Two Checkpoints
AI Guardrails operates as an inline scanning layer with two checkpoints:
| Checkpoint | What It Scans | What It Catches |
|---|---|---|
| Inbound (user → LLM) | User's prompt before it reaches the LLM | Prompt injection, jailbreak attempts, prohibited topics |
| Outbound (LLM → user) | LLM's response before the user sees it | Hallucinated content, data leakage, toxic output |
Detection Methods
| Method | How It Works | Latency |
|---|---|---|
| Pattern matching | Known attack signatures and templates | 1–5 ms |
| NLP classification | ML models trained on attack datasets | 5–20 ms |
| Semantic analysis | Embedding-based comparison to known attacks | 10–30 ms |
| Contextual analysis | Full conversation context, catches multi-turn attacks | 15–40 ms |
Total end-to-end: ~20–50 ms — invisible compared to LLM generation time (500ms–3s).
Connection to What You Know
- Stage 1 (Classification) — attack detection is a classification problem: safe, injection, jailbreak, toxic
- Stage 4 (Embeddings) — semantic analysis uses the same embedding + cosine similarity approach from RAG
- Stage 1 (Evaluation) — the same precision/recall tradeoff applies: too aggressive = blocks legitimate prompts, too permissive = misses attacks
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Think Deeper
Try this:
Your guardrails add 30ms latency. A customer says that's too slow. How do you respond?
30ms is invisible to users — LLM generation itself takes 500ms–3s. The guardrails latency is less than 5% of total response time. Compare: a WAF adds 1–5ms but can't detect prompt injection at all. The tradeoff is 30ms of latency for catching attacks that no other security layer can.
Key insight: Guardrails use the same ML techniques you've learned throughout this program.
Classification, embeddings, semantic similarity — you can explain how they work, not just that they work.
That technical depth is your competitive advantage.