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1
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Icon | completed | Title | Hours | Level | Comments |
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2
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📊 | ✅ | Prisma AIRS Customer Presentation (Oct 2025) | 0.5 | beginner | |
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3
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📖 | ✅ | Reference Architecture - Securing AI Access and Applications: Overview | 1 | beginner | This document links to about 5 other architecture documents. This is arguably NOT intro level material! |
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4
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🎓 | Generative AI for Everyone | 5 | beginner | This course costs $49. Start your learning journey now and master the material in this course to earn a certificate that showcases your knowledge and achievement. | |
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5
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🎬 | ✅ | AI Engineering in 76 Minutes (Complete Course/Speedrun!) | 1.25 | intermediate | YouTube Video |
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6
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🎬 | Deep Dive into LLMs like ChatGPT - Andrej Karpathy | 3.5 | beginner | YouTube Video | |
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7
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🎬 | ✅ | you need to learn MCP RIGHT NOW!! (Model Context Protocol) | 0.25 | beginner | YouTube Video - a good start, found another video that compares SCRIPT.md to MCP, and the evoplution of MCP servers. Also could be helpful to find a video that explains "agent to agent" in a bit more details. |
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8
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🎬 | Andrej Karpathy: Software Is Changing (Again) | 0.67 | intermediate | YouTube Video | |
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9
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🎬 | ✅ | Most devs don't understand how LLM tokens work | 0.5 | beginner | YouTube Video - great explanation but the guy's hands are very distracting! |
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10
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🎓 | ✅ | AI Security Fundamentals | 1.5 | beginner | 06/25/2026 Microsoft Training Path - I captured the materials and my quiz at the end to this link. |
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11
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📰 | 🚫 | Deceptive Delight Attack Research - Unit 42 | 0.5 | intermediate | Broken Link? |
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12
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📄 | OWASP GenAI Security Project Solutions Reference Guide Q2_Q3'25 | 1 | intermediate | download and review the PDF | |
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13
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📄 | OWASP Top 10 for LLM Applications 2025 | 1 | intermediate | ||
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14
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📝 | ✅ | FAQ: Prisma AIRS (Nov 2025) | 0.5 | beginner | Reviewed it, but this looks like more of a "main reference" that we will keep coming back to over time. |
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15
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📝 | Prisma AIRS - PS Lab - Runtime Security API - GCP & n8n | 4 | intermediate | Trying out this lab configuration. || 06 July 2026 reached out to sean youngberg again for access to the "airs-workshop" project in GCP so I can work on the lab. | |
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16
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17
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18
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RECOMMENDED TRAINING ITEMS | |||||
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19
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🎓 | Agentic AI - DeepLearning.AI | 2 | intermediate | made the account here, it is a set of short videos. | |
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20
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📋 | ATLAS Matrix - PANW AI Security Solution Mapping | 1 | intermediate | ||
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21
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🎓 | Create agents in Microsoft Copilot Studio | 8 | intermediate | ||
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22
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📰 | Firewall for AI: Identifying Abuses Before They Reach the Model | 0.25 | intermediate | ||
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23
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🔧 | ✅ | Gandalf CTF - Prompt Injection Challenge | 2 | beginner | Did this one at the end of 2025 |
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24
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💻 | Generative AI for Beginners - Microsoft | 10 | beginner | ||
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25
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📰 | ✅ | Generative Red Team at DEF CON 31 - AI Village Recap | 0.5 | intermediate | Looks like a broken link: https://aivillage.org/defcon%2031/2023/10/12/generative-recap || 6 July 26: found another link to the same material: https://ksankar.medium.com/def-con-31-generative-ai-red-team-grt-for-llmsec-a0ce0cdbf6 |
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26
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📄 | Learn Prompting - Comprehensive Prompt Engineering Guide | 4 | beginner | ||
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27
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🔧 | ✅ | OpenAI Tokenizer Tool | beginner | this is a cool way to learn more about tokenization. | |
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28
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📄 | OWASP GenAI Incident Response Guide 1.0 | 2 | intermediate | ||
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29
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📊 | ✅ | Securing Generative AI - Overview Presentation (Dec 2024) | 0.5 | beginner | A simple summary of the main palo AI reference doc, which in turn links to the arch guide for AWS, Azure, and GCP. |
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30
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🎓 | Vector Databases: from Embeddings to Applications | 1.5 | intermediate | ||
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31
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32
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PROPOSED TRAINING ITEMS | |||||
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33
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Prompting guide 101 | beginner | ||||
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34
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yt video for intro to google "agent to agent" | beginner | ||||
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35
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yt video for intro to n8n | beginner | ||||
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36
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Google Skills | intermediate | ||||
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37
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https://huggingface.co/docs/tokenizers/quicktour | intermediate | 7 July 2026 - Build your own tokenizer! | |||
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38
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https://www.youtube.com/watch?v=zHvTiHr506c | advanced | Subword-based tokenizers: To tokenize Python efficiently, you should use subword or byte-level models. Code-specific tokenizers learn Python keywords, indentations, and special symbols natively. This stops the model from breaking code into awkward, random letters. It also saves memory and cost. | |||