About The Company
70 million customers globally across 35 countries
Operates over 5,000 applications
THE COMPANY
A major U.S. bank, one of the largest multinational financial services companies in the world, faced a common but critical challenge. With operations in 35 countries, serving over 70 million customers globally, this financial giant handles more than just large volumes of transactions—they manage over 5,000 internal applications that ensure their banking services run smoothly. As a leading mortgage originator and one of the top banks in the U.S., their success relies on maintaining a seamless and secure digital infrastructure.
Despite having a robust security team in place, the bank struggled with an overload of redundant alerts coming from multiple security tools. These alerts lacked the business context needed to effectively prioritize them, resulting in confusion, wasted resources, and slow responses. Silos between the security, DevOps, and IT teams made communication difficult. The constant flood of notifications created redundant tickets and miscommunication about which vulnerabilities truly needed attention. As a result, the bank was facing inefficiencies that put their security at risk, delaying remediation efforts and increasing operational costs.
With the ever-evolving nature of cybersecurity threats and the pressure to ensure the highest level of customer trust, the bank sought a solution that could streamline their operations and provide clear visibility into their security risks. Their goal was simple: reduce redundant noise, improve team communication, and establish clear priorities for remediation.
THE PROBLEM
AI adoption was accelerating across more than 25,000 employee endpoints. Business users and developers were installing copilots, local models, coding assistants, extensions, and autonomous agents faster than existing inventory and governance processes could track them. The bank was particularly concerned about Shadow Agents that could access source code, credentials, customer information, files, APIs, and MCP-connected tools without clear ownership or approved boundaries. Traditional endpoint visibility could identify software, but not an agent’s purpose, skills, permissions, connected resources, or intended actions. At the same time, the security team still needed to consolidate and prioritize application vulnerabilities across a large, complex AI technology stack.
THE SOLUTION
PointGuard AI delivered Unified Discovery across managed endpoints, development environments, AI applications, models, agents, and MCP connections. Deep endpoint inspection uncovered approved and unapproved AI tools, including Shadow AI and previously unknown agents. Each agent was added to a comprehensive Agent Registry with its owner, purpose, identity, capabilities, skills, permissions, tools, and connected enterprise resources.
Agentic Endpoint Security added policy enforcement close to the user and agent, protecting code, credentials, intellectual property, and regulated data while intercepting unsafe file, shell, network, package, and tool activity before execution. Unique agent identities established accountability, while the PointGuard AI MCP Security Gateway enforced delegated, on-behalf-of access and granular tool-level authorization.
As a supporting capability, PointGuard AI continued consolidating findings from application security scanners, adding business context, normalized scoring, deduplication, and automated workflows through Jira and ServiceNow Vulnerability Response.
Integrations
THE RESULTS
PointGuard AI gave us unified visibility across endpoints, plus the identity, data protection, and access controls to govern AI agents without slowing our teams or innovation.
Our expert team can assess your needs, show you a live demo, and recommend a solution that will save you time and money.
