US Automotive Finance Provider

About the Customer

The lender is an ambitious adopter of AI tools and large language models (LLMs). At the time of the engagement, the company was rolling out around seven AI tools across its security, development, and QA teams.

The customer — referred to here as “the lender” — is a US-based automotive finance company that provides indirect lending solutions to dealers and consumers across the country. With a business built on processing sensitive financial and personal information, the lender places a strong emphasis on cybersecurity, compliance, and operational resilience. To strengthen its security posture and enhance visibility across its digital environment, the lender partnered with Darktrace to proactively detect emerging threats, protect critical customer data, and support secure business growth in an increasingly complex cyber landscape.

Customer Challenge

The lender was eager to adopt AI workflows across all of its platforms and highly motivated to understand how AI was actually being used. The company wanted to be confident that its adoption tools were effective and looked to / SECURE AI to validate that its strategy was heading in the right direction.

A particular concern for the lender was the risk of misapplication — something the company had experienced before when tools were adopted that did not provide much benefit.

Partner Solution

The lender deployed Darktrace / SECURE AI and quickly gained visibility into per-user activity, spanning the agents built into its platforms. Sessions showed last-seen dates and categorized activity using tags, allowing the lender to see, for example, whether users had been using agents for content generation, HR administration tasks, or coding.

Darktrace / SECURE AI is a SaaS platform, hosted on AWS and running as containerized workloads on Kubernetes, that gives enterprises unified monitoring and detection across the enterprise AI services used throughout their business. The platform ingests generative-AI telemetry from a wide range of enterprise AI tools — including Amazon Bedrock, Microsoft Copilot, Claude Enterprise, ChatGPT Enterprise, and SASE platform logs — and applies Darktrace’s Adaptive AI to detect behavioral anomalies across AI interactions. For AWS, SECURE AI integrates natively with Amazon Bedrock in the customer’s tenant, analyszng model-invocation logs from the foundation models available through it (such as Amazon Nova and Anthropic Claude). The SECURE AI policy-compliance engine calls the Amazon Nova Lite model via the Bedrock Converse API to summarize uploaded policy documents into compliance criteria and evaluate whether prompts comply — with those documents stored in Amazon S3. The platform is secured with standard best-practice AWS-native controls.

Results and Benefits

Almost immediately after collating data from / SECURE AI, the lender had visibility into per-user AI activity across its platforms. The company noted that the real-time telemetry was superior to its existing security stack and filled in gaps in its intelligence.

The lender plans to use these insights to decide which tools are most effective for its employees and to instigate wider discussions about AI adoption. The visibility provided by / SECURE AI is also seen as valuable in helping the company prevent the misapplication of tools that it had experienced previously.

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