9.2 Secure AI Application Architecture¶

Role at Stage 9: AI Security, Blockchain, and ZKML¶

Apply ordinary AppSec and privacy controls to AI services and data flows. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>9.2</b><br/>Secure AI Application<br/>Architecture"]
  P --> S1["<b>9.2.1</b><br/>Authentication and<br/>Authorization"]
  P --> S2["<b>9.2.2</b><br/>Secrets and Service<br/>Accounts"]
  P --> S3["<b>9.2.3</b><br/>Dependency and Model<br/>Supply Chain"]
  P --> S4["<b>9.2.4</b><br/>Privacy PII and Data<br/>Retention"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
9.2.1 Authentication and Authorization	Authentication and Authorization is the working skill inside Secure AI Application Architecture that helps you build the stage artifact, A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo, while collecting enough evidence to trust the result.
9.2.2 Secrets and Service Accounts	Secrets and Service Accounts is the working skill inside Secure AI Application Architecture that helps you build the stage artifact, A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo, while collecting enough evidence to trust the result.
9.2.3 Dependency and Model Supply Chain	Dependency and Model Supply Chain is the working skill inside Secure AI Application Architecture that helps you build the stage artifact, A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo, while collecting enough evidence to trust the result.
9.2.4 Privacy PII and Data Retention	Privacy PII and Data Retention is the working skill inside Secure AI Application Architecture that helps you build the stage artifact, A threat model, red-team report, smart contract security lab, and tiny ZKML or verifiable computation demo, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Secure AI Application Architecture supports a threat model, red-team report, smart contract security lab, and tiny zkml or verifiable computation demo..
Build and inspect this artifact: Create an AI risk register and controls plan.
Measure progress with: Track severity, owner, mitigation, detection, and residual status.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Create an AI risk register and controls plan.

Measure: Track severity, owner, mitigation, detection, and residual status.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 9: AI Security, Blockchain, and ZKML.