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To strengthen AI governance by mitigating security risks, reducing bias, and ensuring regulatory compliance in financial services.
The AI systems were increasingly becoming targets for adversarial attacks. As the models processed vast amounts of sensitive financial data, there was a need to proactively safeguard them against malicious attempts to manipulate outputs or breach data privacy.
There were concerns regarding the potential biases in AI decision-making. With AI models relying on historical data, there was a risk that past biases could be perpetuated, affecting customer outcomes and regulatory compliance.
Given the sensitive nature of financial data, the client needed to ensure that their AI models were designed to protect customer privacy and comply with international data protection regulations, including GDPR.
The client was navigating an evolving regulatory landscape for AI, with new regulations and standards being introduced in different markets. Ensuring compliance while maintaining AI model transparency and accountability was a growing challenge.
VE3 developed an AI risk assessment framework tailored to the client’s needs, identifying potential risks related to security, fairness, and privacy.
The solution incorporated methods like input perturbation detection and secure model validation to reduce vulnerabilities in the model’s decision-making.
To address concerns about AI biases, VE3 implemented a comprehensive auditing process that assessed the fairness of the client’s models.
VE3 integrated privacy-preserving techniques, including differential privacy and homomorphic encryption, into the client’s AI models to safeguard sensitive customer data.
VE3 set up a continuous monitoring system to track the performance and security of the AI models in real-time.
The implementation of VE3’s proactive AI risk management solution delivered tangible results for the client:
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VE3’s AI risk management solution enabled the client to proactively safeguard their AI systems, mitigating security vulnerabilities, reducing bias, and ensuring compliance with data privacy regulations.
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