How Alice Enabled a Global Investment Management Firm to Launch a Compliant Customer-Facing AI App
A Fortune 100 financial services firm partnered with Alice to red team its customer-facing AI personal investment assistant before broad deployment. Operating under some of the most demanding regulatory requirements in financial services, the firm had developed internal guardrails that were restricting legitimate user interactions while leaving real vulnerabilities unaddressed. Alice ran targeted red teaming that exposed critical gaps, then built five custom policy guardrail models within 48 hours to close them. Enabling the firm to go live with high-precision enforcement, serving millions of investors worldwide without compromising on regulatory compliance or user experience.
How Alice Enabled a Global Investment Management Firm to Launch a Compliant Customer-Facing AI App
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Alice red-teamed an AI personal investment assistant pre-launch, identifying gaps in the firm's internally developed guardrails. Five custom guardrail models were built and deployed within 48 hours of findings, delivering high-accuracy detection with a false positive rate well under 1%, at low latency levels that preserve user experience. The engagement gave security, legal, and product stakeholders the evidence base required to approve production deployment with confidence.
Challenge
The firm was preparing to launch a customer-facing AI personal investment assistant, designed to guide millions of investors through account information, balance inquiries, and service questions - without crossing into personalized financial advice.
Operating in one of the most regulated industries in the world, the firm's policy enforcement requirements were exceptionally high. Internal guardrails had been developed to manage the boundary between helpful and legally problematic, but they were built on manual constraints that proved both too restrictive in some areas and insufficiently robust in others. The result was a high false positive rate: legitimate queries were being blocked while real attack paths went undetected.
Given the firm's specific and demanding security and compliance requirements, the assistant needed to be proven policy-compliant before it could reach the firm's full investor base.
How Alice Helped
Alice conducted targeted red teaming against the assistant's pre-launch architecture, going well beyond generic test sets to surface the specific vulnerabilities that matter in a financial services context: prompt injection, policy bypass, and financial advice elicitation.
Based on red teaming findings, Alice built five custom guardrail models within 48 hours, each tuned to the firm's deployment environment and risk profile. The result was enforcement precise enough to catch what mattered, without generating the false positives that degrade user experience at scale.
All models were delivered at sub-99ms latency, meeting the firm's explicit performance requirements for a production-grade, customer-facing product.
The Results
Red teaming surfaced critical gaps in the firm's existing guardrails that internal testing had not detected. Custom policy models addressed those gaps within 48 hours of findings, covering the firm's highest-priority risk categories with high accuracy and a false positive rate well under 1%.
Attack resistance improved significantly. Over-enforcement on benign queries dropped to near zero. Security, legal, and product stakeholders reached alignment on production readiness.
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