AI Acceptable Use Policy (SEC-POL-007)

1. Objective

The objective of this policy is to establish comprehensive guidelines for the acceptable and secure use of Artificial Intelligence (AI) and Machine Learning (ML) technologies at [Company Name]. This policy ensures that AI tools and systems are used responsibly, ethically, and securely while protecting the confidentiality, integrity, and availability of company information, particularly electronic Protected Health Information (ePHI). This policy addresses the unique risks associated with AI technologies including data privacy, bias, transparency, and regulatory compliance with HIPAA, HITECH, and SOC 2 requirements while enabling innovation and productivity improvements through responsible AI adoption.

2. Scope

This policy applies to all [Company Name] workforce members, contractors, third parties, and business associates who use, develop, deploy, or manage AI and ML technologies on behalf of the organization. It encompasses all AI applications including but not limited to generative AI tools (ChatGPT, Claude, Bard), machine learning models, automated decision-making systems, natural language processing tools, computer vision systems, and AI-powered business applications. This policy covers both internally developed AI systems and third-party AI services, regardless of deployment model (cloud-based, on-premises, or hybrid), and applies to all use cases including business operations, software development, clinical decision support, and administrative functions.

3. Policy

[Company Name] shall implement comprehensive governance and security controls for AI technologies to ensure responsible, ethical, and compliant use while protecting sensitive information and maintaining stakeholder trust.

3.1 AI Governance Framework

A formal AI governance structure shall be established to oversee the evaluation, approval, deployment, and monitoring of AI technologies across the organization.

3.1.1 AI Governance Committee

Committee Structure:

  • AI Governance Committee comprising representatives from Security, Privacy, Legal, Clinical, IT, and Business units
  • Designated AI Ethics Officer responsible for ethical AI oversight and compliance
  • Regular committee meetings (monthly) to review AI initiatives and address emerging issues
  • Clear escalation procedures for AI-related risks and ethical concerns
  • Annual review of AI governance policies and procedures

Committee Responsibilities:

  • Approval of new AI tools and applications for organizational use
  • Risk assessment and mitigation for AI implementations
  • Policy development and maintenance for AI acceptable use
  • Incident response coordination for AI-related security or ethical issues
  • Training and awareness program oversight for AI usage

3.1.2 AI Risk Assessment Process

Pre-Implementation Assessment:

  • A formal, documented risk assessment is required for all new AI tools or significant changes to existing tools before deployment.
  • Data sensitivity analysis to identify the use of ePHI, PII, or other confidential information.
  • Bias and fairness evaluation for AI systems that could impact individuals.
  • Privacy Impact Assessment (PIA) for AI applications processing personal data.
  • Security assessment of the AI tool and its vendor, including data protection and access controls.
  • The completed risk assessment must be submitted to and formally approved by the AI Governance Committee prior to use.

Risk Categories:

  • High Risk: AI systems processing ePHI, making automated decisions affecting individuals, or handling Restricted data
  • Medium Risk: AI systems processing Confidential data or providing business-critical functions
  • Low Risk: AI systems processing only Public or Internal data with limited business impact

3.2 Data Protection and Privacy

AI systems shall implement comprehensive data protection measures to safeguard sensitive information and ensure privacy compliance.

3.2.1 Data Handling Requirements

ePHI and Sensitive Data Protection:

  • The use of ePHI or any other Restricted data is strictly prohibited in any public or third-party AI system unless the service is explicitly listed in the company’s Approved AI Service Catalog and is governed by a signed Business Associate Agreement (BAA).
  • Data minimization principles shall be applied to all AI training and inference data, ensuring only the minimum necessary data is used for the intended purpose.
  • Encryption is required for all data at rest and in transit for AI systems handling Confidential or Restricted data.
  • Access to AI systems handling sensitive data shall be logged and reviewed at least quarterly.

Data Anonymization and De-identification:

  • When healthcare data is used for AI model training, it must be de-identified in accordance with the standards set forth in the HIPAA Privacy Rule (45 CFR § 164.514), using either the Safe Harbor method or Expert Determination.
  • The de-identification method used must be documented and the documentation retained.
  • Regular validation of de-identification effectiveness shall be conducted.
  • Any attempt to re-identify individuals from a de-identified dataset is strictly prohibited.

3.2.2 Third-Party AI Service Usage

Approved AI Services:

  • The AI Governance Committee shall maintain an inventory of approved AI services, including documentation of their security and privacy assessments
  • Contractual requirements for data protection, privacy, and compliance
  • Vendor assessment including data handling practices, security controls, and compliance certifications
  • Geographic data location restrictions and cross-border transfer limitations
  • Service level agreements including data breach notification and incident response

Prohibited AI Services:

  • Public AI systems without appropriate enterprise controls and data protection
  • AI services with inadequate privacy protection or unclear data usage policies
  • AI tools that retain or use input data for training without explicit consent
  • AI systems operating in jurisdictions with inadequate data protection laws
  • Free or consumer-grade AI services for processing company information

3.3 Ethical AI Use and Bias Prevention

AI systems shall be developed and deployed in accordance with ethical principles and bias prevention measures to ensure fair and responsible outcomes.

3.3.1 Ethical AI Principles

Fairness and Non-Discrimination:

  • Regular testing for bias in AI systems affecting hiring, promotion, or patient care decisions
  • Diverse training data and validation datasets to minimize algorithmic bias
  • Monitoring of AI system outcomes for disparate impact on protected groups
  • Remediation procedures for identified bias or discriminatory outcomes
  • Documentation of fairness measures and bias testing results

Transparency and Explainability:

  • Clear documentation of AI system capabilities, limitations, and decision-making processes
  • Explainable AI requirements for systems making decisions affecting individuals
  • User notification when interacting with AI systems or AI-generated content
  • Model interpretability measures for critical business decisions
  • Regular communication about AI system changes and updates

3.3.2 Human Oversight and Control

Human-in-the-Loop Requirements:

  • Human review and approval required for AI-generated decisions affecting individuals
  • Override capabilities for all automated AI decisions
  • Training for workforce members supervising AI systems
  • Clear escalation procedures for AI system malfunctions or unexpected outcomes
  • Regular validation of AI system performance and accuracy

3.4 AI Security Controls

Comprehensive security controls shall be implemented to protect AI systems from threats and ensure system integrity.

3.4.1 AI System Security

Access Controls and Authentication:

  • Role-based access control for all AI systems and platforms
  • Multi-factor authentication required for AI system access
  • Privileged access management for AI system administration
  • Regular access reviews and recertification for AI system users
  • API security controls for AI service integrations

Model Security and Protection:

  • Protection of AI models as intellectual property and trade secrets
  • Secure storage and versioning of AI models and training data
  • Adversarial attack prevention and detection measures
  • Model integrity validation and tampering detection
  • Secure deployment pipelines for AI model updates

3.4.2 AI Data Security

Training Data Protection:

  • Encryption of all AI training datasets containing sensitive information
  • Secure data pipelines for AI model training and validation
  • Data lineage tracking and documentation for AI datasets
  • Regular data quality and integrity assessments
  • Secure deletion of training data when no longer needed

Inference Data Security:

  • Real-time data protection for AI system inputs and outputs
  • Monitoring and logging of all AI system interactions
  • Data loss prevention controls for AI-generated content
  • Backup and recovery procedures for AI system data
  • Incident response procedures for AI data breaches

3.5 AI Development and Deployment

Secure development practices shall be applied to all AI system development and deployment activities.

3.5.1 AI Development Lifecycle

Secure AI Development:

  • Security requirements integration into AI development lifecycle
  • Code review and security testing for AI applications
  • Vulnerability assessment of AI frameworks and libraries
  • Secure coding practices for AI model development
  • Version control and change management for AI systems

Model Validation and Testing:

  • Comprehensive testing of AI models before production deployment
  • Performance monitoring and accuracy validation in production
  • A/B testing and gradual rollout procedures for new AI models
  • Rollback procedures for AI model failures or performance degradation
  • Documentation of model validation results and limitations

3.5.2 AI System Monitoring

Continuous Monitoring:

  • Real-time monitoring of AI system performance and accuracy
  • Anomaly detection for unusual AI system behavior or outputs
  • User feedback collection and analysis for AI system improvements
  • Regular audits of AI system decisions and outcomes
  • Incident detection and alerting for AI system failures

Performance Metrics:

  • Key performance indicators (KPIs) for AI system effectiveness
  • Accuracy, precision, recall, and other relevant metrics tracking
  • User satisfaction and experience metrics for AI applications
  • Business impact measurement of AI system implementations
  • Regular reporting on AI system performance to governance committee

3.6 Acceptable Use Guidelines

Specific guidelines shall govern the appropriate use of AI technologies by workforce members across different business functions.

3.6.1 General Use Guidelines

Permitted AI Use Cases:

  • Content creation assistance for marketing, documentation, and communications
  • Code generation and software development assistance
  • Data analysis and business intelligence support
  • Process automation and workflow optimization
  • Research and information gathering for business purposes

Prohibited AI Use Cases:

  • Clinical diagnosis or treatment recommendations without appropriate oversight
  • Automated decision-making for hiring, firing, or promotion without human review
  • Processing of ePHI through unauthorized AI systems
  • Generation of misleading, false, or deceptive content
  • Circumvention of security controls or policy violations

3.6.2 Role-Specific Guidelines

Healthcare and Clinical Staff:

  • AI clinical decision support tools must be FDA-approved or validated through appropriate processes
  • Human clinician review required for all AI-generated clinical recommendations
  • Patient consent required for AI system involvement in care delivery
  • Documentation of AI system use in patient medical records
  • Compliance with medical ethics and professional standards

Software Development Teams:

  • Code review required for all AI-generated code before production deployment
  • Security testing of AI-generated code for vulnerabilities
  • Intellectual property review for AI-generated content and code
  • Documentation of AI tool usage in development processes
  • Compliance with secure development lifecycle requirements

Business and Administrative Functions:

  • Data privacy review for AI applications processing personal information
  • Accuracy validation for AI-generated business documents and reports
  • Human review for AI-assisted decision-making processes
  • Compliance with regulatory requirements for automated processing
  • Documentation of AI system use in business processes

3.7 Training and Awareness

Comprehensive training programs shall ensure workforce members understand AI policies, risks, and best practices.

3.7.1 AI Training Requirements

General AI Awareness:

  • Annual training for all workforce members on AI acceptable use policies
  • Role-specific training for users of AI systems and tools
  • Ethics and bias awareness training for AI system developers and users
  • Privacy and security training for AI applications handling sensitive data
  • Regular updates on new AI technologies and policy changes

Specialized Training:

  • Advanced training for AI governance committee members
  • Technical training for AI system developers and administrators
  • Clinical training for healthcare staff using AI decision support tools
  • Legal and compliance training for AI oversight roles
  • Incident response training for AI-related security events

3.7.2 AI Literacy and Competency

Competency Assessment:

  • Regular assessment of workforce AI literacy and competency
  • Certification requirements for critical AI system users
  • Continuing education for AI technology developments
  • Knowledge sharing and best practices documentation
  • Performance evaluation integration of AI policy compliance

4. Standards Compliance

This policy is designed to comply with and support the following industry standards and regulations.

Policy Section Standard/Framework Control Reference
3.2.1 HIPAA Security Rule 45 CFR § 164.308(a)(4) - Information Access Management
3.2.1 HIPAA Privacy Rule 45 CFR § 164.502(b) - Minimum Necessary Standard
3.2.2 HIPAA Security Rule 45 CFR § 164.314(a)(1) - Business Associate Contracts
3.4 HIPAA Security Rule 45 CFR § 164.312(b) - Audit Controls
All SOC 2 Trust Services Criteria CC6.1 - Logical Access Security
3.2 SOC 2 Trust Services Criteria CC6.7 - Data Transmission and Disposal
3.1 SOC 2 Trust Services Criteria CC2.1 - Communication and Information
3.5 SOC 2 Trust Services Criteria CC8.1 - System Development
All EU AI Act Risk-based AI governance and compliance
3.3 NIST AI Risk Management Framework AI risk management and governance

5. Definitions

Algorithm Bias: Systematic prejudice in AI systems that results in unfair treatment of certain groups or individuals.

Artificial Intelligence (AI): Computer systems that can perform tasks typically requiring human intelligence, including learning, reasoning, and perception.

Business Associate Agreement (BAA): Contract required under HIPAA when third parties access or process ePHI on behalf of covered entities.

De-identification: Process of removing personal identifiers from data to protect individual privacy.

Explainable AI (XAI): AI systems designed to provide understandable explanations for their decisions and recommendations.

Large Language Model (LLM): Type of AI model trained on vast amounts of text data to understand and generate human-like text.

Machine Learning (ML): Subset of AI that enables systems to learn and improve from data without explicit programming.

Model Drift: Degradation in AI model performance over time due to changes in underlying data patterns.

6. Responsibilities

Role Responsibility
AI Ethics Officer Develop AI governance policies, oversee ethical AI practices, coordinate AI risk assessments, and ensure compliance with AI regulations.
AI Governance Committee Approve AI implementations, review AI risks, make policy decisions, and provide strategic guidance for AI initiatives.
Security Officer Ensure AI security controls, assess AI-related risks, monitor AI security incidents, and integrate AI into security programs.
Privacy Officer Ensure AI privacy compliance, oversee ePHI protection in AI systems, conduct privacy impact assessments, and manage AI-related privacy risks.
Data Scientists/AI Engineers Develop secure and ethical AI systems, implement bias testing, document AI model limitations, and ensure model validation and monitoring.
IT Security Team Implement AI security controls, monitor AI system security, respond to AI security incidents, and maintain AI security infrastructure.
Business Unit Leaders Ensure team compliance with AI policies, approve AI tool usage, provide business requirements for AI systems, and support AI governance activities.
Legal and Compliance Team Ensure AI regulatory compliance, review AI contracts and agreements, assess legal risks, and provide guidance on AI liability issues.
All Workforce Members Comply with AI acceptable use policies, report AI-related concerns, complete required AI training, and use AI tools responsibly and ethically.