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AI Advanced Boot Camp

AI Advanced transforms professionals from AI-literate to AI-capable, combining deep technical understanding with hands-on governance implementation to build leaders who can operationalize trustworthy AI at scale.

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AI Advanced Boot Camp

AI Advanced: Technical Foundations & Strategic Implementation

The AI Advanced course builds on foundational knowledge to provide deep technical understanding and strategic implementation capabilities. This intensive 2-day program covers advanced AI technical concepts, real-world threat modeling, governance operationalization, and strategic planning. Perfect for professionals ready to move from AI literacy to AI leadership and implementation.

Training Camp AI Advanced

Course Duration: 2 Days
Course Code: TC-AAI

What You Will Learn

Master advanced AI technical concepts and develop strategic implementation capabilities for organizational AI governance.

  • Deep AI technical foundations and explainability
  • Real-world AI threat modeling and security
  • Operational governance framework implementation
  • AI lifecycle controls and DevSecOps integration
  • Strategic planning and roadmap development

Who This Course Is For

This AI Advanced course is designed for professionals ready to lead AI initiatives and implement governance at scale.

  • AI program managers and technical leads
  • Chief Technology Officers and IT directors
  • Risk and compliance professionals
  • DevSecOps and security architects
  • Consultants implementing AI governance

Why This Course Matters: From AI Literacy to AI Leadership

Deep Technical Understanding

Master how AI actually learns and makes decisions, enabling you to evaluate systems and communicate effectively with technical teams.

AI Security & Threat Modeling

Learn to identify and mitigate real AI threats using industry frameworks like MITRE ATLAS and OWASP Top 10 for LLMs.

Operational Governance

Transform governance frameworks from theory to practice with actionable toolkits and lifecycle controls.

Strategic Planning Capabilities

Develop comprehensive AI roadmaps with OKRs and create governance dashboards that drive organizational success.

Practical Labs & Projects

Apply learning through hands-on labs, threat modeling exercises, and capstone projects you can implement immediately.

Prerequisites: AI Advanced Course Requirements

This AI Advanced course builds on foundational AI knowledge. To succeed in this intensive program, participants should have:

  • AI Foundations Knowledge: Completion of AI Foundations course or equivalent understanding of AI types, ethics, and governance basics.
  • Technical Role Experience: Current or previous experience in technology management, IT security, compliance, or technical project management.
  • Governance Framework Familiarity: Basic understanding of risk management frameworks, compliance processes, or security controls.
  • Strategic Thinking: Experience in organizational planning, process improvement, or change management initiatives.
  • Hands-on Readiness: Comfort with interactive exercises, technical tools, and collaborative problem-solving.

🎯 Recommended Preparation

For maximum benefit, we recommend completing our AI Foundations course first or having equivalent knowledge of AI basics, governance principles, and current regulatory landscape. This AI Advanced course moves quickly through advanced concepts and practical implementation.

Looking for Groups: AI Advanced Team Training Options

Strategic AI Implementation Teams

Accelerate your organization’s AI maturity with comprehensive team training. Perfect for AI implementation teams, governance committees, and technical leadership groups. Includes customized content, industry-specific examples, and post-training consulting options.

Course Schedule: Complete 2-Day AI Advanced Curriculum

Module 1: How AI Learns

  • Deep dive into forward propagation, loss functions, backpropagation, and weight tuning
  • Advanced analogy-driven explanations for complex learning processes
  • Understanding transformers: attention mechanisms, token prediction, and representation learning
  • Critical analysis of AI limitations: why AI predicts sequences but doesn’t truly “understand”
Group Discussion: Analyze real-world examples where AI limitations have impacted business outcomes

Module 2: Explainability & Bias

  • Advanced Model Interpretability:
  • SHAP/LIME visualizations and practical implementation
  • Strategies for communicating AI outputs to non-technical teams
  • Balancing fairness, liability, and transparency requirements
  • Comprehensive Bias Analysis:
  • Advanced bias metrics: statistical parity, equal opportunity, disparate impact
  • Sophisticated mitigation approaches: reweighting and fairness post-processing
Interactive Lab: Use live explainability tools to test model outputs
Group Vote: “Would your organization approve this model for deployment?”

Module 3: AI in the Wild

  • Comprehensive analysis of the top 10 AI types powering today’s world: LLMs, computer vision, RPA, GANs, and more
  • Industry-specific use cases across healthcare, finance, defense, creative industries, and logistics
  • Deep examination of ethical, operational, and regulatory implications for each AI type
  • Strategic considerations for AI selection and implementation across different sectors
Group Discussion: Map AI types to your organization’s current and potential use cases

Module 4: Risk Taxonomies & Threat Models

  • Advanced Risk Frameworks:
  • MITRE ATLAS matrix implementation and practical application
  • OWASP Top 10 for LLMs: identification and mitigation strategies
  • STRIDE threat modeling methodology for AI systems
  • Operational Security Considerations:
  • OSINT & OPSEC considerations in AI system deployment
  • Defensive strategies: prompt injection demos, watermarking, and provenance tracking
Interactive Lab: Prompt injection attack demonstration and mitigation in sandbox environment
Solo Assignment: Threat model your own AI use-case using ATLAS framework

Module 5: Operationalizing Governance Frameworks

  • NIST AI RMF Implementation:
  • Practical application of Govern, Map, Measure, Manage functions
  • Developing organizational AI risk profiles and controls
  • ISO/IEC 42001 AI Management System:
  • Comprehensive overview and certification pathway
  • Integration with existing management systems
  • Practical Implementation Tools:
  • Ready-to-use checklists, tailoring profiles, and evidence artifacts
Group Exercise: Customize governance framework templates for your organization’s specific needs

Module 6: AI Lifecycle Controls

  • Strategic approach to embedding controls throughout each SDLC phase
  • DevSecOps integration strategies for building trustworthy AI from the ground up
  • Comprehensive mapping of risk frameworks to deployment checkpoints
  • Automated monitoring and continuous compliance approaches
Group Discussion: Design lifecycle controls for a complex AI deployment scenario

Module 7: Assurance & Audit Readiness

  • Compliance Framework Alignment:
  • SOC 2 and ISO 27001 integration with AI governance
  • Regulatory compliance mapping and documentation strategies
  • Audit Evidence and Documentation:
  • Leveraging model cards, logs, and documentation as audit evidence
  • Building comprehensive audit trails for AI systems
Guided Exercise: Conduct a mock audit to identify governance gaps and improvement opportunities

Module 8: Strategy & Future Vision

  • Strategic integration of course themes into comprehensive governance lifecycle
  • Emerging Trends & Strategic Implications:
  • Multi-modal AI, agentic systems, and sustainability considerations
  • Strategic implications of autonomous and embedded AI systems
  • Critical analysis: separating actionable practices from industry hype
Strategic Discussion: “What does mature AI governance look like for us in 1 year?”

Module 9: Capstone Project

  • Hands-on Implementation Project:
  • Build a functional prototype AI Governance Dashboard
  • Draft a comprehensive 90-day AI governance roadmap with measurable OKRs
  • Develop implementation timeline and resource allocation plan
  • Collaborative peer review and feedback session for continuous improvement
Capstone Presentation: Present governance roadmap and receive peer feedback for refinement

Module 10: Wrap-Up & Closing

  • Comprehensive Course Integration:
  • Synthesis of technical foundations with strategic implementation
  • Key takeaways: 3 critical lessons + 1 recommended action per participant
  • Pathway planning for continued AI governance maturity and professional development
Final Reflection: Personal action planning, Q&A, and comprehensive course evaluation

FREQUENTLY ASKED QUESTIONS

AI Advanced Boot Camp FAQ

Yes, AI Advanced builds directly on concepts from our AI Foundations course. You need solid understanding of AI types, ethics basics, and governance frameworks before tackling advanced threat modeling, technical implementations, and strategic planning covered in this intensive program.

Unlike courses that only mention frameworks, we provide hands-on implementation of NIST AI RMF and ISO 42001, complete with checklists, evidence artifacts, and audit readiness strategies. You’ll learn to operationalize governance—not just understand it—plus advanced GRC topics like SOC 2 alignment and DevSecOps integration that technical courses typically overlook.

We bridge the gap between high-level business courses and deep technical training. You’ll understand forward propagation, backpropagation, and transformers through practical analogies, then apply that knowledge to real threat modeling using MITRE ATLAS and OWASP frameworks—perfect for leaders who need technical credibility without becoming data scientists.

This course is designed for AI program managers, CTOs, risk professionals, DevSecOps teams, and consultants implementing AI governance. If you’re responsible for AI strategy, security, or compliance—and need to move beyond basic AI literacy to practical implementation—this training provides the advanced skills you need.

You’ll complete threat modeling exercises using real frameworks, build a prototype AI governance dashboard, conduct mock audits, and create a 90-day implementation roadmap with OKRs. Every module includes interactive labs, from prompt injection demonstrations to SHAP/LIME explainability tools—ensuring you leave with immediately applicable skills.

Beyond covering current regulations like the EU AI Act, we teach you to build adaptive governance systems that scale with regulatory changes. You’ll master audit readiness, evidence documentation, and compliance automation strategies that work across multiple frameworks—essential as AI regulation rapidly evolves globally.

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Training Camp’s trainers, especially our instructor Jeff, are in a class of their own. His blend of industry knowledge and teaching acumen is outstanding.

Mike Guzman

USAF

As a project manager, I found his insights invaluable. He simplified complex AI concepts, making them digestible for everyone. Training Camp is truly fortunate to have such talent.

Chris Beebie

Data Corp

Sarah, our trainer at Training Camp, was exceptional. Her approach to training was personalized, making it feel like she was mentoring each one of us individually.

Lea Reed

US Army NCOS

I left the class with a new-found confidence.

Mark N

Booz Allen Hamilton

I believe self-study would have been much much harder to comprehend the information and pass the test on a first try. My colleagues who self-studied flunked the first time they took it. I passed the first time after attending the boot camp.

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