“We significantly reduced AI security investigation time from hours to just minutes. Real-time monitoring has fundamentally changed how our team effectively responds to emerging threats.”
Michael Rodriguez
Director of Security, Vertex
01 // OVERVIEW
AI adoption outpaced security visibility
Vertex had rapidly expanded its use of AI across multiple business units. Internal teams were building copilots, integrating third-party models, and experimenting with autonomous workflows—often faster than security teams could track or govern them.
What began as isolated AI initiatives had evolved into a distributed ecosystem with limited visibility into what was running, who owned it, and how sensitive data was moving across systems.
THE CHALLENGE
Security teams lacked a unified view of AI systems operating across the organization.
02 // Objective
Create a single security layer for enterprise AI
Vertex needed to establish complete visibility across its AI environment while introducing consistent controls without slowing down AI adoption.
The objective was to create a scalable security model that could support both existing deployments and the next generation of AI workloads.
03 // Solution
From distributed AI to a controlled environment
Vertex deployed Secury as a centralized security layer across its AI ecosystem, connecting existing infrastructure without requiring teams to redesign their applications or workflows. The rollout focused on three core areas.
1. AI Asset Discovery
Secury continuously discovered AI models, applications, APIs, agents, and connected services across the organization.
The security team could identify previously unmanaged assets, surface ownership gaps, and establish a complete inventory of the enterprise AI environment.
2. AI Governance
Vertex introduced centralized policies for AI access, permissions, and workload ownership. Teams could apply consistent controls across different business units while maintaining flexibility for individual use cases.
3. Runtime Protection
Runtime controls were deployed to inspect AI interactions, detect abnormal behavior, and enforce policies around sensitive data access and high-impact actions.
Instead of relying solely on pre-deployment reviews, security teams gained continuous visibility into how AI systems behaved in production.
