Building a Secure AI Chatbot: Risk Mitigation and Best Practices

AI chatbots increasingly sit between users and core systems, but they introduce LLM-specific failure modes that can bypass traditional appsec controls. The article breaks down seven high-impact risks—non-compliance, prompt injection, weak API security, auth/authorization gaps, DoS/bot overload, supply-chain dependencies, and insecure self-hosting—and the business impact of data leakage and liability. It then outlines a build plan: secure SDLC with threat modeling and least privilege, security testing in CI/CD, adversarial testing, privacy-first data minimization plus encryption/anonymization, and ongoing pen tests/red teaming aligned with NIST SSDF and ISO/IEC 27001. CTOs should treat chatbots as production systems with explicit trust boundaries and measurable controls.