5. Data Leakage
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6. Concept Drift and Data Distribution Shifts
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7. Interpretability and Explainability
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8. Ethical Considerations
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1. Introduction to Trojan Attacks in Machine Learning
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2. Case Study: Trojan Attack on an Image Classifier (Hypothetical Scenario)
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3. Impact on Model Accuracy
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4. Impact on Model Integrity
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5. Detection and Mitigation Strategies
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1. Types of Data Poisoning Attacks
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2. Impact on Model Accuracy and Integrity
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3. Case Study: Trojan Attack in Image Recognition Models
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1. Fast Gradient Sign Method (FGSM)
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2. Projected Gradient Descent (PGD)
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3. Carlini-Wagner (C&W) Attack
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4. Boundary Attack
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5. Evasion vs. Poisoning vs. Extraction
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5.1. Evasion Attacks
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5.2. Poisoning Attacks
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5.3. Model Extraction Attacks (Model Inversion/Stealing)
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Summary of Differences:
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6. Case Study: Fooling Traffic Sign Detection in Autonomous Cars
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6.1. Significance and Threat Landscape
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6.2. Attack Methodologies: From Digital to Physical
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6.3. Practical Challenges and Implications
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6.4. Defenses Against Adversarial Traffic Sign Attacks
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6.5. Future Directions and Ongoing Research
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🧠 1. Understanding Adversarial Attacks: FGSM, PGD, Carlini-Wagner, Boundary
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🔐 2. Evasion vs. Poisoning vs. Extraction Attacks
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🚗 3. Case Study: Fooling Traffic Sign Detection in Autonomous Cars
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5. Case Study: Stealing Models from Open ML APIs (Google, Amazon)
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MCQs on Model Inversion, Model Stealing, and Membership Inference
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MCQs on Intellectual Property and Black-Box API Vulnerabilities
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MCQs on Case Study: Stealing Models from Google & Amazon APIs
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1. AI-Generated Phishing
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2. AI-Driven Spear Phishing
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3. Social Engineering Bots
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General Countermeasures Against AI-Powered Social Engineering
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4. Deepfakes and Synthetic Identity Generation
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5. Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation
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MCQs on AI-generated Phishing, Spear Phishing & Social Engineering Bots
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MCQs on Deepfakes and Synthetic Identity Generation
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MCQs on Case Study: DeepNude, Fake Celebrity Scandals & Political Disinformation
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1. AI-Crafted Polymorphic Malware
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2. Smart Ransomware and Botnets
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3. AI for Automated Scanning and Payload Generation
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4. Case Study: Emotet AI-based Malware Campaign
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MCQs on AI-Crafted Polymorphic Malware
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MCQs on Smart Ransomware and Botnets
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MCQs on AI for Automated Scanning and Payload Generation
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1. Defensive Distillation and Gradient Masking
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2. Adversarial Training
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3. Detection of Adversarial Inputs
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4. Case Study: Robust AI in Financial Fraud Detection
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MCQs on Defensive Distillation and Gradient Masking
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MCQs on Adversarial Training
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MCQs on Detection of Adversarial Inputs
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Chapter 9: Securing the AI Lifecycle
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1. Secure Data Collection, Storage, and Validation
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2. Model Testing, Versioning, and Deployment Safeguards
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3. Continuous Monitoring and Feedback Loops
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4. Case Study: Uber’s AI Failure in Self-Driving Car Incident
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Secure Data Collection, Storage, and Validation
Duración:00:01:50
Model Testing, Versioning, and Deployment Safeguards
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Continuous Monitoring and Feedback Loops
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Case Study: Uber’s AI Failure in Self-Driving Car Incident
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1. The Imperative of Explainable and Trustworthy AI
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2. Importance of Interpretability
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3. LIME (Local Interpretable Model-agnostic Explanations)
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4. SHAP (SHapley Additive exPlanations)
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5. Bias Detection and Fairness Audits
Duración:00:12:40
6. Logging and Explainability for Compliance
Duración:00:13:50
7. Case Study: COMPAS Recidivism Prediction Bias Lawsuit
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Interpretability: LIME, SHAP, and Model Transparency
Duración:00:03:39
Bias Detection and Fairness Audits
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Logging and Explainability for Compliance
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1. The Evolving Landscape of AI Security
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2. TensorFlow Privacy
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3. CleverHans
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4. IBM ART (Adversarial Robustness Toolbox)
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5. Use of Metasploit, Wireshark, and Kali Linux for AI Apps
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6. Metasploit for AI Applications
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7. Wireshark for AI Network Traffic Analysis
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8. Kali Linux for AI Security Testing
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9. Secure AI Pipelines with MLOps
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10. Case Study: Red Teaming AI Pipelines in Healthcare
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TensorFlow Privacy, CleverHans, IBM ART
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Use of Metasploit, Wireshark, and Kali Linux for AI Applications
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Secure AI Pipelines with MLOps
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1. The Confluence of AI, Blockchain, IoT, and Quantum Computing
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2. AI + Blockchain for Secure Identity and Data Integrity
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3. Securing AI in IoT Environments
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3. Quantum Attacks on Encryption and Model Privacy
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4. Case Study: Smart Home Breaches via Voice AI Assistants
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AI + Blockchain for Secure Identity and Data Integrity
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Securing AI in IoT Environments
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1. Data Privacy Regulations and Their Impact on AI
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2. AI Risk Frameworks
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3. Ethical Hacking and Red Teaming in AI
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4. Case Study: The Facebook-Cambridge Analytica Scandal
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Data Privacy Laws
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AI Risk Frameworks
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1. Roles in AI Cybersecurity: Navigating a New Frontier
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2. Key Skills and Certifications for AI Cybersecurity
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3. Learning Roadmap and Project Ideas for AI Cybersecurity
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4. Mini Insights from Industry Professionals (Synthesized)
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Roles & Responsibilities
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Key Skills & Certifications
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Learning Roadmap & Project Ideas
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Mini Interviews: Insights from Industry Professionals
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Advanced Concepts
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Emerging Trends
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1. The Evolving Threat Landscape and the Need for AI in Cybersecurity
Duración:00:02:36
2. The Foundational Role of AI and Machine Learning in Cybersecurity
Duración:00:03:23
3. Autonomous Cyber Defense with AI
Duración:00:09:57
4. Predictive Threat Intelligence Using AI
Duración:00:08:42
5. Ethical Considerations of AI in Cybersecurity
Duración:00:08:58
6. The Impact of Quantum Computing on AI Cybersecurity
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7. Regulatory Landscape for AI in Cybersecurity
Duración:00:07:26
8. Integration of Generative AI with Security Operations
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9. Integration of Generative AI with Security Operations
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10. Case Study: Generative AI Prompt Injection Attacks on Chatbots
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Autonomous Cyber Defense with AI
Duración:00:01:22
Predictive Threat Intelligence Using AI
Duración:00:06:25