Privacy Engineering with AI and ML Corporate Training

Evaluate AI Privacy Risks Through Threat Modeling and Tested Feature Controls

Edstellar develops privacy engineering judgment for ML, LLM, and agent features. Participants explore differentially private training, threat-model an AI application, and build RAG privacy controls. Controlled testing and a final feature review connect technical evidence with governance decisions, emphasizing privacy-utility trade-offs, documented findings, and defensible remediation rather than blanket privacy claims.

1. Step 01

   Consult

   We review AI feature responsibilities and ML or security familiarity to identify gaps in privacy threat modeling, data exposure controls, or evaluation of protective techniques.

2. Step 02

   Plan

   We introduce AI-specific privacy risks before privacy-preserving training, threat modeling, LLM and agent guardrails, then controlled privacy testing and the complete feature review.

3. Step 03

   Align

   We align AI privacy and security engineering expertise with participant readiness, confirming isolated sample applications and clear authorization boundaries for privacy-testing exercises.

4. Step 04

   Customize

   We tailor synthetic ML data and sample RAG scenarios to familiar feature architectures, including redaction, access, and logging questions without exposing personal training records.

5. Step 05

   Execute

   Participants compare privacy and utility, threat-model LLM features, and test guardrails, receiving feedback on control limitations, supported findings, and the prioritization of remediation.

6. Step 06

   Evaluate

   We assess the AI feature privacy review for evidence-backed threats, measured trade-offs, tested controls, and a clear account of residual risks and governance responsibilities.