AI Safety & Security
Defend AI systems: injection, jailbreaks, red-teaming, data handling, and governance.
For Security and compliance engineers · 8 articles · ~10.8h · 3 stages
This track assumes you already know
- 1
Attack surface
- Prompt InjectionInstructions and data arrive as the same thing. That is the vulnerability, it has no complete fix, and the defense is architectural.Security
- Jailbreaks and GuardrailsWhy safety training is a tendency rather than a rule, and how to build the enforcement layer that does hold.Security
- Red-Teaming AI SystemsAdversarial testing of your own system before someone else does it. What to try, how to organize it, and what to do with findings.Security
- 2
Content and data
- Content ModerationFiltering what goes in and what comes out. Where classifiers belong, and why over-blocking is as real a failure as under-blocking.Security
- Handling PII and Sensitive DataPrompts leave your infrastructure, logs persist, and models memorize. Three exposure paths and what to do about each.Security
- Model Supply Chain RiskDownloading weights runs someone else's file on your hardware. What can hide in a model, an adapter, or a tool server.Security
- 3
Governance and evaluation
- AI Governance and ComplianceThe documentation and process obligations around shipping AI. What holds regardless of jurisdiction, and what to write down.Security
- Human Evaluation and Annotation AgreementFor subjective quality, humans are the ground truth. But humans disagree, so you have to measure whether your labels are consistent enough to trust.Evaluation