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Coveo

Coveo builds an AI-powered relevance platform that injects intelligent search, personalized recommendations, and generative answering into digital touchpoints. The platform operates at the intersection of machine learning and natural language processing, processing user queries and content to deliver contextually appropriate results across e-commerce sites, customer service portals, and workplace intranets. From a security standpoint, the attack surface is significant: the system ingests and processes large volumes of enterprise data, user behavior signals, and content across multiple customer deployments. The platform's generative answering capabilities add another layer of complexity - prompt injection, data leakage through model outputs, and adversarial manipulation of search results are all in-scope threat vectors. Securing the ML pipeline itself, from training data integrity to model inference endpoints, is core infrastructure work rather than an afterthought. The technical stack spans NLP systems, search infrastructure, and AI model serving at scale. Security roles here would likely focus on securing multi-tenant architectures, protecting data flows between customer environments and Coveo's cloud platform, and hardening the machine learning lifecycle against both traditional and AI-specific threats. The company operates across multiple industry verticals - each with its own compliance and data handling requirements - meaning security engineering has to account for varied regulatory contexts simultaneously.

Coveo builds an AI-powered relevance platform that injects intelligent search, personalized recommendations, and generative answering into digital touchpoints. The platform operates at the intersection of machine learning and natural language processing, processing user queries and content to deliver contextually appropriate results across e-commerce sites, customer service portals, and workplace intranets.

From a security standpoint, the attack surface is significant: the system ingests and processes large volumes of enterprise data, user behavior signals, and content across multiple customer deployments. The platform's generative answering capabilities add another layer of complexity - prompt injection, data leakage through model outputs, and adversarial manipulation of search results are all in-scope threat vectors. Securing the ML pipeline itself, from training data integrity to model inference endpoints, is core infrastructure work rather than an afterthought.

The technical stack spans NLP systems, search infrastructure, and AI model serving at scale. Security roles here would likely focus on securing multi-tenant architectures, protecting data flows between customer environments and Coveo's cloud platform, and hardening the machine learning lifecycle against both traditional and AI-specific threats. The company operates across multiple industry verticals - each with its own compliance and data handling requirements - meaning security engineering has to account for varied regulatory contexts simultaneously.

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