AIUC-1
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Standard
A. Data & Privacy
B. Security
Third-party testing of adversarial robustnessDetect adversarial inputManage public release of technical detailsPrevent AI endpoint scrapingImplement real-time input filteringPrevent unauthorized AI agent actionsEnforce user access privileges to AI systemsProtect AI system deployment environmentLimit output over-exposure
C. Safety
D. Reliability
E. Accountability
F. Society
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AIUC-1 Standard
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B. Security
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B004. Prevent AI endpoint scraping
B004

Prevent AI endpoint scraping

Implement safeguards to prevent probing or scraping of external AI endpoints

Keywords

ScrapingProbingRate LimitingQuery QuotasZero Trust

Application

Mandatory

Frequency

Every 12 months

Type

Preventative

Crosswalks

MITRE ATLAS
AML-M0003: Model Hardening
AML-M0004: Restrict Number of AI Model Queries
EU AI Act
Article 15: Accuracy Robustness and Cybersecurity
NIST AI RMF
MEASURE 2.7: Security and resilience
OWASP Top 10
LLM02:25 - Sensitive Information Disclosure
LLM05:25 - Improper Output Handling
LLM08:25 - Vector and Embedding Weaknesses
LLM10:25 - Unbounded Consumption
CSA AICM
AIS-10: API Security
UEM-01: Endpoint Devices Policy and Procedures
UEM-05: Endpoint Management
UEM-09: Anti-Malware Detection and Prevention
UEM-10: Software Firewall
UEM-11: Data Loss Prevention
UEM-14: Third-Party Endpoint Security Posture
TVM-06: Penetration Testing
OWASP AIVSS
Agentic AI Tool Misuse
IBM AI Risk Atlas
IBM 42: Inference - Extraction attack
IBM 47: Inference - Prompt leaking
IBM 56: Inference - Attribute inference attack
IBM 57: Inference - Membership inference attack
Cisco AI Security Framework
AITech-1.4: Multi-Modal Injection and Manipulation
AITech-8.2: Data Exfiltration / Exposure
AITech-10.1: Model Extraction
AITech-10.2: Model Inversion
AITech-12.2: Insecure Output Handling
AITech-13.1: Disruption of Availability
AITech-13.2: Cost Harvesting / Repurposing

Control activities

Typical evidence

Implementing systems distinguishing between high-volume legitimate usage and adversarial behavior. For example, using behavioral analytics and user profiling to calibrate detection thresholds and prevent false positives against trusted users.
B004.1 Config: Anomalous usage detection

Anomaly detection system or configuration file - may include behavioral analytics dashboard (Datadog, Elastic, Splunk) with user scoring rules, rate limiting configuration with tier-based thresholds (config.yaml, API gateway settings), user allowlists or reputation tables, or code implementing session-based threshold logic.

Category

Technical Implementation
Engineering ToolingEngineering Code
Universal
Implementing rate limiting and query restrictions. For example, establishing per-user quotas to prevent model extraction, blocking excessive query patterns, implementing progressive restrictions for suspicious behavior, or using economic disincentives for high-volume usage.
B004.2 Config: Rate limits

Rate limiting configuration for API endpoints - may include per-user quota settings, query throttling rules, progressive restriction policies, WAF configuration (Cloudflare, AWS WAF, Azure Application Gateway) with blocking rules for excessive patterns, or pricing tier settings implementing usage-based cost increases.

Category

Technical Implementation
Engineering Tooling
Universal
Conducting simulated external attack testing of AI endpoints. For example, performing automated attack simulations, testing endpoint protection effectiveness against high-volume and distributed attacks, and documenting methodologies appropriate to organizational threat profile.
B004.3 Report: External pentest of AI endpoints

Third-party penetration test report for AI endpoints including attack simulations tested (e.g. scraping attempts, brute force, reconnaissance), rate limiting and endpoint protection validation, distributed attack testing, test methodology, and findings on protection effectiveness.

Category

Technical Implementation
Engineering Practice
Universal
Maintaining endpoint security through remediation. For example, tracking identified vulnerabilities, implementing protective measures based on testing outcomes, and regularly updating endpoint defenses and detection thresholds.
B004.4 Documentation: Vulnerability remediation

Issue tracking system (GitHub, Jira, Linear) showing endpoint vulnerability lifecycle - must include vulnerability identification, remediation proposal, implementation, and production deployment with timestamps and approval records.

Category

Technical Implementation
Engineering Practice
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