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RainFocus

RainFocus builds event marketing technology for enterprises, running a platform that unifies audience data and powers AI-driven agents under the product name Nexus . The tech stack spans data unification pipelines, AI-powered agents engineered to support event marketing workflows, and a deployment cadence that ships features weekly. That velocity means security teams here are defending a platform where sensitive attendee data, behavioral signals, and enterprise customer pipelines are constantly in motion. The attack surface isn't theoretical: Nexus agents ingest and act on real-time event data across enterprise deployments. Securing that pipeline - data at rest, in transit, and within AI agent execution contexts - is the core challenge. The company reports customers seeing 40% more pipeline through the platform, which means the data flowing through it is high-value and the stakes for a breach or data integrity failure are concrete. Operations span Lehi and London , with a remote-capable culture. The engineering org runs on a startup-velocity model: weekly releases, customer-outcome-driven metrics rather than internal vanity stats, and a team structure where individuals own their scope end to end. For security practitioners, that means embedding with engineering rather than gating from the outside - and dealing with the realities of AI agent security, not just perimeter defense.

RainFocus builds event marketing technology for enterprises, running a platform that unifies audience data and powers AI-driven agents under the product name Nexus. The tech stack spans data unification pipelines, AI-powered agents engineered to support event marketing workflows, and a deployment cadence that ships features weekly. That velocity means security teams here are defending a platform where sensitive attendee data, behavioral signals, and enterprise customer pipelines are constantly in motion.

The attack surface isn't theoretical: Nexus agents ingest and act on real-time event data across enterprise deployments. Securing that pipeline - data at rest, in transit, and within AI agent execution contexts - is the core challenge. The company reports customers seeing 40% more pipeline through the platform, which means the data flowing through it is high-value and the stakes for a breach or data integrity failure are concrete.

Operations span Lehi and London, with a remote-capable culture. The engineering org runs on a startup-velocity model: weekly releases, customer-outcome-driven metrics rather than internal vanity stats, and a team structure where individuals own their scope end to end. For security practitioners, that means embedding with engineering rather than gating from the outside - and dealing with the realities of AI agent security, not just perimeter defense.

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