AI chatbot security frameworks: guardrails, data, and monitoring

As AI chatbots connect to sales, support, and hiring workflows, LLM-specific attacks can create direct business exposure beyond traditional appsec. This webinar breaks down three recurring risk classes—prompt injection, data leakage (logs/APIs/integrations), and data poisoning—and why single controls fail in production. It proposes a layered framework: input/output guardrails, least-privilege access with strong authentication, encryption plus retention controls, and secure pipelines for training/feedback. It also outlines how to validate controls through adversarial testing, API/log inspection, and continuous monitoring with alerting and incident response. CTOs can use this to define trust boundaries and a minimum control set before scaling chatbots into sensitive processes.