Secure Your AI Applications: Essential Strategies for Enterprise

In today’s digital landscape, the integration of artificial intelligence (AI) into business processes has revolutionized the way organizations operate. However, with the rapid deployment of AI systems comes an array of vulnerabilities that traditional security measures are ill-equipped to manage. This course, “Enterprise AI Security Architecture: Protecting AI Apps,” addresses these challenges head-on, providing a comprehensive framework designed to secure AI applications against real-world threats.

Throughout this course, you will explore the unique security challenges faced by AI systems, particularly those powered by large language models (LLMs) and retrieval-augmented generation (RAG) applications. The curriculum is meticulously structured to arm you with the necessary tools to understand modern AI attacks—such as prompt injection, model exploitation, and data exposure—and implement robust controls that safeguard your data and maintain the integrity of your applications. By the end of the course, you’ll have a solid grasp of how to map threats across AI architectures, bolster data governance, and deploy security measures that truly align with enterprise needs.

What sets this course apart is its practical approach. Each module is designed not just to explore theoretical concepts but to provide immediately applicable skills and knowledge. You will receive architecture diagrams, templates for threat modeling, and actionable checklists that will streamline your AI security strategy. Whether you are looking to enhance your current security posture or prepare for the increasing demand for skilled professionals in AI security, this course offers a pathway to success, empowering you to build and operate safe and reliable GenAI applications from the ground up.

What you will learn:

• Analyze the unique attack surface of GenAI systems and see how LLMs and RAG apps are exploited
• Use a structured AI security architecture to plan protections across all layers of an AI solution
• Build complete threat models for AI workloads and connect identified risks with practical defenses
• Deploy AI gateways and guardrail engines to filter inputs, outputs, and tool executions
• Integrate security into every AI development stage, including data sourcing, evaluations, and safety reviews
• Set up strong authentication, scoped permissions, and regulated tool access for AI components
• Govern sensitive data in RAG pipelines with structured policies, metadata rules, and controlled retrieval flows
• Operate AI SPM tools to track models, datasets, connectors, and detect risk or drift over time
• Implement logging, telemetry, and evaluation pipelines to observe how AI behaves in production
• Construct a complete AI security control stack and define an actionable plan for short and long term adoption

Course Content:

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

Requirements:

• General experience with IT, software, or engineering environments
• Helpful but optional familiarity with AI workflows or retrieval systems
• Basic awareness of cybersecurity ideas like access control or data protection
• Ability to follow technical explanations and architectural breakdowns
• No prior hands on work with AI security platforms or evaluations needed.

Who is it for?

• Engineers and developers creating applications powered by LLMs
• ML practitioners and data specialists working with model pipelines
• Solution architects defining AI system structures and security controls
• Cybersecurity and DevSecOps teams overseeing AI deployments
• Technical leaders aiming to manage AI risk and governance in their organizations.

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!

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