jacobdjwilson · August 31, 2024 17:35
diff --git a/AI Usage Policy Framework b/AI Usage Policy Framework
 Objective: Create a detailed AI Usage Policy for an organization that includes General AI Principles, Approval Considerations, and standards for tooling or product. The policy should be tailored to the specific industry, line of business, and operational context of the organization, ensuring it is applicable across various sectors and locations. The policy should also integrate considerations for data governance, security, and ethical AI usage.
 Input Requirements:
 Organization Details: Industry/Sector (e.g., Healthcare, Finance, Technology, Education); Regulations/Standards (e.g., GDPR, HIPAA, PCI, CCPA; Yes/No); Business Objectives: Main Objectives (e.g., Risk Management, Ethical AI Use, Compliance, Efficiency); Challenges/Concerns (e.g., Data Privacy, Bias, Security Risks; Yes/No); Policy Scope: Primary Users/Stakeholders (e.g., IT Department, Data Scientists, General Employees); AI Tools/Models (e.g., ChatGPT, GPT-4, Custom LLMs; Yes/No); Approval and Governance: Existing Processes (e.g., Formal Approval Processes, Data Governance Frameworks; Yes/No); Data Classification (e.g., Customer Data, Internal Data, Training Data); Integration with Existing Policies: Policy Interaction (e.g., Align with Data Protection Policies, Integrate with Security Protocols); Existing Frameworks (e.g., ISO Standards, NIST Guidelines; Yes/No); Reporting and Compliance: Reporting Procedures (e.g., Incident Reporting Channels, Escalation Procedures); Consequences (e.g., Disciplinary Actions, Corrective Measures; Yes/No).
 Output Requirements:
 General AI Principles Section: Include principles such as limiting agency, ensuring critical review, maintaining human oversight, upholding data privacy, defining data ownership, transparency, and accountability; tailor these principles to the specific needs of the organization based on their industry and line of business. Approval Considerations Section: Outline processes for data governance and software requests; address data classification standards, detailing types of data like prompts, completions, training and validation data, and results; consider interactions with existing policies related to data governance, privacy, and software development. Standards for Tooling or Product: Provide guidelines for approved AI tools and language models, detailing their usage, integration, and interaction (e.g., interactive use, API integration); include a table similar to the example provided to categorize tools and their usage standards. Reporting and Violations Section: Define procedures for reporting AI-related incidents and violations; specify consequences for policy violations, including disciplinary actions and corrective measures. Contextual Integration: Incorporate relevant background information such as the OWASP Top 10 for LLMs and key AI terms (e.g., AI, ML, LLM, Generative AI, Bias); ensure the policy addresses the challenges and considerations highlighted in the blog post about establishing an AI Acceptable Use Policy.
 Example Structure: Introduction: Purpose of the policy, Scope and applicability; General AI Principles: [Customized principles based on industry and business needs]; Approval Considerations: Data classification and types, Existing policies and processes; Standards for Tooling or Product: Table of tools and standards, Usage guidelines; Reporting and Violations: Reporting procedures, Consequences and corrective actions.
	Objective: Create a detailed AI Usage Policy for an organization that includes General AI Principles, Approval Considerations, and standards for tooling or product. The policy should be tailored to the specific industry, line of business, and operational context of the organization, ensuring it is applicable across various sectors and locations. The policy should also integrate considerations for data governance, security, and ethical AI usage.
	Input Requirements:
	Organization Details: Industry/Sector (e.g., Healthcare, Finance, Technology, Education); Regulations/Standards (e.g., GDPR, HIPAA, PCI, CCPA; Yes/No); Business Objectives: Main Objectives (e.g., Risk Management, Ethical AI Use, Compliance, Efficiency); Challenges/Concerns (e.g., Data Privacy, Bias, Security Risks; Yes/No); Policy Scope: Primary Users/Stakeholders (e.g., IT Department, Data Scientists, General Employees); AI Tools/Models (e.g., ChatGPT, GPT-4, Custom LLMs; Yes/No); Approval and Governance: Existing Processes (e.g., Formal Approval Processes, Data Governance Frameworks; Yes/No); Data Classification (e.g., Customer Data, Internal Data, Training Data); Integration with Existing Policies: Policy Interaction (e.g., Align with Data Protection Policies, Integrate with Security Protocols); Existing Frameworks (e.g., ISO Standards, NIST Guidelines; Yes/No); Reporting and Compliance: Reporting Procedures (e.g., Incident Reporting Channels, Escalation Procedures); Consequences (e.g., Disciplinary Actions, Corrective Measures; Yes/No).
	Output Requirements:
	General AI Principles Section: Include principles such as limiting agency, ensuring critical review, maintaining human oversight, upholding data privacy, defining data ownership, transparency, and accountability; tailor these principles to the specific needs of the organization based on their industry and line of business. Approval Considerations Section: Outline processes for data governance and software requests; address data classification standards, detailing types of data like prompts, completions, training and validation data, and results; consider interactions with existing policies related to data governance, privacy, and software development. Standards for Tooling or Product: Provide guidelines for approved AI tools and language models, detailing their usage, integration, and interaction (e.g., interactive use, API integration); include a table similar to the example provided to categorize tools and their usage standards. Reporting and Violations Section: Define procedures for reporting AI-related incidents and violations; specify consequences for policy violations, including disciplinary actions and corrective measures. Contextual Integration: Incorporate relevant background information such as the OWASP Top 10 for LLMs and key AI terms (e.g., AI, ML, LLM, Generative AI, Bias); ensure the policy addresses the challenges and considerations highlighted in the blog post about establishing an AI Acceptable Use Policy.
	Example Structure: Introduction: Purpose of the policy, Scope and applicability; General AI Principles: [Customized principles based on industry and business needs]; Approval Considerations: Data classification and types, Existing policies and processes; Standards for Tooling or Product: Table of tools and standards, Usage guidelines; Reporting and Violations: Reporting procedures, Consequences and corrective actions.