Artificial Intelligence (AI) Governance
2025.10
Purpose
The purpose of this policy is to establish a comprehensive framework for the responsible and ethical development, deployment, and use of Artificial Intelligence (AI) systems within Bioscope AI. As an AI-first precision medicine company, we leverage cutting-edge AI technologies to solve complex omics challenges and deliver clinical insights to licensed healthcare providers. This policy aims to:
- Ensure compliance with all applicable laws, regulations, and ethical standards, including HIPAA and the HITECH Act
- Promote the responsible, transparent, and accountable use of AI technologies in healthcare applications
- Mitigate potential risks associated with AI, including bias, privacy violations, and security vulnerabilities
- Foster trust and confidence in Bioscope AI’s use of AI to process genomic patient data and deliver clinical recommendations
- Outline clear roles, responsibilities, and processes for AI governance across all organizational functions
- Position Bioscope AI as a leader in responsible AI development for precision medicine
Scope
This policy applies to:
- All employees, contractors, partners, and stakeholders involved in the development, implementation, management, or use of AI systems at Bioscope AI
- All AI systems deployed in production environments that process electronic protected health information (ePHI), including genomic patient data
- HIPAA-compliant AI/ML services utilized through AWS Bedrock and GCP Vertex AI platforms
- Third-party AI tools and services used for internal business purposes
- AI models and systems under development or evaluation for potential deployment
Background
Bioscope AI specializes in applying advanced artificial intelligence and machine learning technologies to address some of the most challenging problems in omics and precision medicine. Our AI pipeline processes electronic protected health information (ePHI), including genomic patient data, to deliver actionable recommendations to licensed healthcare providers.
We leverage HIPAA-compliant Large Language Models (LLMs) and other AI/ML services through AWS Bedrock and GCP Vertex AI to ensure the highest standards of data security and regulatory compliance. Our commitment to tracking and implementing the latest technical advances in AI enables us to provide cutting-edge solutions while maintaining rigorous ethical and compliance standards.
This policy acknowledges both the transformative potential and inherent risks of AI in healthcare and establishes a governance framework to ensure that AI is used responsibly, ethically, and in full compliance with healthcare regulations.
This policy is aligned with Bioscope AI’s mission, values, and commitment to protecting the privacy and security of patient health information.
Definitions
Artificial Intelligence (AI): The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, genomic analysis, predictive analysis, and natural language processing. This includes, but is not limited to, generative AI, machine learning, deep learning, natural language processing (NLP), and computer vision.
AI System: A specific implementation of AI technology, including software, hardware, data, and processes, designed to perform a specific task or set of tasks. At Bioscope AI, this includes our production AI pipeline that processes genomic data and generates clinical recommendations.
AI Model: A learned representation of data used by an AI system to make predictions, classifications, or decisions. This includes large language models (LLMs), predictive models for genomic analysis, and other machine learning models deployed in our systems.
Production AI Systems: HIPAA-compliant AI systems deployed in production environments through AWS Bedrock and GCP Vertex AI that process ePHI and deliver clinical insights to healthcare providers.
Third-Party AI Tools: External AI services (such as ChatGPT, Claude, or similar tools) used for internal business purposes that must never process sensitive or confidential information.
Data Privacy: The protection of personal information, including protected health information (PHI) and genomic data, from unauthorized access and misuse.
Data Security: The appropriate handling of data to ensure the confidentiality, integrity, and availability of data and compliance with applicable privacy laws, regulations, and policies, including HIPAA Security Rule requirements.
Bias: Systematic and unfair discrimination or prejudice in AI outputs due to flawed or unrepresentative data, algorithms, or development processes. In healthcare AI, this includes potential disparities in model performance across different demographic groups.
Transparency: The extent to which the inner workings of an AI system are understandable and explainable to relevant stakeholders, including healthcare providers and patients.
Accountability: The ability to assign responsibility for the outcomes and actions of an AI system, including clinical recommendations generated by AI models.
HIPAA: The Health Insurance Portability and Accountability Act of 1996, as amended, including implementing regulations of United States federal law that protects the privacy and security of individuals’ protected health information (PHI).
ePHI (Electronic Protected Health Information): Any PHI that is created, stored, transmitted, or received electronically, including genomic patient data processed by Bioscope AI’s systems.
Genomic Data: Genetic and molecular information about an individual derived from sequencing or analysis of DNA, RNA, or related biological materials, which constitutes highly sensitive ePHI.
Clinical Decision Support: AI systems that assist licensed healthcare providers in making clinical decisions by analyzing patient data and providing recommendations, predictions, or insights.
Policy Statements
A. Compliance with Laws and Regulations
All AI systems developed, deployed, or used by Bioscope AI must comply with all applicable laws, regulations, and ethical standards, including but not limited to:
- HIPAA Privacy Rule (45 CFR Part 160 and Part 164, Subparts A and E)
- HIPAA Security Rule (45 CFR Part 164, Subpart C)
- HITECH Act
- FDA regulations applicable to clinical decision support software
- State-specific healthcare data protection laws
- Applicable AI safety and ethics guidelines
B. Data Privacy and Security
AI systems handling ePHI, including genomic patient data, must adhere to the strictest data privacy and security standards:
Access Controls: Implement role-based access controls (RBAC) and the principle of least privilege to protect ePHI from unauthorized access. All access to production AI systems processing ePHI must be logged and audited.
Encryption: Use strong encryption to protect ePHI both at rest and in transit. All data transmitted to and from AWS Bedrock and GCP Vertex AI must use TLS 1.2 or higher. All storage of ePHI must use AES-256 encryption or equivalent.
Security Assessments: Conduct regular security assessments, vulnerability scanning, and penetration testing of all AI systems processing ePHI.
Incident Response: Maintain and regularly test incident response procedures for data breaches involving AI systems. All breaches must be reported in accordance with HIPAA Breach Notification requirements.
Data Minimization: Ensure that AI systems process only the minimum necessary ePHI required for their intended purpose.
Purpose Limitation: Restrict the use of ePHI in AI systems to specified, explicit, and legitimate purposes related to clinical decision support.
Secure Development: Follow secure software development lifecycle (SDLC) practices for all AI systems, including threat modeling, secure coding practices, and security testing.
C. Production AI Systems for Clinical Use
Bioscope AI’s production AI systems that process genomic ePHI and deliver clinical recommendations must meet enhanced requirements:
HIPAA Compliance: All production AI systems must operate on HIPAA-compliant infrastructure (AWS Bedrock, GCP Vertex AI) with executed Business Associate Agreements (BAAs) in place.
Clinical Validation: AI models used for clinical decision support must undergo rigorous validation to ensure accuracy, reliability, and clinical utility before deployment.
Human Oversight: All AI-generated clinical recommendations must be subject to review and approval by licensed healthcare providers. AI systems augment, but do not replace, clinical judgment.
Genomic Data Handling: Special controls must be implemented for genomic data processing, recognizing its highly sensitive and identifiable nature:
- Genomic data must never be used for model training without explicit consent and de-identification
- Access to genomic data must be strictly controlled and audited
- Genomic data retention must follow documented policies and legal requirements
Model Performance Monitoring: Continuous monitoring of model performance, including accuracy, fairness across demographic groups, and detection of model drift or degradation.
Regulatory Compliance: Ensure compliance with FDA regulations for clinical decision support software and other applicable healthcare AI regulations.
D. Third-Party AI Tools
The use of external, non-controlled AI tools (such as ChatGPT, Claude, Copilot, or similar services) is permitted only under the following conditions:
Prohibited Uses: Third-party AI tools may NEVER be used to process:
- Protected Health Information (PHI) or electronic Protected Health Information (ePHI)
- Genomic data or patient information of any kind
- Confidential business information or trade secrets
- Security credentials, API keys, or access tokens
- Data subject to contractual confidentiality obligations
Permitted Uses: Third-party AI tools may be used for:
- Document template generation for non-sensitive business purposes
- Marketing language review and content creation
- General research and information gathering on public topics
- Code assistance for non-production, non-sensitive development tasks
Vendor Assessment: All third-party AI tools must undergo vendor risk assessment before organizational use is approved.
Training and Awareness: Employees must receive training on the appropriate use of third-party AI tools and the risks of data exposure.
E. Ethical Considerations
AI systems must be developed and used ethically, considering potential impacts on fairness, equity, and human well-being:
Bias Mitigation: AI systems must be designed, trained, and monitored to minimize bias and discrimination. Special attention must be paid to ensuring fair performance across diverse patient populations, including different:
- Racial and ethnic groups
- Age groups
- Genders
- Socioeconomic backgrounds
- Geographic locations
Transparency and Explainability: AI systems used for clinical decision support should provide explanations for their recommendations to the extent technically feasible. Healthcare providers must understand the basis for AI-generated insights.
Human Autonomy: AI systems must be designed to respect and support human autonomy and decision-making. Clinical AI systems must augment, not replace, the professional judgment of licensed healthcare providers.
Beneficence and Non-Maleficence: AI systems must be designed to maximize benefit and minimize harm to patients and healthcare providers.
Privacy Respect: AI development and deployment must respect patient privacy rights and honor individual preferences regarding data use.
F. Risk Management
Pre-Deployment Risk Assessment: A comprehensive risk assessment must be conducted before deploying any AI system that processes ePHI. This assessment must identify and evaluate:
- Data privacy and security risks
- Clinical safety risks
- Bias and fairness risks
- Regulatory compliance risks
- Technical performance risks
- Operational risks
Risk Mitigation: Identified risks must be addressed through appropriate mitigation strategies before system deployment. Residual risks must be documented and accepted by appropriate stakeholders.
Ongoing Risk Monitoring: Continuous monitoring of deployed AI systems to detect new or emerging risks, including:
- Security vulnerabilities
- Model performance degradation
- Bias or fairness issues
- Regulatory changes requiring system updates
G. Transparency and Explainability
Model Documentation: All production AI models must be thoroughly documented, including:
- Model architecture and algorithms
- Training data sources and characteristics
- Performance metrics and validation results
- Known limitations and potential biases
- Intended use cases and contraindications
Decision Transparency: For clinical decision support systems, provide explanations of AI recommendations to healthcare providers when technically feasible.
Auditability: Maintain comprehensive audit trails of AI system decisions, data access, and system modifications to support compliance audits and investigations.
H. Human Oversight and Control
Clinical Oversight: Human oversight by licensed healthcare providers is required for all AI-generated clinical recommendations. AI serves as a decision support tool, not a replacement for clinical judgment.
Escalation Procedures: Establish clear escalation paths for situations requiring human intervention, including:
- AI system uncertainty or low confidence outputs
- Detection of potential safety issues
- Unusual or unexpected results
- System errors or malfunctions
Override Capability: Healthcare providers must retain the ability to override AI recommendations based on their clinical judgment and patient-specific factors.
Feedback Mechanism: Enable healthcare providers to provide feedback on AI recommendations to support continuous improvement.
I. Training and Awareness
Role-Based Training: Employees involved in AI development, deployment, or use must receive appropriate role-based training:
- Developers and Data Scientists: AI ethics, secure development practices, bias detection and mitigation, HIPAA compliance
- Healthcare Providers: Appropriate use of AI decision support tools, understanding of AI capabilities and limitations
- Operations and IT: AI system monitoring, incident response, security practices
- All Employees: Appropriate use of third-party AI tools, data protection requirements
Continuous Education: Regular training updates to reflect evolving AI technologies, regulatory requirements, and organizational policies.
Competency Assessment: Periodic assessment of AI-related competencies for personnel in critical roles.
J. AI System Documentation
All AI systems processing ePHI must maintain comprehensive documentation:
System Architecture: Detailed documentation of system design, components, data flows, and integration points.
Data Management: Documentation of data sources, data processing methods, data quality controls, and data retention policies.
Model Details: Complete documentation of algorithms, models, training methodologies, and validation approaches.
Performance Metrics: Ongoing documentation of model performance, including accuracy, precision, recall, fairness metrics, and clinical utility measures.
Security Controls: Documentation of implemented security controls, access management, encryption methods, and audit mechanisms.
Change Management: Version control and change history for all AI system components, including models, code, and configurations.
K. Monitoring and Auditing
Continuous Monitoring: AI systems must be continuously monitored for:
- Performance metrics and model accuracy
- Bias and fairness indicators
- Security events and anomalies
- Data quality issues
- System availability and reliability
Regular Audits: Conduct regular audits of AI systems, including:
- Compliance with HIPAA Security and Privacy Rules
- Adherence to this AI Governance Policy
- Bias and fairness assessments
- Security vulnerability assessments
- Clinical validation of decision support outputs
Audit Documentation: Maintain comprehensive documentation of all audits, findings, and remediation actions.
Third-Party Audits: Support external audits by regulatory authorities, certification bodies, and business partners as required.
L. Continuous Improvement
Policy Review: This AI Governance Policy will be reviewed and updated at least annually or more frequently as needed to reflect:
- Changes in AI technology and capabilities
- New regulatory requirements
- Organizational changes
- Lessons learned from incidents or audits
- Industry best practices
System Enhancement: Continuously improve AI systems based on:
- Performance monitoring results
- User feedback from healthcare providers
- Advances in AI technology
- Identified fairness or bias issues
- Changing clinical needs
Research and Innovation: Bioscope AI is committed to tracking and evaluating the latest technical advances in AI to solve complex omics problems while maintaining rigorous ethical and compliance standards.
Governance Structure
A. AI Governance Committee
An AI Governance Committee (AIGC) is established to oversee the implementation and adherence to this policy.
Committee Composition:
- Chief Information Security Officer (CISO) - Chair
- Head of Engineering
- AI Ethics Officer
- Representatives from Engineering, Security, and Operations teams
Committee Responsibilities:
- Review and approve AI system deployments to production environments
- Establish guidelines and acceptable use policies for AI technologies
- Monitor compliance with ethical and regulatory standards
- Oversee risk management and mitigation strategies for AI systems
- Evaluate the effectiveness of AI systems and this policy
- Review and approve changes to AI Governance Policy
- Investigate and address AI-related incidents or ethics concerns
- Monitor for potential bias, discrimination, or harm resulting from AI systems
Meeting Frequency: The AIGC meets at least quarterly, with additional meetings as needed for urgent matters.
B. AI Ethics Officer
An AI Ethics Officer is designated to provide expertise on ethical issues related to AI and to serve as a point of contact for ethical concerns or violations.
AI Ethics Officer Responsibilities:
- Advise on ethical considerations during AI system development and deployment
- Conduct ethics reviews of proposed AI systems and use cases
- Investigate and address ethical issues or complaints related to AI systems
- Facilitate training on ethical AI use and practices
- Monitor AI systems for ethical compliance and potential harm
- Participate in AI Governance Committee meetings
- Stay informed of evolving AI ethics standards and best practices
- Report significant ethical concerns to senior leadership and the AIGC
Reporting Structure: The AI Ethics Officer reports to the CISO and has a dotted line to the CEO for escalation of critical ethical concerns.
C. Senior Leadership Accountability
The CEO and senior leadership team are ultimately responsible for ensuring that Bioscope AI adheres to AI governance principles and this policy. Senior leadership must:
- Allocate appropriate resources for responsible AI development and governance
- Foster a culture of ethical AI development and use
- Support the AI Governance Committee and AI Ethics Officer
- Ensure accountability for AI-related incidents or policy violations
- Communicate AI governance priorities throughout the organization
Development and Deployment Procedures
A. Assessment and Approval Process
Before any AI system processing ePHI is deployed to production, it must complete a multi-stage approval process:
Technical Review:
- Architecture and design review
- Code quality and security review
- Performance and scalability assessment
- Integration and testing validation
Security Risk Analysis:
- Threat modeling and risk assessment
- Security control validation
- Penetration testing results
- HIPAA Security Rule compliance verification
Ethical Review:
- Bias and fairness assessment
- Transparency and explainability evaluation
- Human oversight and control mechanisms
- Patient privacy impact assessment
Legal and Compliance Review:
- HIPAA compliance verification
- Business Associate Agreement (BAA) validation
- FDA regulatory requirements (if applicable)
- Data use agreement and consent verification
Clinical Validation (for clinical decision support systems):
- Clinical utility assessment
- Performance validation on representative patient populations
- Healthcare provider usability evaluation
- Comparison to existing clinical standards
Final Approval:
- AI Governance Committee review and approval
- Sign-off by CISO, Head of Engineering, and relevant stakeholders
- Documentation of approval decision and conditions
B. Documentation Requirements
Comprehensive documentation must be created and maintained for all AI systems:
System Design Documentation:
- Architecture diagrams and technical specifications
- Data flow diagrams
- Integration points and dependencies
- Infrastructure and deployment configuration
Model Documentation:
- Algorithm description and rationale
- Training data sources, size, and characteristics
- Training methodology and hyperparameters
- Validation approach and results
- Performance metrics across different patient populations
- Known limitations and contraindications
Security Documentation:
- Threat model and risk assessment
- Implemented security controls
- Access control policies and procedures
- Encryption methods and key management
- Audit logging configuration
Operational Documentation:
- Deployment procedures
- Monitoring and alerting configuration
- Incident response procedures
- Maintenance and update procedures
- Disaster recovery and business continuity plans
Compliance Documentation:
- HIPAA compliance assessment
- Privacy impact assessment
- Business Associate Agreements
- Regulatory filing documentation (if applicable)
Documentation must be maintained in version control systems and updated regularly to reflect system changes.
C. Change Management
All changes to production AI systems must follow the established change management process:
- Changes must be documented, tested, and approved before implementation
- Security and compliance impacts must be assessed for all changes
- Changes to AI models require re-validation and approval by the AIGC
- All changes must be tracked in Linear (PRODCM project) and approved via GitHub Actions workflows
- Rollback procedures must be documented and tested for all changes
Monitoring and Evaluation
A. Continuous Monitoring
AI systems processing ePHI must be continuously monitored for:
Performance Metrics:
- Model accuracy, precision, recall, and other relevant metrics
- Inference latency and system response times
- System availability and uptime
- Error rates and exception handling
Fairness and Bias Indicators:
- Performance disparities across demographic groups
- Representation of different patient populations in processed data
- Potential sources of algorithmic bias
- Fairness metrics (e.g., demographic parity, equalized odds)
Security Events:
- Access attempts and authorization failures
- Data access patterns and anomalies
- Security control effectiveness
- Potential security incidents or breaches
Data Quality:
- Completeness and accuracy of input data
- Data drift or distribution changes
- Missing or invalid data patterns
- Data integrity verification
Clinical Outcomes (for clinical decision support):
- Healthcare provider acceptance and use rates
- Clinical workflow integration effectiveness
- Patient outcome impacts (where measurable)
- Provider feedback and reported issues
B. Regular Audits and Reviews
Quarterly Reviews:
- Review of monitoring data and system performance
- Assessment of incidents and issues
- Evaluation of compliance with this policy
- Discussion of improvements and enhancements
Annual Comprehensive Audits:
- Full compliance audit against HIPAA requirements
- Security vulnerability assessment and penetration testing
- Bias and fairness comprehensive assessment
- Clinical validation review (for decision support systems)
- Review and update of all AI system documentation
Incident-Triggered Reviews:
- Investigation of security incidents involving AI systems
- Analysis of AI system failures or errors
- Review of bias or fairness concerns
- Assessment of clinical safety events
C. Feedback Mechanisms
Provider Feedback: Establish accessible channels for healthcare providers to report concerns, provide feedback, or request clarification about AI system recommendations.
Internal Reporting: Enable employees to report AI-related concerns or potential policy violations through:
- Direct reporting to the AI Ethics Officer
- Anonymous reporting through established whistleblower channels
- Incident reporting in Linear ticketing system
- Discussion in relevant Slack channels (#infosec, #ai-governance)
Patient Concerns: Establish procedures for addressing patient concerns about AI use in their care, in coordination with healthcare provider partners.
Feedback Integration: Systematically review and integrate feedback into AI system improvements and policy updates.
Enforcement and Accountability
A. Policy Violations
Violations of this AI Governance Policy are taken seriously and will be addressed promptly:
Investigation: All reported or suspected policy violations will be investigated by the AI Ethics Officer in coordination with the Security and Compliance teams.
Disciplinary Action: Violations may result in disciplinary action up to and including:
- Mandatory retraining
- Suspension of AI system access privileges
- Formal written warning
- Performance improvement plan
- Termination of employment or contractual agreements
System Actions: AI systems found to be in violation of this policy may be:
- Immediately disabled or taken offline
- Subject to mandatory remediation
- Required to undergo re-approval process
- Permanently decommissioned
Legal Consequences: Violations that result in regulatory non-compliance, data breaches, or harm may result in:
- Regulatory enforcement actions
- Civil or criminal liability
- Contractual penalties
- Reputational harm
B. Reporting Requirements
Internal Reporting: Employees and stakeholders must report:
- Suspected policy violations
- AI system malfunctions or unexpected behavior
- Potential bias or fairness concerns
- Security incidents involving AI systems
- Patient safety concerns related to AI systems
External Reporting: The organization will report to external parties as required:
- HIPAA breach notifications to HHS, affected individuals, and potentially media
- FDA adverse event reports (if applicable)
- State data breach notifications as required
- Business partner notifications per contractual obligations
Reporting Channels:
- AI Ethics Officer: [Contact information to be specified]
- Security Team: security@bioscope.ai
- Linear ticketing system: IT/Security or Compliance projects
- Anonymous hotline: [To be established]
Policy Approval and Maintenance
A. Approval Authority
This AI Governance Policy must be approved by:
- Chief Executive Officer (CEO)
- Chief Information Security Officer (CISO)
- Head of Engineering
- AI Governance Committee
B. Policy Review and Revision
Scheduled Reviews: This policy will be reviewed at least annually by the AI Governance Committee to ensure its continued effectiveness and relevance.
Triggered Reviews: Policy reviews will be conducted when:
- Significant changes to AI technology or capabilities occur
- New regulations or guidance affecting AI use are issued
- Significant incidents or policy violations occur
- Organizational changes impact AI governance structure
- Industry best practices evolve
Revision Process:
- Proposed revisions are drafted by the AI Governance Committee
- Revisions are circulated to stakeholders for review and comment
- Final revisions are approved by the AIGC and senior leadership
- All employees are notified of policy changes
- Training is updated to reflect policy changes
Version Control: All versions of this policy are maintained with:
- Version number and date
- Summary of changes
- Approval signatures
- Effective date
Applicable Regulations and Standards
This policy is designed to comply with and align to the following regulations and standards:
- Health Insurance Portability and Accountability Act (HIPAA)
- Health Information Technology for Economic and Clinical Health (HITECH) Act
- National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF)
- NIST Cybersecurity Framework
- FDA regulations for clinical decision support software (21 CFR Part 820, when applicable)
- Executive Order 14110 on Safe, Secure, and Trustworthy AI
- CIS Benchmarks for cloud infrastructure security
- ISO/IEC 27001 Information Security Management
- OWASP guidelines for secure AI system development
References and Resources
- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
- Executive Order 14110 (October 2023): https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
- HHS HIPAA for Professionals: https://www.hhs.gov/hipaa/for-professionals/index.html
- FDA Clinical Decision Support Software: https://www.fda.gov/medical-devices/software-medical-device-samd/clinical-decision-support-software
Document Information
Policy Owner: Chief Information Security Officer (CISO)
Approved By:
- CEO
- CISO
- CTO
- AI Governance Committee
Current Version: 2025.10
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