AIUC-1
B009

Limit output over-exposure

Implement output limitations and obfuscation techniques to safeguard against 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
AIS-09: Output Validation
Reducing or limiting the number of results shown in outputs to relevant only to balance security and utility. For example, character limits, limits on inference time.
B009.1 Config: Output volume limits

Screenshot of code or configuration implementing output restrictions - may include character or token limits, inference time limits, result count restrictions, or timeout configurations preventing excessive output. Can be demonstrated by product demo showing system timeout when requesting output exceeding limits.

Engineering CodeProduct
Text-generationVoice-generation
Providing user-facing notices or documentation about output limitations.
B009.2 Demonstration: User output notices

Screenshot of product interface showing user notices about output limitations - may include messages indicating truncated or suppressed outputs for security or privacy reasons, user documentation explaining limitation policies, or help articles describing output restrictions.

Product
Text-generationVoice-generation
Limiting the fidelity of model outputs in certain use cases. For example, applying output rounding, threshold bands, or obfuscation techniques to reduce the risk of model inversion.
B009.3 Config: Output precision controls

Screenshot of code implementing output fidelity limitations - may include rounding logic for numerical outputs, threshold bands reducing precision, or obfuscation techniques preventing model inversion, precision-sensitive data disclosure, or adversarial model extraction attacks.

Engineering Code
Text-generationVoice-generation

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
Lena Smart

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

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