AI Security Engineering · Module 00
AI, ML & LLM Foundations Workbook
Learner: ______________________________ Date: ________________
A. Concept relationship map
Define each term in one sentence and draw their relationship.
| Term | Course definition | Subset / contains / related to | Security-relevant example |
|---|---|---|---|
| Artificial intelligence | |||
| Machine learning | |||
| Deep learning | |||
| Generative AI | |||
| Foundation model | |||
| Large language model |
B. ML workflow
| Stage | Inputs | Outputs / artifact | Quality decision | Security concern |
|---|---|---|---|---|
| Collect / label | ||||
| Split / transform | ||||
| Train | ||||
| Validate / tune | ||||
| Test | ||||
| Deploy / monitor |
C. Neural-network trace
Input tensor:
Forward-pass transformations:
Output logits or values:
Target:
Loss:
Gradient:
Optimizer update:
Parameter:
Hyperparameters:
Generalization check:
Potential adversarial influence:
D. LLM request trace
| Step | Input / representation | Versioned component | Output | Telemetry |
|---|---|---|---|---|
| Messages / template | ||||
| Tokenization | ||||
| Embeddings / Transformer | ||||
| Logits / decoding | ||||
| Output processing |
E. Adaptation method selection
| Requirement | Prompt | RAG | Fine-tune / PEFT | Why |
|---|---|---|---|---|
| Current internal facts | ||||
| Consistent response format | ||||
| New domain behavior | ||||
| Per-user private data |
F. RAG trace
Source and owner:
Parser and transformation:
Chunking strategy:
Metadata and ACL inheritance:
Embedding model and version:
Index / vector store:
Query and identity:
Authorization filter:
Retrieval and reranking:
Selected chunk lineage:
Prompt augmentation:
Generation:
Citation validation:
Metrics:
Security boundaries:
G. Agent and MCP trace
| Component | Identity | Data / authority | Validation | Audit event |
|---|---|---|---|---|
| Host / application | ||||
| MCP client | ||||
| MCP server | ||||
| Tool | ||||
| Resource | ||||
| Memory | ||||
| Approval / execution |
H. Metric selection
| Decision | Metric | Dataset / traffic | Threshold | Failure cost | Limitation |
|---|---|---|---|---|---|
| Task quality | |||||
| Retrieval | |||||
| Security | |||||
| Performance / cost |
I. Terminology clinic
Rewrite this statement accurately using the course glossary:
“The AI learned our PDF during inference, stored it in its neural-network database, reasoned deterministically, and securely called the MCP API. The 95% accuracy score proves the agent is safe.”
J. Exit checklist
□ Concept map □ ML lifecycle □ LLM trace □ RAG trace
□ Agent/MCP trace □ Metrics □ Terminology correction □ Knowledge check