The Customer Conversation
| Customer Stage | What They Say | What They Need |
|---|---|---|
| Unaware | "Our chatbot is internal, so it's fine" | Education: internal users can still inject prompts and extract data |
| Concerned | "Security team blocked our AI project" | Solution: guardrails enable safe deployment |
| Building | "We're building a customer-facing LLM app" | Technical: inline scanning, latency guarantees, compliance |
| Post-incident | "Someone jailbroke our chatbot" | Urgency: deploy now, show the attack would have been caught |
Competitive Differentiation
| Competitor Approach | Limitation | CP Advantage |
|---|---|---|
| Cloud LLM safety filters | Only protect their own models | Works with any LLM — cloud, on-prem, hybrid |
| Generic WAF rules | Can't parse semantic intent | Purpose-built NLP classifiers for LLM attacks |
| Manual prompt engineering | Easily bypassed | Automated inline scanning |
| Open-source (LLM Guard) | No console, no support | Managed product in Infinity Platform |
Demo Script (5 min)
- Explain the attack surface — "Every LLM app accepts natural language input. That input can contain hidden instructions."
- Run a prompt injection — show the AI following injected instructions without guardrails
- Show Lakera detection — the guardrails catch and classify the attack
- Run a data extraction attempt — trying to extract the system prompt
- Show the audit log — "Every scan is logged. This is your compliance evidence."
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Think Deeper
Try this:
A customer says 'We already have DLP — why do we need AI-specific security?' How do you respond?
DLP catches sensitive data patterns (credit card numbers, SSNs) in transit. Prompt injection is not a data pattern — it's a semantic attack using natural language. 'Ignore previous instructions' contains no regulated data. DLP and AI Guardrails solve different problems: DLP protects data leaving the org, guardrails protect the AI application itself.
Key insight: Demo over deck. Pull out a laptop and show something working.
The Lakera-Demo is more compelling than any PowerPoint. You can explain how the classification
and embedding-based detection actually works because you built those components yourself.