Secure the complete AI system

Analyze trust boundaries, identity, provenance, privacy, adversarial inputs, and evidence for security decisions.

10 published lessons1 module313 min of guided study
  1. 01
    Secure the complete AI systemresearch · 10 lessons

Follow the path or begin with a question. Every lesson is open.

Your notebook0 of 10 lessons completed

Your place, at your pace. Progress is optional and saved only in this browser when available.

Inside this module

Each lesson includes an interactive lab

Secure the complete AI system

Analyze trust boundaries, identity, provenance, privacy, adversarial inputs, and evidence for security decisions.

  1. 01

    Threat modeling AI systems

    Turn an AI architecture into explicit assets, adversary capabilities, trust boundaries, and testable security claims.

    30 min
  2. 02

    Workload identity and least privilege

    Connect short-lived service identities to resource authorization, credential lifetimes, and bounded operational authority.

    29 min
  3. 03

    Model artifact provenance

    Verify the identity and production history of model artifacts without confusing signatures with behavioral safety.

    29 min
  4. 04

    Prompt injection and trust boundaries

    Keep untrusted retrieved content from acquiring tool authority through an AI application's instruction channel.

    32 min
  5. 05

    Training data poisoning and backdoors

    Analyze data and model supply paths, targeted failures, and the limits of random audits when defending training pipelines.

    31 min
  6. 06

    Privacy and membership inference

    Evaluate what a model can reveal about training participation while accounting for base rates, access, and population mismatch.

    31 min
  7. 07

    Differential privacy and accounting

    Reason from neighboring datasets to sensitivity, randomized release, and cumulative privacy loss without confusing epsilon with an attack probability.

    35 min
  8. 08

    Confidential computing and attestation

    Understand protected execution, evidence freshness, and key release while keeping hardware trust separate from application correctness.

    32 min
  9. 09

    Abuse detection and rate controls

    Combine cost-aware admission, bounded bursts, and calibrated detection while respecting false positives and distributed enforcement limits.

    31 min
  10. 10

    Security evaluation and response

    Turn scoped assessments into reproducible evidence, release decisions, and practiced containment of AI-system incidents.

    33 min
All engineering modules