Discover how a global insurance leader deployed HiddenLayer's AI Security Platform to protect sensitive data and scale AI safely.

Customer Overview

A leading global insurance provider delivers comprehensive coverage solutions, benefits administration, and risk management services to organizations worldwide. Operating across multiple highly regulated markets, the company manages sensitive financial, healthcare, and personal information on behalf of millions of customers and policyholders.

As part of its broader digital transformation strategy, the organization has invested heavily in artificial intelligence to improve underwriting decisions, streamline claims processing, enhance fraud detection, personalize benefits recommendations, and support customer service operations. AI adoption accelerated rapidly across business units, creating new opportunities for efficiency and innovation while increasing the need for centralized governance and risk management.

Recognizing both the business value and security implications of enterprise AI, leadership sought a scalable approach to ensure AI systems could be deployed responsibly without slowing innovation.

The Challenge

As AI adoption expanded across the organization, security and compliance teams faced increasing visibility challenges. Employees and business units were increasingly leveraging AI-powered tools to improve productivity and decision-making, yet there was no consistent mechanism to monitor how AI systems were being used or what information was being shared with them.

The organization lacked centralized oversight of AI interactions, potentially exposing it to risks that traditional security tools were not designed to address. Sensitive policyholder information, protected health information (PHI), and proprietary business data could potentially be entered into AI systems without appropriate controls or visibility. At the same time, security teams had limited ability to identify prompt injection attacks, adversarial inputs, or other attempts to manipulate model behavior.

These challenges were compounded by the pace of AI adoption. New tools and use cases were emerging faster than manual governance processes could keep up, creating inconsistencies in how AI was used across departments and increasing compliance concerns related to HIPAA, state insurance regulations, and emerging AI governance requirements.

Without a centralized control point, the organization risked losing visibility into its expanding AI ecosystem just as AI became increasingly critical to business operations.

The Solution

To address these challenges, the organization deployed HiddenLayer’s AI Security Platform and runtime security as a centralized security and governance layer across its AI ecosystem.

HiddenLayer was implemented through an inline API gateway architecture positioned between employees and the AI systems they accessed. This approach enabled every AI interaction to be inspected in real time, regardless of the underlying model or applicationor, application, or vendor.

By creating a single enforcement point for AI security policies, the organization gained consistent oversight across business units without requiring individual teams to implement controls within each AI application. Employees continued using their existing AI tools and workflows without disruption, while security and compliance teams gained the visibility needed to govern AI usage at enterprise scale.

HiddenLayer’s Runtime Security module continuously analyzes prompts and model responses to identify threats before they can impact users or business operations. Real-time inspection capabilities helped detect prompt injection attempts, adversarial inputs, sensitive data exposure, and policy violations before requests reached AI systems or responses were delivered to end users.

The deployment also provided centralized governance controls, allowing security and compliance teams to enforce uniqueconsistent policies per AI application across the organization from a single platform.

The company selected HiddenLayer because of its purpose-built approach to AI security. Unlike traditional cybersecurity solutions adapted for AI environments, HiddenLayer was designed specifically to address the unique risks associated with agentic and generative AI systems. Equally important, the platform could operate at enterprise scale without introducing meaningful latency or disrupting user productivity.

Throughout the implementation, HiddenLayer worked closely with security, compliance, and IT stakeholders to ensure the deployment aligned with existing regulatory obligations and internal governance requirements while supporting the organization's long-term AI strategy.

Results

The deployment of HiddenLayer’s AI Security Platform provided the organization with the visibility, control, and governance capabilities needed to support enterprise-wide AI adoption securely.

For the first time, security teams gained a centralized, real-time view of AI activity across business units, replacing a fragmented landscape of disconnected tools and workflows with a single governance framework. This visibility eliminated critical blind spots and provided a consistent mechanism for monitoring AI interactions throughout the organization.

The implementation also significantly reduced the risk of sensitive data exposure. By inspecting inputs and outputs in real time, HiddenLayer helped prevent the inadvertent exposure of policyholder information, protected health information, and proprietary business data through AI interactions.

At the same time, the organization was able to expand AI usage without increasing operational burden on security teams. Governance controls scaled automatically alongside AI adoption, enabling business units to continue pursuing new AI initiatives while maintaining appropriate oversight and risk management.

The deployment further strengthened the organization's compliance posture by providing centralized logging, policy enforcement, and audit-ready records of AI activity across multiple jurisdictions. Security, compliance, and business stakeholders gained a shared framework for managing AI risk, improving collaboration, and accelerating the responsible adoption of AI technologies across the enterprise.

Key Takeaways

This engagement demonstrated that effective AI governance does not require organizations to choose between innovation and security. By implementing a centralized security layer across its AI ecosystem, the organization was able to expand AI adoption while maintaining consistent oversight and control.

The project also highlighted the importance of visibility as a foundational requirement for responsible AI use in regulated industries. Real-time monitoring of AI interactions provided security and compliance teams with the context needed to identify risk, enforce policy, and demonstrate governance without creating friction for employees.

Perhaps most importantly, the deployment reinforced that AI-specific threats require AI-specific defenses. Risks such as prompt injection, jailbreak attempts, unsafe model behavior, AI supply chain vulnerabilities, and the misuse of AI systems introduce challenges that traditional security tools were not designed to address. Organizations seeking to scale AI responsibly need purpose-built security capabilities that provide protection across the AI lifecycle, from model sourcing and deployment to runtime monitoring and governance.

Looking Ahead

Building on the success of the initial deployment, the organization plans to expand AI security coverage across additional applications and business units as AI adoption continues to grow.

Future initiatives include extending runtime security to new generative AI use cases, strengthening governance controls for emerging AI technologies, and enhancing visibility across the broader AI lifecycle. The organization is also evaluating additional safeguards for its AI supply chain, including validating and monitoring third-party and open-source models before deployment, to help ensure security remains embedded throughout every stage of the AI lifecycle.