AIUC-1
B009

Limit output over-exposure

Implement output limitations and obfuscation techniques to reduce information leakage

Keywords
Output Obfuscation
Fidelity Reduction
Information Leakage
Adversarial Use
Response Filtering
Application
Mandatory
Frequency
Every 12 months
Type
Preventative
Crosswalks
AML-M0002: Passive AI Output Obfuscation
MEASURE 2.10: Privacy risk assessment
LLM02:25 - Sensitive Information Disclosure
LLM05:25 - Improper Output Handling
LLM08:25 - Vector and Embedding Weaknesses
LLM09:25 - Misinformation

Control activities

Reducing or limiting the number of results shown in outputs to relevant only to balance security and utility.

Limiting the output format to reduce exploitability. For example, disabling or redacting structured formats such as JSON, XML, or code snippets where not necessary, especially in externally facing outputs.

Filtering sensitive information that may reveal internal system behavior. For example, removing or abstracting technical details about model architecture, prompt structure, or tool invocation logic.

Providing user-facing notices or documentation about output limitations. For example, clearly indicating when results have been truncated, rounded, or suppressed to align with security and privacy safeguards.

Limiting the fidelity of numerical outputs in certain use cases. For example, applying output rounding, threshold bands, or obfuscation techniques to reduce the risk of model inversion or precision-sensitive data disclosure.

Organizations can submit alternative evidence demonstrating how they meet the requirement.

AIUC-1 is built with industry leaders

Phil Venables

"We need a SOC 2 for AI agents— a familiar, actionable standard for security and trust."

Google Cloud
Phil Venables
Former CISO of Google Cloud
Dr. Christina Liaghati

"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."

MITRE
Dr. Christina Liaghati
MITRE ATLAS lead
Hyrum Anderson

"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."

Cisco
Hyrum Anderson
Senior Director, Security & AI
Prof. Sanmi Koyejo

"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."

Stanford
Prof. Sanmi Koyejo
Lead for Stanford Trustworthy AI Research
John Bautista

"AIUC-1 standardizes how AI is adopted. That's powerful."

Orrick
John Bautista
Partner at Orrick and creator of the YC SAFE
Lena Smart

"An AIUC-1 certificate enables me to sign contracts must faster— it's a clear signal I can trust."

SecurityPal
Lena Smart
Head of Trust for SecurityPal and former CISO of MongoDB
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