Establish criteria for selecting cloud provider, and circumstances for on-premises processing considering data sensitivity, regulatory requirements, security controls, and operational needs
Conducting deployment risk assessments. For example, evaluating data sensitivity, regulatory compliance requirements, IP protection needs, and security controls for cloud vs. on-premises AI processing.
Documenting decision criteria and rationale. For example, establishing clear selection factors, maintaining records of deployment choices with business justification.
Implementing deployment-appropriate security controls. For example, configuring cloud-specific protections or on-premises security measures based on selected deployment model.
Implementing hybrid deployment strategies. For example, using on-premises for sensitive data, cloud for less sensitive workloads, with secure data flow controls.
Establishing cloud vendor management procedures. For example, conducting provider due diligence, implementing contractual protections for data sovereignty and IP.
Reviewing deployment decisions when requirements change. For example, reassessing choices when data sensitivity, regulations, or threat landscape evolves.
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."