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GenAI and AI Security – Frameworks and Best Practices
1.221 Teilnehmer:innen
Das wirst du lernen
- Master the foundational principles and best practices for integrating Generative AI in cybersecurity.
- This course uses 1–2 minute byte-sized concept videos with additional materials in each chapter, so please enroll only if you are comfortable with this format.
- Practitioners who derive insights from research papers, case studies, and foundational concepts
- 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.
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Kursinhalt
17 Abschnitte • 171 Lektionen • 6 Std. 45 Min. Gesamtdauer
Anforderungen
- Those with a basic, working knowledge of AI/ML and GenAI technologies (Not for beginners)
- This course uses 1–2 minute byte-sized concept videos with additional materials in each chapter, so please enroll only if you are comfortable with this format.
- Professionals who are involved in or supporting GenAI initiatives at work / Enroll only if you have practical knowledge
- People who actively follow real-world AI/GenAI developments and use cases across industries
- Individuals who have worked with or alongside AI/ML teams and understand practical applications
- Security teams actively involved in GenAI initiatives or supporting projects that integrate AI/ML into products or workflows
- People interested in translating GenAI risks into actionable security controls, playbooks, or architecture
- Not Recommended for Complete beginners with no exposure to AI/GenAI projects
- Not Recommended for Theoretical or academic professionals without hands-on or applied experience
Beschreibung
Welcome to GenAI & AI Security – Frameworks and Best Practices for Responsible AI Adoption
Generative AI is transforming how products are built, decisions are made, and businesses operate. This course is designed for practitioners who want to move beyond hype and understand how to adopt GenAI responsibly, securely, and meaningfully.
More from a practitioner’s experience, drawing on research papers, core ideas, and real-world outcomes. It's not to aim a perfect recipe but provide moments of learning and useful directions. Don’t think of this as classroom-style coaching. It’s more about experience sharing and perspective alignment. That’s the blend, an industry practitioner’s perspective.
AI can amplify productivity, creativity, and automation but only when grounded in data, domain understanding, guardrails, and governance. This course uses 1–2 minute byte-sized concept videos with additional materials in each chapter, so please enroll only if you are comfortable with this format. I would rather be an imperfect teacher than a perfect LLM AI Avatar, Mistakes make us human :)
This Udemy course venture is more of summary of my daily blogs, research papers, ideas at work. This is not a perfect recipe but more of sharing a practitioners perspective across implementation of use cases.
What This Course Is (and Isn’t)
Bad AI use cases
AI suggesting layoffs
AI replacing human judgment in sales or hiring
Emotion detection without context or ethics
Responsible AI use cases
Knowledge assistants for FAQs and decision support
AI-assisted writing and summarization
Automated information processing (OCR, multimodal), with humans in control
Responsible AI isn’t about banning technology, it’s about using it with intent, limits, and accountability.
What You’ll Learn
By the end of this course, you’ll understand:
Core concepts: AI, ML, DL, GenAI
Cybersecurity risks in AI/ML systems
AI ethics, privacy, and data governance
AI risk & threat management using NIST AI RMF
AI controls, audits, compliance, and regulations (EU AI Act, GDPR, OECD)
Generative AI & LLM security: risks, biases, defenses
Real-world case studies across industries
Practical frameworks for low-risk, responsible AI adoption
How This Course Helps You
Build an AI lens to map your domain and data
Ask better AI solution questions, with clarity and required technical depth
Identify where to focus: GenAI PM · GenAI Development · Fine-tuning · Agents · Text-to-SQL · Vision · Domain-specific use cases
Distinguish hands-on expertise vs opinions vs hype
Evaluate AI systems using benchmarks, guardrails, and evidence
A practitioner-led AI experience, where ideas, logic, research, and real-world adoption come together.
Support & Mentorship
At any point during the course, you’re welcome to reach out for 1-on-1 discussions, project ideation, reviews, or mentoring.
Not recommended for beginners.
This course won’t make you an expert overnight but it will help you ask better, use-case-driven questions and evaluate AI systems with clarity and responsibility.
Before You Enroll
This course is for practitioners who care about thoughtful, responsible GenAI adoption. There’s no single “right” answer, what matters is your approach, perspective, and willingness to explore trade-offs. If that resonates with you you’ll feel at home here.
You’ll Get Lifetime Access To
Comprehensive video lessons
Real-world case studies
Practical exercises and projects
Up-to-date industry insights
If you’re making AI decisions without a clear risk, ROI, and governance lens, this course will change how you think
Enroll if you want to move from AI curiosity → responsible, real-world capability.
Build responsibly. Think critically. Deploy with control.
Happy learning.
Für wen eignet sich dieser Kurs:
- 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.