Industry Standards

NIST AI Risk Management Framework

NIST AI 100-1 (2023)

Core Functions

1. GOVERN - Establish AI governance and oversight
2. MAP - Identify and assess AI risks
3. MEASURE - Analyze and track AI risks
4. MANAGE - Prioritize and respond to risks

Risk Categories

Security Risks:

• Adversarial attacks (prompt injection, data poisoning)
• Model theft and unauthorized access
• Privacy violations and data leakage
• Supply chain vulnerabilities

Implementation:

class NISTCompliance:
    def assess_risk(self, ai_system):
        """
        NIST AI RMF risk assessment
        """
        risks = {
            'security': self.assess_security(ai_system),
            'privacy': self.assess_privacy(ai_system),
            'fairness': self.assess_fairness(ai_system),
            'transparency': self.assess_transparency(ai_system)
        }
        
        return {
            'overall_risk': max(risks.values()),
            'categories': risks,
            'recommendations': self.generate_recommendations(risks)
        }

Reference: NIST AI Risk Management Framework

OWASP Top 10 for LLM Applications

Version 1.1 (2024)

LLM01: Prompt Injection (HIGHEST RISK)

Description: Manipulating LLM behavior via crafted inputs

Attack Types:

• Direct prompt injection (user-controlled)
• Indirect prompt injection (data poisoning)
• Encoding-based attacks

Prevention:

• Privilege control and least privilege
• Human-in-the-loop for critical operations
• Segregate external content from system prompts
• Establish clear trust boundaries
• Input validation and sanitization

LLM02: Insecure Output Handling

Description: Insufficient validation of LLM outputs

Prevention:

• Encode outputs appropriately
• Validate output format and content
• Implement content filtering
• Monitor for sensitive information disclosure

LLM03: Training Data Poisoning

Description: Manipulating training data to compromise model behavior

Prevention:

• Verify data provenance
• Implement anomaly detection
• Use sandboxed environments
• Regular model validation

LLM04: Model Denial of Service

Description: Overloading LLMs with resource-heavy operations

Prevention:

• Rate limiting
• Resource quotas
• Input length restrictions
• Monitoring and alerting

LLM05: Supply Chain Vulnerabilities

Description: Compromised components, services, or datasets

Prevention:

• Vendor assessment
• Dependency scanning
• Secure software development practices
• Regular security audits

Full List: OWASP Top 10 for LLM Applications

ISO/IEC Standards

ISO/IEC 42001:2023 - AI Management System

Scope: Requirements for establishing, implementing, maintaining AI management systems

Key Controls:

• Risk assessment and management (Clause 6.1)
• Data governance and quality (Clause 7.4)
• AI system lifecycle management (Clause 8)
• Performance monitoring and evaluation (Clause 9)
• Incident management (Clause 8.5)

Certification: Organizations can achieve ISO 42001 certification

ISO/IEC 23894:2023 - AI Risk Management

Framework:

• Risk identification
• Risk analysis
• Risk evaluation
• Risk treatment and monitoring

Applicable To:

• AI system developers
• AI system deployers
• AI system operators

IEEE Standards

IEEE 2941-2023 - AI Model Governance

Coverage:

• Model development lifecycle
• Testing and validation procedures
• Deployment controls
• Monitoring and maintenance requirements
• Incident response

IEEE 7000-2021 - Systems Design for Ethical Concerns

Process:

1. Identify stakeholders and their concerns
2. Elicit ethical values and requirements
3. Translate values to technical requirements
4. Verify implementation against requirements
5. Monitor and maintain ethical alignment

C2PA (Content Authenticity)

Coalition for Content Provenance and Authenticity

Members: Adobe, Microsoft, BBC, Intel, Sony, Nikon, Canon, Leica

Standard: C2PA v1.3 (2024)

Features:

• Cryptographic content binding
• Tamper-evident manifests
• Edit history tracking
• Creator attribution and provenance
• Claim verification

Implementation:

// Using C2PA JavaScript SDK
import { createC2pa } from 'c2pa';

async function signContent(imageBuffer, metadata) {
    const c2pa = createC2pa();
    
    const manifest = {
        claim_generator: 'MyApp/1.0',
        assertions: [
            {
                label: 'c2pa.actions',
                data: {
                    actions: [{
                        action: 'c2pa.created',
                        when: new Date().toISOString(),
                        softwareAgent: 'MyApp/1.0',
                        parameters: {
                            description: 'Original content creation'
                        }
                    }]
                }
            }
        ]
    };
    
    return await c2pa.sign(imageBuffer, manifest);
}

Adoption:

• Adobe Creative Cloud (2021+)
• Nikon Z9 (2022+)
• Canon EOS R3 (2023+)
• Leica M11-P (2023+)
• Microsoft Edge (2024+)

MITRE ATT&CK for AI

Framework: ATLAS (Adversarial Threat Landscape for AI Systems)

Tactics:

1. Reconnaissance - Gather information about AI systems
2. Resource Development - Prepare attack infrastructure
3. Initial Access - Gain entry to AI systems
4. ML Attack Staging - Prepare for ML-specific attacks
5. Exfiltration - Extract data from AI systems
6. Impact - Disrupt or degrade AI systems

Techniques:

• AML.T0051: Prompt Injection
• AML.T0043: Model Poisoning
• AML.T0024: Backdoor Attack
• AML.T0002: Data Poisoning
• AML.T0015: Model Extraction

Reference: MITRE ATLAS

Industry Certifications

SOC 2 Type II (AI Systems)

Trust Service Criteria:

• Security - Protection against unauthorized access
• Availability - System availability and performance
• Processing Integrity - Accurate and complete processing
• Confidentiality - Protection of confidential information
• Privacy - Collection and use of personal information

AI-Specific Controls:

• Model versioning and rollback
• Training data governance
• Bias testing and monitoring
• Adversarial testing
• Model performance tracking

ISO 27001 + AI Extension

Annex A Controls (relevant to AI):

• A.8.24: Use of cryptography for data protection
• A.12.6: Technical vulnerability management
• A.14.2: Security in development and support
• A.18.1: Compliance with legal requirements

Compliance Mapping

Research Citations

1. NIST AI 100-1 (2023) - AI Risk Management Framework
2. OWASP (2024) - Top 10 for LLM Applications v1.1
3. ISO/IEC 42001:2023 - AI Management System
4. ISO/IEC 23894:2023 - AI Risk Management
5. IEEE 2941-2023 - AI Model Governance
6. IEEE 7000-2021 - Ethical Systems Design
7. C2PA v1.3 (2024) - Content Authenticity Standard
8. MITRE ATLAS - Adversarial Threat Landscape

Next: Threat Intelligence →