Blip operates an Intelligent Conversational Platform that companies use to build AI-powered conversational experiences across the customer journey. The platform facilitates communication on major messaging channels - including WhatsApp, Instagram, and Messenger - and exposes over 90 open extensions and APIs for integration. The technical stack sits at the intersection of conversational AI, chatbot development, and system integration, with presence spanning 32 countries and billions of messages transmitted through the infrastructure.
That kind of scale presents a distinct threat surface. Conversational AI platforms handle sensitive customer interactions across banking, retail, logistics, and consumer goods verticals - domains where data exfiltration, prompt injection, and API-layer abuse are live concerns. The security challenge here isn't theoretical: billions of messages flowing through integrated endpoints means the attack surface is the platform itself, from the AI model layer through to third-party integrations.
For security practitioners, the draw is operational complexity with real stakes. You're not defending a static perimeter; you're securing a distributed, multi-channel conversational system where the payload is user-generated content, the integrations are numerous by design, and the trust boundary between AI logic and external services has to hold at scale across dozens of countries and regulatory regimes.
