Traditional software governance is no longer sufficient in the fast-paced world of AI, where machine learning models can change in real-time, potentially leading to hundreds of bad decisions before an issue is discovered. To address this, organizations must adopt a continuous, integrated compliance process, or an “audit loop,” that operates in real-time alongside AI development and deployment.
This approach involves implementing shadow mode rollouts, where new AI systems are deployed in parallel with existing systems, receiving real production inputs but not influencing real decisions or user-facing outputs. This allows teams to discover problems early by comparing the shadow model’s decisions to expectations. Additionally, teams must set up monitoring signals and processes to catch issues such as data or concept drift, anomalous or harmful outputs, and user misuse patterns. For instance, Nvidia, Ring, and OpenAI are examples of companies that may benefit from this approach, although they are not explicitly mentioned in the context of this article.
Audit logs are also crucial in continuous AI compliance, providing a permanent, detailed record of every important action and decision made by the AI, along with the reasons and context. These logs should be well-organized, difficult to change, and protected by access controls and encryption. By continuously monitoring and reacting to drift and misuse signals, companies can transform compliance from a periodic audit to an ongoing safety net, catching and addressing issues in hours or days, rather than months.
The implementation of an “audit loop” may seem like extra work, but it enables faster and safer AI delivery. By integrating governance into each stage of the AI lifecycle, organizations can move quickly and responsibly, catching issues early and avoiding major failures. This approach can also accelerate innovation, as developers and data scientists can iterate on models without endless back-and-forth with compliance reviewers. Ultimately, continuous AI compliance can unlock AI’s potential in important areas like finance, healthcare, and infrastructure, while ensuring safety and values are protected.
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