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Standard
A. Data & Privacy
B. Security
C. Safety
Define AI risk taxonomyConduct pre-deployment testingPrevent harmful outputsPrevent out-of-scope outputsPrevent customer-defined high risk outputsPrevent output vulnerabilitiesFlag high risk outputs for human reviewMonitor AI risk categoriesEnable real-time feedback and interventionThird-party testing for harmful outputsThird-party testing for out-of-scope outputsThird-party testing for customer-defined risk
D. Reliability
E. Accountability
F. Society
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Evidence overview
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AIUC-1 Standard
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C. Safety
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C004. Prevent out-of-scope outputs
C004

Prevent out-of-scope outputs

Implement safeguards or technical controls to prevent out-of-scope outputs (e.g. political discussion, healthcare advice)

Keywords

Out-of-ScopePolitical DiscussionTechnical Controls

Application

Mandatory

Frequency

Every 12 months

Type

Preventative

Crosswalks

EU AI Act
Article 72: Post-Market Monitoring by Providers and Post-Market Monitoring Plan for High-Risk AI Systems
NIST AI RMF
MAP 2.2: Knowledge limits
MAP 3.4: Operator proficiency
OWASP Top 10
LLM05:25 - Improper Output Handling
CSA AICM
AIS-09: Output Validation
GRC-09: Acceptable Use of the AI Service
LOG-15: Output Monitoring
TVM-11: Guardrails
OWASP AIVSS
Agent Goal and Instruction Manipulation
IBM AI Risk Atlas
IBM 69: Output - Improper usage
Cisco AI Security Framework
AITech-1.1: Direct Prompt Injection
AITech-2.1: Jailbreak
AITech-15.1: Harmful Content

Control activities

Typical evidence

Detecting and blocking out-of-scope requests. For example, detecting conversations outside intended use cases, blocking prohibited topics, providing redirection messages when users hit boundaries, and escalating or restricting access for repeated violations.
C004.1 Config: out-of-scope guardrails

Blocking rules, defensive prompting, or filtering configuration showing how out-of-scope requests are detected and handled - may include topic blocklists, redirection message templates, escalation rules for repeated attempts, or system prompts defining allowed topics.

Category

Technical Implementation
Engineering Code
Text-generationVoice-generation
Tracking out-of-scope violations and updating boundaries. For example, logging boundary violations, adjusting restrictions based on misuse patterns.
C004.2 Logs: Out-of-scope attempts

Logs showing out-of-scope attempts with frequency data. May include documentation of boundary updates made in response to violations, monitoring dashboard of flagged requests, change log showing restriction updates with rationale, or incident reports triggering scope adjustments.

Category

Technical Implementation
Logs
Text-generationVoice-generation
Providing user guidance on system capabilities and limitations. For example, communicating what the AI system can and cannot do, intended use cases, and topics or requests outside the system's scope.
C004.3 Demonstration: User guidance on scope

User-facing guidance explaining system capabilities and limitations - may include onboarding tooltips or welcome screens, help documentation or FAQs describing intended use, UI warnings when approaching scope boundaries, or published usage guidelines.

Category

Technical Implementation
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-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