Establish safeguards to prevent personal data leakage through AI outputs
Establishing data segregation controls. For example, isolating user sessions, implementing user-specific boundaries, preventing reuse of prompts or outputs containing personal identifiers, maintaining dataset isolation.
Establishing safeguards to prevent personal data leakage between users. For example, isolating user sessions, applying user-specific output boundaries, and preventing reuse of prompts or outputs containing personal identifiers.
Documenting protection procedures and incident management. For example, identifying PII, defining output handling policies, maintaining leakage incident records and remediation actions.
Implementing output monitoring. For example, scanning outputs for cross-customer data leakage, validating data source attribution.
Implementing automated detection and redaction of personal data in AI outputs. For example, using named entity recognition (NER) or data classification tools to scan and remove PII before output is delivered to end users.
Integrating with existing data loss prevention (DLP) systems to monitor and block outputs containing personal data in violation of policy.
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."