Essential Security for AI Applications: Safeguarding GenAI Technologies

Artificial intelligence applications have brought remarkable advancements but also serious security challenges that traditional cybersecurity measures cannot adequately address. As we increasingly integrate AI systems like LLM applications and retrieval pipelines into our workflows, we expose new vulnerabilities that could compromise sensitive information or disrupt operations. This course is designed to provide a comprehensive, practical framework for securing generative AI systems in real-world environments.

Throughout the course, you will gain a deep understanding of how modern AI threats operate, from how attackers exploit input prompts to how sensitive data might leak through various layers of AI systems. We will guide you through the entire AI stack, offering concrete strategies for implementing effective security measures at every critical point, ensuring you can confidently secure and operate generative AI at scale. Each module is tailored to equip you with the tools and knowledge needed to navigate the complex landscape of AI security and make informed decisions.

You’ll be provided with a wealth of resources, including architecture diagrams, security policy templates, and practical checklists that can be immediately applied to your projects. With a focus on actionable strategies rather than just theory, this course readies you for one of the fastest-growing fields in technology, ensuring your AI deployments are resilient against evolving threats.

What you will learn:

• Examine how GenAI systems expand the attack surface across models, data, and tools
• Use an end to end AI security architecture to map protections onto each subsystem
• Develop comprehensive threat scenarios for LLM based applications and choose fitting safeguards
• Deploy guardrail frameworks and policy engines to control user inputs and model outputs
• Integrate security gates into AI delivery processes, covering data validation and model assessments
• Set up authentication flows, permission boundaries, and controlled tool capabilities for AI services
• Apply data protection practices to RAG pipelines, including filtering, encryption, and structured access
• Operate AI SPM solutions to track assets, detect misconfigurations, and monitor system drift
• Build monitoring pipelines that capture queries, responses, tool usage, and evaluation metrics
• Design a full AI security control map and plan actionable rollout steps for organizational adoption

Course Content:

• Sections: 3
• Lectures: 20
• Duration: 6h 7m

Requirements:

• Basic understanding of software development or IT systems
• Familiarity with AI concepts such as LLMs or RAG is helpful but not required
• General knowledge of cybersecurity principles is beneficial
• Ability to read technical diagrams and system architectures
• No prior experience with AI security tools or frameworks needed

Who is it for?

• Professionals building or maintaining applications enhanced with generative AI
• ML specialists working with embeddings, retrievers, and model endpoints
• Architects responsible for structuring secure AI and data pipelines
• Security teams evaluating risks in AI powered systems
• Leaders and practitioners managing AI adoption, governance, and operational safety

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

👉 Don’t miss this coupon! – Cupón NOVEMBER_FREE3_2025