AI Ethics and Compliance Policy (SEC-POL-013)

1. Objective

The objective of this policy is to establish comprehensive ethical guidelines and regulatory compliance requirements for the use of Artificial Intelligence (AI) and Machine Learning (ML) technologies at [Company Name]. This policy ensures that AI systems are developed, deployed, and used in accordance with ethical principles, regulatory requirements, and responsible AI practices while protecting individual rights, preventing bias and discrimination, and maintaining transparency and accountability. This policy focuses specifically on AI governance, ethical principles, regulatory compliance, and acceptable use guidelines while coordinating with technical security requirements defined in SEC-POL-012.

2. Scope

This policy applies to all [Company Name] workforce members, contractors, third parties, and business associates who use, evaluate, approve, or govern AI and ML technologies on behalf of the organization. It encompasses all AI applications including generative AI tools, machine learning models, automated decision-making systems, and AI-powered business applications regardless of their technical implementation. This policy covers AI governance, risk assessment, ethical evaluation, regulatory compliance, and acceptable use across all business functions including healthcare, administrative, and operational activities.

3. Policy

• [Company Name] shall implement comprehensive AI governance, ethical guidelines, and compliance controls to ensure responsible, fair, and compliant use of AI technologies while protecting individual rights, preventing discrimination, and maintaining regulatory compliance as coordinated with technical security requirements in SEC-POL-012.

3.1 AI Governance and Risk Management Framework

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

3.1.1 AI Governance Committee Structure

• Committee Composition and Leadership:
  • AI Governance Committee comprising representatives from Security, Privacy, Legal, Clinical, IT, Business units, and external ethics expertise
  • Designated AI Ethics Officer responsible for ethical AI oversight, compliance coordination, and organizational ethics leadership
  • Patient Advocate or Patient Representative for healthcare-related AI governance decisions
  • External Ethics Advisor or AI Ethics Consultant for independent perspective and specialized expertise
  • Executive Sponsor from senior leadership for strategic direction and resource allocation
• Governance Committee Responsibilities:
  • Strategic oversight of AI initiatives and alignment with organizational values and mission
  • Approval of new AI tools and applications based on ethical, compliance, and risk assessments
  • Policy development and maintenance for AI ethics, compliance, and acceptable use
  • Cross-functional coordination for AI incidents involving ethics, bias, or regulatory compliance
  • Annual review and update of AI governance policies, procedures, and risk appetite

3.1.2 AI Risk Assessment and Classification

• Comprehensive AI Risk Assessment Process:
  • Mandatory risk assessment for all new AI tools, significant changes to existing AI systems, and periodic review of deployed systems
  • Multi-dimensional risk evaluation including ethical implications, regulatory compliance, bias potential, and individual impact
  • Stakeholder impact assessment including patients, employees, customers, and communities affected by AI decisions
  • Data sensitivity analysis with specific focus on ePHI, PII, and other protected information categories
  • The completed risk assessment must be submitted to and formally approved by the AI Governance Committee prior to deployment
• AI Risk Classification Framework:
  • High Risk: AI systems making automated decisions affecting individuals, processing ePHI or Restricted data, or having significant ethical implications
  • Medium Risk: AI systems providing recommendations influencing human decisions, processing Confidential data, or affecting business-critical functions
  • Low Risk: AI systems for content assistance, processing only Public or Internal data, with limited individual or business impact
  • Regulatory Risk: Additional classification for AI systems subject to FDA approval, clinical validation, or other regulatory oversight
  • Risk classification determines approval authority, monitoring requirements, and compliance obligations

3.2 AI Ethics and Fairness Framework

Comprehensive ethical principles and fairness measures shall guide AI development, deployment, and usage to ensure responsible and equitable outcomes.

3.2.1 Ethical AI Principles

• Fairness and Non-Discrimination:
  • Commitment to preventing algorithmic bias and discrimination based on protected characteristics including race, gender, age, disability, and other legally protected categories
  • Regular bias testing and fairness evaluation for AI systems affecting hiring, promotion, patient care, or other individual decisions
  • Diverse and representative training data and validation datasets to minimize algorithmic bias and ensure equitable outcomes
  • Ongoing monitoring of AI system outcomes for disparate impact on protected groups with corrective action procedures
  • Documentation and reporting of fairness measures, bias testing results, and remediation activities
• Transparency and Explainability:
  • Clear documentation and communication of AI system capabilities, limitations, decision-making processes, and potential risks
  • Explainable AI (XAI) requirements for systems making decisions affecting individuals with understandable reasoning and justification
  • User notification and disclosure when individuals are interacting with AI systems or AI-generated content
  • Model interpretability measures for critical business and clinical decisions with accessible explanations
  • Regular communication about AI system changes, updates, and performance to affected stakeholders

3.2.2 Human Oversight and Control

• Human-in-the-Loop Requirements:
  • Mandatory human review and approval for AI-generated decisions affecting individuals including employment, healthcare, and financial decisions
  • Override capabilities and escalation procedures for all automated AI decisions with clear human authority
  • Training and competency requirements for workforce members supervising AI systems and making AI-assisted decisions
  • Clear escalation procedures for AI system malfunctions, unexpected outcomes, or ethical concerns
  • Regular validation of AI system performance, accuracy, and alignment with intended outcomes and ethical principles
• Individual Rights and Agency:
  • Right to human review for individuals affected by automated AI decisions with accessible appeal processes
  • Right to explanation for AI-generated decisions affecting individuals with clear and understandable reasoning
  • Opt-out procedures for individuals who prefer human-only decision-making where technically and operationally feasible
  • Consent and notification requirements for AI system involvement in healthcare delivery and patient care
  • Protection of individual autonomy and decision-making authority in AI-assisted processes

3.3 Regulatory Compliance and Data Protection

Comprehensive compliance controls shall ensure AI systems meet all applicable regulatory requirements and protect individual privacy and data rights.

3.3.1 Healthcare and Clinical AI Compliance

• Clinical AI Regulatory Requirements:
  • FDA approval or validation through appropriate regulatory processes for AI clinical decision support tools and medical devices
  • Clinical evidence and validation requirements for AI systems providing diagnostic, therapeutic, or clinical recommendations
  • Medical ethics and professional standards compliance for AI systems involved in patient care delivery
  • Patient safety monitoring and adverse event reporting for AI systems with clinical applications
  • Integration with clinical governance and quality assurance programs for AI-enabled healthcare delivery
• HIPAA and ePHI Protection:
  • Strict prohibition of ePHI processing in unauthorized AI systems without Business Associate Agreements (BAAs) and appropriate safeguards
  • De-identification requirements for healthcare data used in AI model training in accordance with HIPAA Privacy Rule standards (45 CFR § 164.514)
  • Safe Harbor method or Expert Determination for ePHI de-identification with documented methodology and validation
  • Re-identification prohibition and controls to prevent unauthorized linkage of de-identified data to individuals
  • Minimum necessary rule compliance for AI systems accessing ePHI with purpose limitation and data minimization

3.3.2 Privacy and Data Protection Compliance

• Privacy Rights and Data Subject Rights:
  • Individual rights implementation including access, rectification, erasure, and portability for AI systems processing personal data
  • Consent management and preference systems for AI data processing with granular control and easy withdrawal
  • Privacy impact assessments (PIAs) for AI applications processing personal data with risk mitigation measures
  • Cross-border data transfer restrictions and data localization requirements for AI services and data processing
  • Privacy by design principles integration into AI system development and deployment processes
• Data Minimization and Purpose Limitation:
  • Data minimization principles for all AI training and inference data ensuring only necessary data is collected and used
  • Purpose limitation and use restriction controls preventing AI data use beyond authorized purposes
  • Data retention and disposal requirements for AI systems with automated enforcement and compliance monitoring
  • Secondary use controls and governance for AI data repurposing with ethical review and approval
  • Anonymization and pseudonymization requirements for AI data processing with privacy protection validation

3.4 AI Acceptable Use and Guidelines

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

3.4.1 General Acceptable Use Guidelines

• Permitted AI Use Cases and Applications:
  • Content creation assistance for marketing, documentation, and communications with human review and validation
  • Code generation and software development assistance with security review and intellectual property compliance
  • Data analysis and business intelligence support with data protection and privacy compliance
  • Process automation and workflow optimization with human oversight and quality assurance
  • Research and information gathering for business purposes with accuracy validation and source attribution
• Prohibited AI Use Cases and Activities:
  • Clinical diagnosis or treatment recommendations without appropriate medical oversight, validation, and regulatory compliance
  • Automated decision-making for hiring, firing, promotion, or performance evaluation without human review and appeal processes
  • Processing of ePHI through unauthorized AI systems without BAAs and appropriate safeguards
  • Generation of misleading, false, or deceptive content including deepfakes, misinformation, or fraudulent materials
  • Circumvention of security controls, policy violations, or unauthorized access through AI assistance

3.4.2 Role-Specific AI Guidelines and Requirements

• Healthcare and Clinical Staff:
  • AI clinical decision support tools must be FDA-approved, clinically validated, or approved through institutional review processes
  • Mandatory human clinician review and validation for all AI-generated clinical recommendations and decisions
  • Patient consent and disclosure requirements for AI system involvement in care delivery with clear opt-out procedures
  • Documentation of AI system use in patient medical records with decision rationale and human oversight
  • Compliance with medical ethics, professional standards, and institutional clinical governance policies
• Software Development Teams:
  • Code review and security testing requirements for all AI-generated code before production deployment
  • Intellectual property review and clearance for AI-generated content and code with legal compliance validation
  • Security vulnerability assessment of AI-generated code with penetration testing and security scanning
  • Documentation of AI tool usage in development processes with audit trail and accountability measures
  • Compliance with secure development lifecycle requirements and integration with SEC-POL-012 technical controls
• Business and Administrative Functions:
  • Data privacy review and approval for AI applications processing personal information or sensitive data
  • Accuracy validation and fact-checking for AI-generated business documents, reports, and communications
  • Human review and approval for AI-assisted decision-making processes affecting individuals or business operations
  • Compliance with regulatory requirements for automated processing and algorithmic decision-making
  • Documentation and audit trail for AI system use in business processes with accountability and oversight

3.5 Third-Party AI Service Governance

Comprehensive governance controls shall ensure third-party AI services meet ethical, compliance, and contractual requirements.

3.5.1 AI Vendor Ethics and Compliance Assessment

• Vendor Ethics Evaluation:
  • Comprehensive assessment of third-party AI vendor ethical practices, governance frameworks, and responsible AI commitments
  • Review of vendor AI development practices including bias testing, fairness validation, and transparency measures
  • Evaluation of vendor data handling practices, privacy protection, and individual rights implementation
  • Assessment of vendor compliance with applicable regulations including healthcare, privacy, and AI-specific requirements
  • Due diligence review of vendor AI ethics policies, incident response procedures, and accountability measures

3.5.2 AI Service Contracts and Agreements

• Contractual Requirements and Protections:
  • Business Associate Agreements (BAAs) for AI services processing ePHI with HIPAA compliance and breach notification requirements
  • Data processing agreements with privacy protection, individual rights implementation, and compliance validation
  • Intellectual property protection and confidentiality agreements for AI service usage and data processing
  • Liability and indemnification clauses for AI-related risks including bias, discrimination, and compliance violations
  • Service level agreements including ethical AI requirements, transparency obligations, and audit rights

3.6 AI Training and Awareness Program

Comprehensive training and awareness programs shall ensure workforce members understand AI ethics, compliance requirements, and responsible use practices.

3.6.1 AI Ethics Training Requirements

• General AI Ethics Awareness:
  • Annual mandatory training for all workforce members on AI ethics principles, bias prevention, and responsible use practices
  • Role-specific training for AI system users including ethical decision-making and bias recognition
  • Ethics and fairness awareness training for managers and decision-makers using AI-assisted tools
  • Privacy and compliance training for workforce members handling AI systems processing personal data
  • Regular updates on emerging AI ethics issues, regulatory changes, and policy modifications
• Specialized Ethics Training Programs:
  • Advanced ethics training for AI Governance Committee members including ethical frameworks and decision-making models
  • Clinical ethics training for healthcare staff using AI decision support tools with patient safety and care quality focus
  • Legal and compliance training for AI oversight roles including regulatory requirements and liability issues
  • Leadership training on AI ethics governance, organizational culture, and stakeholder communication
  • Train-the-trainer programs for internal AI ethics champions and subject matter experts

3.6.2 AI Ethics Competency and Culture

• Ethics Competency Assessment and Development:
  • Regular assessment of workforce AI ethics literacy and competency with targeted improvement programs
  • Certification requirements for critical AI system users including ethics knowledge validation
  • Continuing education and professional development for AI ethics and responsible AI practices
  • Knowledge sharing and best practices documentation for AI ethics implementation and lessons learned
  • Performance evaluation integration of AI ethics compliance and responsible use practices
• Organizational AI Ethics Culture:
  • Clear communication of organizational AI ethics values, principles, and expectations from leadership
  • Recognition and reward programs for exemplary AI ethics practices and responsible innovation
  • Open reporting and discussion culture for AI ethics concerns without fear of retaliation
  • Regular organizational assessment of AI ethics culture and continuous improvement initiatives
  • External engagement and thought leadership in AI ethics and responsible AI development

3.7 Coordination with AI Development and Security

This policy coordinates with SEC-POL-012 (AI Development and Deployment Security Policy) to ensure comprehensive coverage of AI ethics, compliance, and technical security requirements.

3.7.1 Cross-Policy Integration and Coordination

• Ethics and Security Integration:
  • Technical security controls shall support and enable ethical AI principles and compliance requirements
  • Ethics review and approval processes shall coordinate with security assessments and technical validation
  • Incident response procedures shall integrate ethics and compliance considerations with technical security response
  • Governance and oversight activities shall coordinate ethics compliance with security and technical requirements
  • Training and awareness programs shall integrate ethics education with security and technical competency development

4. Standards Compliance

This AI Ethics and Compliance Policy aligns with and supports compliance requirements from multiple regulatory frameworks while coordinating with technical security requirements in SEC-POL-012.

4.1 Regulatory Compliance Mapping

Policy Section	Standard/Framework	Control Reference
3.1	HITRUST CSF v11.2.0	01.d - Information Security Governance
3.1	HITRUST CSF v11.2.0	01.e - Information Handling Requirements
3.2	HITRUST CSF v11.2.0	13.b - Information Security Awareness
3.3	HITRUST CSF v11.2.0	19.a - Data Protection and Privacy Policy
3.3	HITRUST CSF v11.2.0	19.d - Privacy Controls
3.5	HITRUST CSF v11.2.0	14.a - Third Party Assurance
3.6	HITRUST CSF v11.2.0	13.a - Information Security Education
3.3	HIPAA Security Rule	45 CFR § 164.308(a)(4) - Information Access Management
3.3	HIPAA Privacy Rule	45 CFR § 164.502(b) - Minimum Necessary Standard
3.3	HIPAA Privacy Rule	45 CFR § 164.514 - De-identification
3.5	HIPAA Security Rule	45 CFR § 164.314(a)(1) - Business Associate Contracts
3.3	HIPAA Privacy Rule	45 CFR § 164.522 - Rights to Request Privacy Protection
3.1, 3.2	SOC 2 Trust Services Criteria	CC2.1 - Communication and Information
3.3	SOC 2 Trust Services Criteria	PI1.1 - Privacy Notice and Communication
3.3	SOC 2 Trust Services Criteria	PI1.2 - Privacy Choice and Consent
3.3	SOC 2 Trust Services Criteria	PI1.3 - Privacy Collection
3.2	NIST AI Risk Management Framework	AI risk management and governance
3.2	NIST Privacy Framework	GV.PO - Governance and Privacy Objectives

5. Definitions

• Algorithmic Bias: Systematic prejudice in AI systems that results in unfair treatment of certain groups or individuals based on protected characteristics.

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

• De-identification: Process of removing personal identifiers from data to protect individual privacy in accordance with regulatory standards.

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

• Human-in-the-Loop: AI system design requiring human oversight, review, and decision-making authority for automated processes.

• Privacy Impact Assessment (PIA): Systematic assessment of privacy risks and mitigation measures for systems processing personal data.

• Responsible AI: Approach to AI development and deployment emphasizing ethical principles, fairness, transparency, and accountability.

6. Responsibilities

Role	Responsibility
AI Ethics Officer	Overall responsibility for AI ethics program, governance coordination, and integration with SEC-POL-012 technical requirements.
AI Governance Committee	Approval of AI implementations, ethics and compliance review, policy decisions, and strategic guidance for responsible AI initiatives.
Privacy Officer	AI privacy compliance oversight, ePHI protection validation, privacy impact assessments, and coordination with technical security controls.
Legal and Compliance Team	Regulatory compliance validation, contract review for AI services, legal risk assessment, and coordination with technical implementation teams.
Clinical Leadership	Healthcare AI governance, clinical validation requirements, patient safety oversight, and coordination with technical security measures.
Business Unit Leaders	Team compliance with AI ethics policies, business requirement validation, responsible AI culture development, and coordination with technical teams.
Training and Development Team	AI ethics education program delivery, competency assessment, and coordination with technical training requirements.
All Workforce Members	Compliance with AI ethics and acceptable use policies, responsible AI practices, and coordination with technical security requirements.