Securing a Growing AI Infrastructure for Travel Technology Leader

A global travel technology company was accelerating the buildout of its AI infrastructure to support model routing, agent-driven workflows, and broader AI-enabled business operations. As the organization scaled its use of models, tools, and connected services, it needed stronger control over how those systems were discovered, governed, and secured in production.

This was not a narrow proof-of-concept environment. The customer was already working across AWS Bedrock with a meaningful number of active models and MCP servers, while also planning for future expansion into Vertex AI, Snowflake Cortex, and Salesforce AgentForce. That combination created an urgent need for a security and governance layer that could keep pace with a dynamic AI stack rather than treating each platform or model in isolation.

The company’s main challenge was operational complexity. AI adoption was moving quickly, but the environment itself was becoming harder to secure consistently. With roughly 10 models, 50 MCP servers, and an agent/client architecture already in scope, the organization needed a clearer understanding of how models, tools, and applications were connected—and where security controls should be enforced first.

The customer was also evaluating multiple layers of the AI stack at once. Discovery mattered, but so did runtime protection, MCP governance, and broader platform control. This meant the team was not simply looking for point visibility into AI assets. It needed a broader way to secure model-driven applications, protect agent workflows, and govern an environment where AI infrastructure was likely to expand across several cloud and SaaS ecosystems.

There was also a commercial and process challenge. The buyer wanted a modular breakdown of pricing and licensing rather than a single bundled quote, indicating that internal stakeholders were likely assessing different capabilities independently. They asked for per-module estimates, descriptions of each module, and licensing detail so they could evaluate the solution in a more structured way and move it through procurement.

To address these needs, PointGuard AI positioned its AI Security & Governance platform as a modular control layer for the customer’s evolving AI environment. Rather than forcing the customer into a monolithic adoption path, the proposal gave the team a way to evaluate discovery, MCP controls, runtime protection, and governance as connected capabilities that could also be assessed independently.

Centralized AI Discovery and Visibility

The first need was visibility. In an environment with multiple models, a sizable MCP footprint, and an agent-oriented architecture, PointGuard AI provided a way to create a clearer view of the AI ecosystem. This included understanding what models were in use, how they were being accessed, which applications and services depended on them, and where governance attention should be focused as the environment grew.

MCP Security and Agent Ecosystem Governance

MCP was a central theme in the deal. With approximately 50 MCP servers in the environment, the customer needed confidence that agent and tool interactions could be governed consistently rather than managed as fragmented, tool-by-tool exceptions. PointGuard AI’s value here was in helping establish stronger security and control around model-to-tool interactions, agent access patterns, and the broader operational boundaries around AI-enabled workflows.

Runtime Protection for AI Applications

The environment also required runtime protection. The customer was building toward a model router and agent ecosystem that would operate in real business workflows, which raised the stakes for production controls. PointGuard AI’s runtime capabilities were relevant because they help security teams monitor live AI interactions, detect risky behavior, and apply guardrails where AI systems are actively being used—not just during design reviews or offline testing.

Modular Adoption Across a Multi-Platform AI Roadmap

One of the strongest signals in the engagement was the customer’s request for detailed module-level pricing and licensing information. That pointed to a need for flexibility in how the platform could be adopted. PointGuard AI was able to support that evaluation by breaking the solution into understandable components, enabling the customer to assess what to prioritize first while preserving a broader end-state vision across Bedrock, Vertex AI, Snowflake Cortex, Salesforce AgentForce, and adjacent enterprise systems.

Integration into Existing Security Operations

The proposed solution also aligned with the customer’s existing enterprise architecture. The quote explicitly referenced integration with IAM, ticketing, SIEM, and additional systems, which reinforced the value of PointGuard AI as a platform that could fit into established security operations rather than creating an isolated new workflow. That kind of integration is essential when AI security needs to become part of day-to-day governance and remediation processes rather than remaining a side project.

• A path to govern a live AI environment spanning approximately 10 models and 50 MCP servers.
• Stronger visibility into a growing AI infrastructure built around models, agents, and model-connected workflows.
• A modular platform evaluation process that helped the customer move capabilities through internal review and procurement.
• Runtime and MCP-oriented controls aligned to the customer’s evolving model router and agent ecosystem.
• Integration potential with enterprise security operations through IAM, ticketing, and SIEM connectivity.