Fintech Leader Establishes AI Security & Governance

This financial technology leader provides software platforms and services with over 2,000 global customers and 10,000 employees. Operating multiple business units catering to diverse financial products, the organization plays a pivotal role in the financial services industry, delivering cutting-edge technology to streamline operations for its clients worldwide.

As the company dives into adopting Generative AI (GenAI) and large language models (LLMs), its AI Governance working group faced significant hurdles. This group, comprising representatives from Security, Legal, Enterprise Risk, and Data Governance, needed to address a fragmented AI environment. Each business unit autonomously established their LLM pipelines on Azure Cloud, leading to widespread challenges:

1. Visibility and Discovery: The organization struggled to discover all Azure AI services and Databricks instances deployed across business units. This lack of visibility hampered their ability to manage and govern their AI operations effectively.
2. Risk Management: Experimenting with multiple open-source models early in the development lifecycle raised concerns about security, licensing, and operational risks. The company needed mechanisms to assess and mitigate these risks proactively.
3. Resource Mapping and Hardening: Creating a unified inventory of MLOps resources and mapping these to AI projects proved daunting. Identifying misconfigurations within the MLOps platform and implementing robust security measures were critical to ensuring compliance and operational stability.
4. Model Validation: The governance group sought rigorous testing frameworks to evaluate models against security and responsible AI standards, an essential step for safeguarding sensitive financial data and adhering to industry regulations.

To address these challenges, the company implemented the PointGuard AI platform, integrating seamlessly with their existing AI ecosystem, which included Azure Databricks, Azure OpenAI, Jira, and Microsoft Teams. The solution unfolded in three key phases:

1. Discovery and Inventory Management: PointGuard AI enabled the customer to automatically discover all Azure AI services, Databricks instances, and MLOps resources across business units. This centralized visibility streamlined governance and provided a comprehensive inventory of resources linked to specific AI use cases.
2. Risk Assessment and Governance: Leveraging PointGuard's security and operational risk assessment tools, the organization could evaluate open-source models and other resources against licensing and compliance benchmarks. The platform's automated alerts helped detect misconfigurations early, ensuring a secure and efficient development pipeline.
3. Testing and Validation: The platform's model-testing capabilities incorporated security and responsible AI frameworks into the development lifecycle. PointGuard provided detailed insights, enabling teams to test models for bias, security vulnerabilities, and adherence to governance policies before deployment.

The decision to adopt PointGuard AI was driven by its unmatched capabilities to address the company's intricate AI governance challenges. Key differentiators that set PointGuard apart include:

1. Most Complete AI Security Platform: PointGuard stood out as the industry’s most comprehensive AI security solution, encompassing all aspects of AI governance. From discovery and inventory to testing and compliance, PointGuard provided end-to-end support, ensuring the organization’s AI initiatives met the highest standards of security and governance.
2. Seamless Integration with MLOps Platforms and Lifecycle: PointGuard integrated effortlessly with the company’s existing AI infrastructure, including Azure Databricks, Azure OpenAI, and other MLOps tools. This seamless alignment allowed the governance team to incorporate security and compliance checks naturally into the development lifecycle, reducing friction and promoting consistent adherence to best practices.
3. Visibility into AI Supply Chains: A standout feature of PointGuard was its ability to offer unparalleled visibility into the organization’s AI supply chain. By mapping resources, dependencies, and use cases across business units, the platform enabled the company to track AI assets comprehensively, ensuring accountability and mitigating supply chain vulnerabilities.
4. Robust Platform for Managing and Remediating Security Issues: PointGuard’s advanced risk detection and remediation tools empowered the governance team to identify security issues promptly and address them efficiently. The platform provided actionable insights and automated workflows to correct misconfigurations, enforce security policies, and mitigate vulnerabilities, strengthening the organization’s AI security posture.

These differentiators made PointGuard AI the ideal choice for navigating the complex demands of AI governance in a decentralized, high-stakes environment.