Which AI Security Techniques Are Taught in the Certification Course?

As businesses and industries increasingly integrate artificial intelligence (AI) and large language models (LLMs) into their operations, securing these systems has become a critical priority. Cyberattacks targeting AI are growing more sophisticated, with techniques like prompt injection and model poisoning posing significant risks. A specialized AI security certification course equips professionals with the tools and knowledge to protect these systems. 

This article explores the key AI security techniques covered in such a course, based on the curriculum of a comprehensive, hands-on training program designed for security engineers, developers, and technical leaders. From foundational concepts to advanced attack and defense strategies, here’s what you’ll learn.

Understanding AI and LLMs: The Foundation

Before diving into security techniques, the course lays a solid groundwork by explaining how AI and LLMs function. You’ll start with the basics of artificial intelligence, including its evolution from traditional systems to generative AI. The curriculum covers essential concepts like:

• What Are LLMs? You’ll learn the difference between open-source and closed-source LLMs and how they power applications like smart customer support chatbots.
• Embeddings and Vector Databases: These lessons introduce embeddings numerical representations of data that help LLMs understand internal datasets and how to store and query them for practical applications.
• Retrieval-Augmented Generation (RAG): You’ll explore how RAG combines LLMs with external data to create context-aware applications, such as internal security chatbots.

These foundational topics ensure you understand the technology you’re securing, even if you have no prior AI experience. This knowledge is crucial for identifying vulnerabilities and implementing effective defenses.

Building and Securing AI Applications

The course emphasizes hands-on learning by guiding you through the creation of AI applications, which helps you understand their vulnerabilities from the inside out. Key techniques include:

• Building Your First LLM App: You’ll write a simple AI application, often referred to as the “Hello World” of AI, to grasp how LLMs process tasks and integrate with tools.
• Prompt Engineering: You’ll master the principles of crafting effective prompts, learning techniques for optimization and avoiding common pitfalls (anti-patterns). Good prompt design is critical for reducing vulnerabilities like prompt injection.
• Creating a RAG Application: Through a project, you’ll build an internal security chatbot using RAG, combining embeddings and vector databases to handle real-world use cases.
• Using Langchain and Langsmith: These tools help streamline AI workflows. You’ll learn how to build applications with Langchain and monitor performance with Langsmith, ensuring secure and efficient operations.

These practical exercises teach you how AI systems are constructed, setting the stage for understanding how attackers exploit them and how to defend them.

Mastering AI Agents

A significant portion of the course focuses on AI agents autonomous systems that think, act, and adapt to complete tasks. You’ll learn:

• Agent Functionality: The course breaks down the “Think → Act → React → Observe” cycle, where agents analyze tasks, execute actions using tools (like APIs or scanners), evaluate results, and refine their approach.
• Building an Agent: You’ll create a Python-based agent that integrates with an LLM to perform tasks like web scanning with tools such as Nmap or ExploitDB. This project teaches you how agents coordinate intelligence with tool execution for complex workflows.
• Tools and Function Calls: You’ll explore how agents use tools (e.g., Nmap for port scanning or custom APIs) to perform specific actions, understanding their role in the “Act” phase of the agent cycle.

By building and analyzing agents, you’ll gain insights into their potential vulnerabilities and how to secure them against misuse.

Exploring the AI Attack Surface

Understanding how attackers target AI systems is critical for effective defense. The course dives into real-world attack techniques through hands-on labs, including:

• Prompt Injection: You’ll learn how attackers manipulate prompts to alter AI behavior, using an essay AI app as a practical example. This technique exploits poorly designed prompts to bypass controls or extract unintended outputs.
• Sensitive Information Disclosure: Through a lab attacking an AI support bot, you’ll see how poorly secured systems can leak sensitive data, such as user credentials or proprietary information.
• Indirect Prompt Injection: This advanced attack involves embedding malicious inputs in user data to manipulate AI behavior. You’ll explore this using a personal assistant AI bot.
• Model Backdoor: The course covers how adversaries embed hidden behaviors in AI models, using a real-world example from Hugging Face to illustrate the risks.
• Model Poisoning: You’ll study how attackers compromise training data to skew AI outputs, learning the impacts through hands-on poisoning attack simulations.

These labs provide practical experience in executing attacks, helping you understand the threat landscape and prepare for real-world scenarios.

Conducting Security Reviews and Threat Modeling

A key skill taught is how to assess and secure AI systems systematically. The course covers:

• Security Reviews: You’ll learn to perform detailed audits of LLM and AI applications using tools, checklists, and best practices. This includes evaluating code, configurations, and data flows for vulnerabilities.
• Threat Modeling: The curriculum teaches you to develop threat models tailored to AI systems, identifying potential attack vectors and prioritizing risks. This proactive approach ensures you can anticipate and mitigate threats before they’re exploited.

These techniques equip you to evaluate AI systems holistically, ensuring they’re robust against both technical and human-based attacks.

Defensive Strategies for AI Systems

The course emphasizes multi-layered defense strategies to protect AI applications. Key techniques include:

• Input Validation and Response Management: You’ll learn to validate prompts and manage AI outputs to prevent attacks like prompt injection. This includes using sanitization techniques and monitoring response patterns.
• Mitigating Model Attacks: The course covers defenses against backdoors and poisoning, including:
• Model Vetting: Inspecting models for hidden vulnerabilities before deployment.
• Anomaly Detection: Monitoring AI behavior to identify unusual patterns that may indicate an attack.
• Secure Training Practices: Ensuring training data is clean and protected from tampering.
• Third-Party Model Risk Assessment: Evaluating risks when using external models, such as those from open-source platforms.
• Prompt Sanitization and Robust Design: You’ll explore how to design prompts that resist manipulation and implement monitoring to detect anomalies in real time.

These defensive techniques ensure you can harden AI systems against a range of threats, from data leaks to malicious model behavior.

Understanding Model Context Protocol (MCP)

The course includes a dedicated module on the Model Context Protocol (MCP), a framework for managing AI model interactions. You’ll learn:

• What MCP Is: Understand its role in facilitating secure communication between AI components.
• Building an MCP Server: A step-by-step guide to creating your own server, ensuring secure data handling.
• Attacking and Defending MCP Servers: You’ll explore common attack techniques targeting MCP servers and learn defensive strategies to protect them.

This knowledge is crucial for securing the infrastructure that supports AI applications, especially in enterprise settings.

Practical Implementation and Tool Architecture

The course wraps up with hands-on implementation, teaching you to:

• Build an LLM Security Tool: You’ll code and integrate a security tool that leverages LLMs, focusing on secure design and implementation.
• Understand Tool Architecture: Learn about components, data flows, and design patterns to create robust AI security tools.

These skills ensure you can apply what you’ve learned to real-world projects, from developing secure AI applications to enhancing existing systems.

Why These Techniques Matter

The techniques taught in this course are critical because AI and LLMs are increasingly targeted by cybercriminals. With attacks like prompt injection and model poisoning on the rise, organizations need professionals who can both understand these systems and protect them. 

The hands-on labs covering everything from building agents to mitigating backdoors equip you with practical skills that align with industry needs. Whether you’re a security engineer, developer, or technical leader, mastering these techniques positions you to tackle the unique challenges of AI-driven systems.

Conclusion

This AI security certification course offers a comprehensive, hands-on approach to securing AI and LLM systems, covering foundational concepts, real-world attacks, and robust defenses. From prompt engineering to model poisoning mitigation, the techniques taught are practical and relevant, ensuring you can protect cutting-edge technologies.

For those looking to dive into this field, Modern Security’s course provides the tools and expertise to stay ahead in the evolving landscape of AI security.