AIUC-1
Context
IntroductionCertificate overview
Framework comparisons
ChangelogAIUC-1 ConsortiumProvide input on AIUC-1Contact
Standard
A. Data & Privacy
B. Security
C. Safety
D. Reliability
E. Accountability
AI failure plan for security breachesAI failure plan for harmful outputsAI failure plan for hallucinationsAssign accountabilityDocument data storage securityConduct vendor due diligence[Retired] Document system change approvalsReview internal processesMonitor third-party accessEstablish AI acceptable use policyRecord processing locationsDocument regulatory complianceImplement quality management system[Retired] Share transparency reportsLog AI system activityImplement AI disclosure mechanismsDocument system transparency policy
F. Society
Certification
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Evidence overview
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The Security, Safety, and Reliability standard for AI agents

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AIUC-1 Standard
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E. Accountability
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E011. Record processing locations
E011

Record processing locations

Document AI data processing locations

Keywords

Data ProcessingStorage LocationData Protections

Application

Mandatory

Frequency

Every 12 months

Type

Preventative

Crosswalks

EU AI Act
Article 11: Technical Documentation
ISO 42001
A.7.5: Data provenance
NIST AI RMF
GOVERN 1.6: AI system inventory
CSA AICM
DSP-13: Personal Data Sub-processing
DSP-19: Data Location
UEM-04: Endpoint Inventory
IBM AI Risk Atlas
IBM 35: Training Data - Data acquisition restrictions
IBM 37: Training Data - Data transfer restrictions

Control activities

Typical evidence

Maintaining AI infrastructure location documentation. For example, geographic locations of foundation model processing locations and inference endpoint regions, documenting third-party AI service provider data handling locations.
Reviewing and updating documentation regularly.
E011.1 Documentation: AI processing locations

Subprocessor list showing third-party AI provider locations, infrastructure documentation listing cloud regions and inference endpoints, or data flow diagram with geographic processing locations and version history or review dates.

Category

Operational Practices
Trust Center
Universal
Implementing transfer compliance procedures. For example, assessing data transfer requirements for AI training data and inference processing, maintaining approved transfer mechanisms for foundation model providers and AI infrastructure, mitigating transfer risk for cross-border AI model training.
E011.2 Documentation: Data transfer compliance

Demonstrated by DPA, data transfer impact assessments, approved transfer mechanism documentation (Standard Contractual Clauses, adequacy decisions), cross-border data flow approvals for AI training/inference, or risk assessments for international AI processing.

Category

Legal Policies
Internal policiesData Processing Agreement
Universal

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

Orrick
John Bautista
Partner at Orrick
Lena Smart

"An AIUC-1certificate 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