AI security & guardrails for the product leader
Jailbreak, injection, extraction, and poisoning are four different attacks, not one — and why governance only counts once it becomes compliance evidence a regulator or an enterprise buyer can actually check.
An LLM mixes trusted instructions and untrusted data in one channel, and it's usually serving more than one customer from shared infrastructure. That combination produces attacks that don't exist in ordinary software, and defending against them takes more than a filter — it takes an architecture, and a paper trail proving the architecture works.
A note on scope. This is one of the most exhaustively covered topics in the curriculum, for the same reason evaluation and observability was. Safety engineering and Multi-tenant isolation already develop prompt injection, the lethal trifecta, data leakage, and cross-tenant isolation in full engineering depth. Safety, security & governance for agents already develops least privilege, sandboxing, human-in-the-loop approval, audit trails, and organizational governance in full. Re-deriving any of that here would only restate it. This module exists to answer the two questions those deep-dive lessons don't lead with: the full threat taxonomy guardrails have to cover — jailbreak, injection, extraction, and poisoning are four different attacks, not one — and how governance becomes compliance evidence a regulator or an enterprise buyer can actually check. That is why this module is two lessons, not seven.
The knowledge graph
Lesson 1 · The threat model — four distinct attack shapes
Jailbreak
Social-engineer the model out of its own refusals
Injection
Hide commands in data the model reads
Extraction
Pull training data, prompts, weights back out
Poisoning
Corrupt what the model learns in the first place
Guardrails · layered, fail-closed Defense
Each attack has its own countermeasure. When any layer is uncertain, block by default.
Red-teaming Evidence
Structured adversarial testing that generates evidence on purpose.
Governance & compliance Lesson 2
SOC 2 · GDPR/DPDP · EU AI Act · model cards — evidence someone outside can check.
The outcome Payoff
Enterprise procurement clears. Regulatory exposure is bounded.
Read it as one argument in two parts. The threat model: "guardrails" is not one control, it's a layered defense against four distinct attacks, each of which needs its own countermeasure and fails in its own way. Governance & compliance: the internal discipline of controlling an AI system only pays off in the moments that matter — a security review, a regulator's request — if it produces evidence someone outside the company can actually check.
The lessons
- The threat model, and guardrails as architecture — jailbreak vs. injection vs. extraction vs. poisoning, and why a guardrail system has to fail closed.
- Governance, audit & compliance — SOC 2, the EU AI Act's risk tiers, GDPR's automated-decision rules, and red-teaming as the practice that generates proof before an incident does.
Each lesson pairs the product framing with a 🎯 For the product leader briefing — why it matters, the decision it changes, the question to ask your team, and the risk if ignored — plus a diagram. For the engineering depth behind every mechanic mentioned here, follow the spokes into Safety engineering, Multi-tenant isolation, and Safety, security & governance for agents.
📌 Close out the module: Recap & real-world examples.