Background
Challenge: Reliable and Responsible AI
- Ensuring Responsible AI: The GenAI system needed to align with ethical AI principles, regulatory requirements, and industry best practices to promote fairness and accountability.
- Securing Against Internal and External Threats–The system had to be resilient against adversarial attacks, data breaches, and manipulation attempts to safeguard customer data and operational integrity.
Why Vulcan?
As the first institution aiming to implement GenAI following the release of local AI guidelines, they faced the challenge of navigating an untested vendor landscape. Still, their requirements were clear: a partner with deep GenAI security expertise and a strong understanding of the local market. It ultimately chose Vulcan for the following reasons:
- Leading Solution –The first and only commercially available solution at that time capable of conducting comprehensive assessments on GenAI models.
- Financial Industry Expertise – In-depth knowledge of local regulations and compliance requirements for financial institutions, ensuring alignment with industry standards.
- Local Knowledge – Testing GenAI models requires a deep understanding of local language and culture, which Vulcan is well-equipped to handle.
Vulcan’s Assessment Process
The institution partnered with Vulcan to conduct a structured evaluation process, ensuring that the selected AI model met security and compliance requirements. The key steps included:
- Adversarial Testing – Simulated real-world attack scenarios to identify security vulnerabilities and assess system robustness.
- Fairness and Bias Assessment – Evaluated outputs from different foundation models to detect biases, ensuring equal treatment of customers from diverse backgrounds.
- Regulatory Compliance Mapping –Validated model alignment with local financial AI guidelines and evolving global AI governance frameworks.
Outcome
By working with Vulcan, the institution successfully:
- Understood Risks – Identified key security vulnerabilities, compliance gaps, and potential biases present in different foundation models.
- Selected a Proper Foundation Model – Chose the most suitable foundation model based on security posture and regulatory alignment.
- Ensured Compliance from the Beginning– Integrated security and regulatory considerations early in the development phase to mitigate risks before full-scale implementation.
Conclusion
As financial institutions continue to explore AI-driven innovations, this case highlights the critical role of security and compliance in foundation model selection, ensuring safe, responsible, and secure GenAI development from an early stage.
Explore how Vulcan conducts foundation model assessments here, or simply get in touch with us at contact@vulcanlab.ai.
Disclaimer: To maintain client confidentiality, some details in this case study have been modified or eliminated. However, the security challenges, testing methodologies, and key insights accurately represent real-world scenarios and best practices in GenAI security.
