Co-authored by Sabeen Malik and Laura Ellis
In the evolving world of artificial intelligence (AI), keeping our customers secure and maintaining their trust is our top priority. As AI technologies integrate more deeply into our daily operations and services, they bring a set of unique challenges that demand a robust management strategy:
The Black Box Dilemma: AI models pose significant challenges in terms of transparency and predictability. This opaque nature can complicate efforts to diagnose and rectify issues, making predictability and reliability hard to achieve.Model Fragility: AI's performance is closely tied to the data it processes. Over time, subtle changes in data input—known as data drift—can degrade an AI system’s accuracy, necessitating constant monitoring and adjustments.Easy Access, Big Responsibility: The democratization of AI through cloud services means that powerful AI tools are just a few clicks away for developers. This ease of access underscores the need for rigorous security measures to prevent misuse and effectively manage vulnerabilities.Staying Ahead of the Curve: With AI regulation still in its formative stages, proactive development of self-regulatory frameworks like ours helps inform our future AI regulatory compliance frameworks; but most importantly, it builds trust among our customers. When thinking about AI’s promises and challenges, we know that trust is earned. But that trust is also is of concern for global policymakers, and that is why we are looking forward to engaging with NIST on discussions related to the AI Risk Management, Cyber Security, and Privacy frameworks. It’s also why we were an inaugural signer of the CISA Secure by Design Pledge to demonstrate to government stakeholders and customers our commitment to building things and understanding the stakes at large.Our TRiSM (Trust, Risk, and S ..
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