AI Advanced

AI Advanced: Securing the Future of Artificial Intelligence

Artificial Intelligence is transforming everything, from personalized services to automated infrastructure—but with great power comes great vulnerability. This AI Advanced course is designed for professionals ready to understand, evaluate, and defend against real-world AI threats.

Through 19 detailed modules, you’ll explore adversarial AI, privacy risks, poisoning attacks, model tampering, deepfakes, and the complexities of securing large language models (LLMs). This course is ideal for cybersecurity practitioners, AI engineers, DevSecOps teams, and federal IT professionals who need to safeguard machine learning systems and generative AI applications.

Delivered by experienced instructors and grounded in hands-on, practical examples, this training prepares you to defend AI environments in high-risk sectors, especially those operating in or contracting with the federal government.

 

 

 

 

Why Take AI Advanced Training in Maryland?

With Washington DC’s federal agencies, Maryland’s cybersecurity corridor, and Virginia’s growing AI and cloud infrastructure hubs, the DMV region is a hotspot for AI deployment and innovation, but also a target for adversarial threats.

By training with TrainACE:

• You gain regionally relevant skills in AI threat modeling, governance, and red-teaming
• You prepare to meet DoD, NIST, and executive order guidelines around AI security and trustworthy AI
• You engage with real-world, hands-on labs that go beyond theory and prepare you for today’s evolving threat landscape

Whether you're supporting a SOC, overseeing AI infrastructure, or advising on AI ethics and policy, this course provides critical defensive and offensive knowledge.

What You Need to Know Before Taking AI Advanced

Prerequisites:

• Strong understanding of basic AI/ML concepts
• Familiarity with Python scripting and cloud-based AI workflows
• Ideal candidates will have completed TrainACE’s AI Essentials course, or possess equivalent foundational knowledge

While this is a highly technical course, content is structured for professionals with solid IT or cybersecurity backgrounds, not just PhDs in machine learning.

What Are the Benefits of AI Advanced Training?

After completing this course, you’ll be able to:

• Evaluate and mitigate adversarial threats against deployed AI
• Conduct poisoning, evasion, and privacy attacks in lab environments
• Understand the security implications of LLMs and generative AI (including ChatGPT and open-source models)
• Implement practical defenses like watermarking, differential privacy, secure MLOps, and threat modeling
• Apply governance and standards frameworks including AI RMF, MLSecOps, and Secure by Design
• Be better prepared for roles such as AI Red Teamer, Adversarial ML Researcher, Privacy Engineer, or ML Security Analyst

Who Needs AI Advanced Training?

This course is ideal for:

• Cybersecurity professionals specializing in AI security, red-teaming, or threat analysis
• AI/ML engineers deploying models into production
• Federal and DoD contractors subject to AI risk and compliance mandates
• DevSecOps teams implementing secure MLOps pipelines
• Data scientists interested in secure-by-design principles
• Professionals pursuing careers in trustworthy AI, privacy engineering, or secure development

How Long DoesAI Advanced Training Take?

This course is delivered over 5 full days and is available in both in-person (Greenbelt, MD) and live-online formats. It includes:

• Instructor-led sessions
• AI/ML engineers deploying models into production
• Interactive labs
• Real-world attack scenarios
• Certificate of Completion from TrainACE

How Hard Is the AI Advanced Course?

This is an intermediate-to-advanced level course. It assumes a working knowledge of AI/ML fundamentals and some scripting experience. However, the content is structured to guide learners through each concept with:

• Hands-on walkthroughs
• Sample attack/defense scenarios
• Templates and tools for lab use

If you’ve worked in security or AI and are ready to go deeper into adversarial risk, this course will meet you where you are.

Suggested Next Certifications / Courses

You will receive a TrainACE Certificate of Completion, recognizing your achievement in this advanced, hands-on AI security course. This training supports career advancement in fields such as:

• Adversarial Machine Learning
• AI Red Teaming
• AI Security Governance and Compliance
• Trustworthy AI Risk Management

It also serves as a foundation for pursuing:

• Certified Ethical Hacker (CEH)
• CompTIA Security+
• CompTIA CySA+
• CompTIA SecurityX (CASP)
• NIST AI RMF/CGRC aligned governance credentials

What Will I Learn in This AI Advanced Class?

This course includes 19 detailed modules covering core, applied, and cutting-edge AI security practices:

• Getting Started with AI, covers key concepts and terms surrounding AI and ML to get us started with adversarial AI.
• Building Our Adversarial Playground, goes through the step-by-step setup of our environment and the creation of some basic models and our sample Image Recognition Service (ImRecS)
• Security and Adversarial AI, discusses how to apply traditional cybersecurity to our sample ImRecS and bypass it with a sample adversarial AI attack.
• Poisoning Attacks, covers poisoning data and models, and how to mitigate them with examples from our ImRecS.
• Model Tampering with Trojan Horses and Model Reprogramming, looks at changing models by embedding code-based Trojan horses and how to defend against them.
• Supply Chain Attacks and Adversarial AI, covers traditional and new AI supply chain risks and mitigations, including building our own private package repository.
• Evasion Attacks against Deployed AI, explores fooling AI systems with evasion attacks and how to defend against them.
• Privacy Attacks – Stealing Models, looks at model extraction attacks to replicate models and how to mitigate these attacks, including watermarking.
• Privacy Attacks – Stealing Data, looks at model inversion and inference attacks to reconstruct or infer sensitive data from model responses.
• Privacy-Preserving AI, discusses techniques for preserving privacy in AI, including anonymization, differential privacy, homomorphic encryption, federated learning, and secure multi-party computations.
• Generative AI – A New Frontier, provides a hands-on introduction to generative AI with a focus on GANs.
• Weaponizing GANs for Deepfakes and Adversarial Attacks, provides an exploration of how to use GANs to support adversarial attacks, including deepfakes, and how to mitigate these attacks.
• LLM Foundations for Adversarial AI, provides a hands-on introduction to LLMs using the OpenAI API and LangChain to create our sample Foodie AI bot with RAG
• Adversarial Attacks with Prompts, explores prompt injections against LLMs and how to mitigate them
• Poisoning Attacks and LLMs, looks at poisoning attacks with RAG, embeddings, and fine-tuning, using Foodie AI as an example, and appropriate defenses.
• Advanced Generative AI Scenarios, looks at poisoning the open source LLM Mistral with fine-tuning on Hugging Face, model lobotomization, replication, and inversion and inference attacks on LLMs.
• Secure by Design and Trustworthy AI, explores a methodology using standards-based taxonomies, threat modeling, and risk management to build secure AI with a case study combining predictive AI and LLMs.
• AI Security with MLSecOps, looks at MLSecOps patterns with examples of how to apply them using Jenkins, MLflow, and custom Python scripts.
• Maturing AI Security, discusses applying AI security governance and evolving AI security at an enterprise level.