Ai In Cybersecurity [Cybersecurity - 02]
Published 8/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 20h 0m | Size: 7.85 GB
Master AI Theory, Cybersecurity Integration & Future Trends - No Coding Required for 2025 Success
What you'll learn
Explain key generative AI architectures including GANs, VAEs, diffusion models, and LLMs with their real-world applications
Analyze the intersection of AI and cybersecurity, including threat detection, adversarial attacks, and security frameworks
Evaluate ethical implications and societal impacts of generative AI across industries like healthcare, finance, and creative arts
Compare traditional vs AI-enhanced cybersecurity approaches using case studies and theoretical frameworks from the field
Identify emerging threats and future trends in generative AI, including deepfakes, automated attacks, and quantum security
Apply theoretical knowledge to assess AI system vulnerabilities, model robustness, and supply chain risks in practice
Requirements
Basic computer literacy and familiarity with fundamental technology concepts, though no programming experience is required.
High school level mathematics understanding, particularly basic statistics and logical reasoning skills for grasping AI concepts.
Genuine curiosity about artificial intelligence and cybersecurity trends, with willingness to engage with theoretical concepts.
Access to a computer with internet connection for accessing course materials and staying current with rapidly evolving field developments.
Open mindset toward ethical considerations and societal implications of emerging technologies in professional and personal contexts.
Description
This course contains the use of artificial intelligence. This comprehensive course explores the theoretical foundations and advanced applications of artificial intelligence in cybersecurity, providing learners with deep conceptual understanding of how AI technologies revolutionize modern security practices. Through 50 structured lectures across 10 sections, students will master the theoretical frameworks that underpin AI-driven security solutions.The course begins with foundational theories linking AI and cybersecurity, covering core security principles, AI paradigms, and the convergence of these fields. Students will explore mathematical foundations essential for AI algorithms, including linear algebra, probability theory, and statistical methods applied to threat analysis.Primary topics include supervised and unsupervised learning theories for threat classification and anomaly detection, reinforcement learning in adversarial environments, and natural language processing for security intelligence. The curriculum delves into machine learning models such as decision trees, support vector machines, Bayesian methods, and clustering algorithms specifically contextualized for cybersecurity applications.Advanced sections cover deep learning frameworks including neural networks, CNNs, RNNs, and autoencoders for network anomaly detection. Students will examine cutting-edge topics like generative adversarial networks, transfer learning, attention mechanisms, and quantum computing's impact on security.The course also addresses socio-technical systems theory, human factors in security, trust models, organizational security frameworks, and risk management theories. Ethical, legal, and privacy considerations are thoroughly explored alongside adversarial machine learning and future challenges.Through theoretical case studies covering enterprise systems, critical infrastructure protection, financial fraud detection, and advanced persistent threats, students gain practical context for applying theoretical knowledge. The course culminates with comprehensive integration of all concepts and research methodologies.
Who this course is for
Technology Professionals: IT specialists, cybersecurity analysts, and software developers seeking to understand how generative AI transforms their field and enhances security operations without requiring hands-on coding experience.
Business Leaders and Managers: Executives, project managers, and decision-makers who need strategic understanding of generative AI applications, risks, and opportunities to make informed technology investment and policy decisions.
Students and Career Changers: University students, recent graduates, and professionals transitioning into AI or cybersecurity fields who want comprehensive theoretical foundation before pursuing specialized technical training.
Entrepreneurs and Innovators: Startup founders, product managers, and business strategists exploring how generative AI can transform their industries while understanding associated security challenges and ethical considerations.
Cybersecurity Professionals: Security architects, risk managers, and compliance officers who need to understand AI-enhanced threats, defensive strategies, and governance frameworks in the evolving threat landscape.
Lifelong Learners: Curious individuals from any background who want to understand the intersection of AI and cybersecurity, including societal implications and future trends shaping our digital world.
Ai In Cybersecurity [Cybersecurity - 02]
