Establish and communicate AI input data policies covering how customer data is used for model training, inference processing, data retention periods, and customer data rights
Defining data usage policies. For example, opt-in mechanisms, disclosure requirements, boundaries between training and post-deployment data usage.
Implementing data retention and deletion procedures. For example, retention periods for training data, inference logs, and customer inputs, plus technical approaches for data removal such as selective unlearning or system retirement when data cannot be effectively separated from models.
Documenting how data retention periods are calculated and justified.
Documenting data subject rights processes. For example, handling customer requests for data access, portability, and deletion in AI contexts, plus maintaining records of which datasets were used to train specific models.
Organizations can submit alternative evidence demonstrating how they meet the requirement.
"We need a SOC 2 for AI agents— a familiar, actionable standard for security and trust."
"Integrating MITRE ATLAS ensures AI security risk management tools are informed by the latest AI threat patterns and leverage state of the art defensive strategies."
"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."
"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."
"AIUC-1 standardizes how AI is adopted. That's powerful."
"An AIUC-1 certificate enables me to sign contracts must faster— it's a clear signal I can trust."