The business case for gated intake
SMBs are adopting AI quickly, but trust is still fragile. In August 2025, the U.S. Chamber reported that 58% of small businesses were already using generative AI tools. Demand is real, but open public chat endpoints can create cost and abuse exposure if they are not controlled.
Why public chat can become a cost leak
An unprotected endpoint is effectively a free compute interface for bots, scrapers, and low-intent traffic. You pay for tokens while your team triages noise.
Practical controls:
Security guidance to anchor implementation
NIST AI RMF emphasizes governance, measurement, and risk management in AI systems. OWASP also calls out prompt injection, insecure output handling, and excessive agency as major LLM risks. Intake architecture should minimize attack surface before adding autonomous behavior.
Recommended launch model
Start with a secure, structured intake flow that stores and routes requests to a human-reviewed queue. Add model-generated classification/summarization later, behind feature flags and budget caps.
Sources
- Challenge every new session with CAPTCHA (for example, Cloudflare Turnstile).
- Apply per-IP, per-session, and burst rate limits.
- Use short-lived signed session tokens.
- Require structured intake steps before any model invocation.
- [U.S. Chamber (Aug 18, 2025)](https://www.uschamber.com/small-business/small-businesses-embrace-ai-to-stay-competitive-while-building-trust)
- [NIST AI Risk Management Framework](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10)
- [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/llm-top-10/)