LLMSecForge/cybersecurity-governance-framework.md

LLMSecForge: AI Cybersecurity Governance & Policy Frameworks

/frameworks/governance/

This directory provides comprehensive governance frameworks, policy templates, and compliance guidance for managing adversarial risks in AI systems, establishing best practices for LLM security governance.

frameworks/governance/
├── README.md
├── policy-frameworks/
│   ├── security-governance-model.md
│   ├── risk-management-framework.md
│   ├── incident-response-policy.md
│   └── compliance-integration.md
├── implementation/
│   ├── governance-implementation.md
│   ├── security-controls.md
│   ├── monitoring-framework.md
│   └── testing-protocols.md
├── roles/
│   ├── security-responsibilities.md
│   ├── red-team-governance.md
│   ├── disclosure-management.md
│   └── oversight-structure.md
├── standards/
│   ├── testing-standards.md
│   ├── documentation-requirements.md
│   ├── evidence-collection.md
│   └── assessment-methodologies.md
├── risk-analysis/
│   ├── threat-modeling.md
│   ├── vulnerability-classification.md
│   ├── impact-assessment.md
│   └── risk-quantification.md
└── templates/
    ├── governance-policy-template.md
    ├── risk-assessment-template.md
    ├── testing-documentation.md
    └── compliance-checklist.md

README.md

AI Cybersecurity Governance & Policy Frameworks

This framework provides a comprehensive approach to AI security governance, establishing structured methodologies for managing adversarial risks, implementing appropriate controls, and ensuring compliance with emerging regulatory requirements for AI systems.

Governance Framework Purpose

This section of the repository addresses critical governance needs:

1. Policy Framework Integration: Structured approaches to embedding adversarial security within organizational governance
2. Compliance Alignment: Methodologies for aligning security practices with emerging AI regulations and standards
3. Risk Management Structures: Frameworks for systematically assessing and managing adversarial risks
4. Organizational Implementation: Guidance for implementing governance across different organizational structures
5. Documentation Standards: Templates and requirements for governance documentation

Core Framework Components

1. Policy & Governance Frameworks

Comprehensive governance structures for AI security:

• Security Governance Model: Organizational structure and oversight frameworks
• Risk Management Framework: Structured approach to AI security risk management
• Incident Response Policy: Governance for security incidents and vulnerabilities
• Compliance Integration: Alignment with regulatory and industry standards

2. Implementation Guidance

Practical approaches to governance implementation:

• Governance Implementation: Step-by-step implementation methodologies
• Security Controls: Technical and procedural control frameworks
• Monitoring Framework: Continuous monitoring approaches
• Testing Protocols: Governance requirements for security testing

3. Roles & Responsibilities

Clear delineation of security governance roles:

• Security Responsibilities: Role-based security responsibilities
• Red Team Governance: Oversight and management of adversarial testing
• Disclosure Management: Responsible disclosure governance
• Oversight Structure: Board and executive-level oversight frameworks

4. Standards & Requirements

Detailed standards for security governance:

• Testing Standards: Requirements for adversarial testing
• Documentation Requirements: Standards for security documentation
• Evidence Collection: Requirements for evidence gathering and retention
• Assessment Methodologies: Standardized assessment approaches

5. Risk Analysis Frameworks

Structured approaches to AI security risk:

• Threat Modeling: Frameworks for AI-specific threat modeling
• Vulnerability Classification: Standardized vulnerability categorization
• Impact Assessment: Methodologies for evaluating security impact
• Risk Quantification: Approaches to quantifying AI security risk

Applications of this Framework

This governance framework supports several critical organizational functions:

1. Executive Leadership: Provides governance structures for board and executive oversight
2. Security Teams: Establishes clear roles, responsibilities, and procedures
3. Compliance Functions: Aligns security practices with regulatory requirements
4. Risk Management: Provides frameworks for systematic risk management
5. Audit Functions: Establishes clear standards for security assessment and evidence

For Security Leaders

If you're responsible for AI security governance:

1. Review the governance model to establish appropriate organizational structures
2. Implement the risk management framework to systematically address AI risks
3. Utilize the implementation guidance for practical governance rollout
4. Leverage the templates for efficient policy and procedure development

For Compliance Teams

If you're responsible for AI compliance:

1. Use the compliance integration framework to align security with regulatory requirements
2. Implement the documentation standards to ensure adequate evidence collection
3. Leverage the assessment methodologies for compliance verification
4. Utilize the templates for creating compliance-aligned documentation

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AI Security Governance Model

# AI Security Governance