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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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B008. Protect AI system deployment environment
B008

Protect AI system deployment environment

Implement security measures for AI system deployment environments including encryption, access controls and authorization

Keywords

Model EnvironmentEncryptionAccess Controls

Application

Mandatory

Frequency

Every 12 months

Type

Preventative

Crosswalks

MITRE ATLAS
AML-M0005: Control Access to AI Models and Data at Rest
AML-M0012: Encrypt Sensitive Information
AML-M0019: Control Access to AI Models and Data in Production
EU AI Act
Article 15: Accuracy Robustness and Cybersecurity
OWASP Top 10
LLM07:25 - System Prompt Leakage
CSA AICM
AIS-06: Secure Application Deployment
AIS-14: AI Cache Protection
CEK-01: Encryption and Key Management Policy and Procedures
CEK-03: Data Encryption
CEK-04: Encryption Algorithm
CEK-19: Key Compromise
CEK-10: Key Generation
CEK-11: Key Purpose
CEK-12: Key Rotation
CEK-13: Key Revocation
CEK-14: Key Destruction
CEK-15: Key Activation
CEK-16: Key Suspension
CEK-17: Key Deactivation
CEK-18: Key Archival
CEK-20: Key Recovery
CEK-21: Key Inventory Management
IAM-04: Separation of Duties
IAM-14: Strong Authentication
MDS-01: Training Pipeline Security
MDS-02: Model Artifact Scanning
MDS-08: Model Integrity Checks
UEM-08: Storage Encryption
AIS-02: Application Security Baseline Requirements
DSP-07: Data Protection by Design and Default
MDS-09: Model Signing/Ownership Verification
OWASP AIVSS
Agent Memory and Context Manipulation
IBM AI Risk Atlas
IBM 7: Agentic AI - Unauthorized use
IBM 29: Training Data - Data poisoning
Cisco AI Security Framework
AITech-1.3: Goal Manipulation
AITech-6.1: Training Data Poisoning
AITech-7.1: Reasoning Corruption
AITech-10.1: Model Extraction
AITech-11.1: Environment-Aware Evasion
AITech-14.1: Unauthorized Access
AITech-16.1: Eavesdropping

Control activities

Typical evidence

Implementing AI system access protection. For example, restricting access to production AI systems based on job function and operational need, implementing MFA for model system access, maintaining user access reviews appropriate to organizational size.
B008.1 Config: Model access controls

IAM configuration, permission settings, or admin panel showing role-based access restrictions for production AI systems covering IAM role assignments restricting model access by job function, MFA configuration for model system access, and access review records validating model permissions.

Category

Technical Implementation
Engineering CodeInternal processes
Universal
Enforcing caller authentication across API endpoints and agentic interfaces. For example, applying scoped API tokens or signed requests for model API access; enforcing OAuth 2.0 or OIDC token validation with appropriate scoping for MCP server connections; implementing mutual authentication for agent-to-agent interfaces.
B008.2 Config: API and agentic interface authentication

Configuration or code showing caller authentication controls - may include scoped API token or signed request configuration for model API endpoints, OAuth token scoping or OIDC validation middleware for MCP server connections, or mutual authentication configuration for agent-to-agent interfaces (e.g. A2A protocol authentication config).

Category

Technical Implementation
Engineering Code
Universal
Securing data in transit across model API endpoints and agentic interfaces. For example, enforcing TLS for all model API endpoint traffic, MCP server connections, and agent-to-agent communication channels; implementing credential rotation policies for long-lived service connections.
B008.3 Config: API and agentic interface transport security

Configuration or code showing transport security controls - may include TLS/HTTPS certificate configuration for model API endpoints, MCP server traffic, or agent-to-agent connections, or credential rotation policy documentation for service-level MCP or A2A connections.

Category

Technical Implementation
Engineering Code
Universal
Securing model hosting environments. For example, using up-to-date and minimal container images, scanning for known vulnerabilities in dependencies and base images, and applying infrastructure-level isolation techniques based on risk level (e.g. container namespaces, VM separation, or dedicated GPU access).
B008.5 Config: Model hosting security

Container configuration or infrastructure setup for model hosting - may include Dockerfile with minimal base images and up-to-date dependencies, vulnerability scanning results from Trivy or Snyk for container images, or infrastructure configuration showing isolation techniques (container namespaces, VM separation, network policies, dedicated GPU allocation).

Category

Technical Implementation
Engineering Code
Universal
Verifying model integrity before and during deployment. For example, using cryptographic checksums or signed artifacts to detect tampering, scanning model files for malicious payloads.
B008.6 Config: Model integrity verification

Deployment pipeline or code implementing model integrity checks - may include cryptographic checksum verification, model artifact signature validation, hash comparison before deployment, model scanning configuration detecting malicious payloads (e.g. Pickle, ONNX) using tools like Cisco's pickle-fuzzer, Trail of Bit's Fickling, or deployment logs recording model version hashes.

Category

Technical Implementation
Engineering Code
Universal
Enforcing data integrity across agentic interfaces. For example, implementing cryptographic message signing for agent-to-agent communication; applying schema validation and input sanitization to MCP tool call inputs and outputs.
B008.4 Config: Agentic interface data integrity

Configuration or code showing data integrity controls for agentic interfaces - may include cryptographic message signing configuration for agent-to-agent interfaces (e.g. signed agent cards), or schema validation configuration applied to MCP tool call inputs and outputs.

Category

Technical Implementation
Engineering Code
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