AIUC-1
D001

Prevent hallucinated outputs

Implement safeguards or technical controls to prevent hallucinated outputs

Keywords
Hallucinations
Technical Controls
Application
Mandatory
Frequency
Every 12 months
Type
Preventative
Crosswalks
MEASURE 2.5: Validity and reliability
LLM05:25 - Improper Output Handling
LLM09:25 - Misinformation
AIS-09: Output Validation
MDS-10: Model Continuous Monitoring
MDS-11: Model Failure
Implementing factual accuracy controls. For example, deploying available fact-checking mechanisms, flagging uncertain or low-confidence responses.
D001.1 Config: Groundedness filter

Screenshot of code or configuration showing groundedness validation - may include filters checking responses against source documents, fact-checking API integration, or logic comparing generated content to retrieved context for factual accuracy.

Engineering Code
Text-generationVoice-generation
Establishing information source validation. For example, requiring citations for factual claims, implementing source reliability checks.
D001.2 Demonstration: User-facing citations & source attributions

Screenshot of UI or output format showing citations and source attributions provided to users - may include inline citations, source links, reference lists, or attribution labels identifying where information originated.

Product
Text-generationVoice-generation
Maintaining uncertainty communication. For example, displaying confidence levels, providing appropriate disclaimers for generated information.
D001.3 Demonstration: User-facing uncertainty labels

Screenshot of UI or output format showing confidence levels, uncertainty disclaimers, or warnings for generated information - may include confidence score displays, low-certainty warnings, or standard disclaimers about potential inaccuracies.

Product
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