Integrated governance

Responsible AI & Security Governance Framework

FullyKnow governs AI, cybersecurity, privacy, clinical safety, and enterprise risk as an integrated system because trustworthy AI depends on trustworthy data and accountable operations.

Governance questions

1. Does this benefit the individual?
2. Does it improve trust?
3. Does it reduce unnecessary risk?
4. Is it transparent and explainable?
5. Could the decision be explained publicly?

Governance structure

• Executive leadership.
• Responsible AI Governance Committee.
• Security and Privacy Office.
• Product governance and accountable delivery teams.
• Clinical, legal, compliance, and user-experience advisors.

Five governance pillars

1. Patient data governance

Consent, provenance, lineage, quality, auditability, access, retention, and lifecycle management.

2. Cybersecurity and zero trust

Continuous verification, least privilege, encryption, monitoring, vulnerability management, and incident response.

3. Responsible AI governance

Purpose definition, validation, fairness, transparency, human oversight, monitoring, drift detection, and retirement.

4. Clinical safety governance

Controls against inappropriate reliance, clear limitations, escalation paths, and qualified professional review.

5. Enterprise risk and compliance

Integrated management of cybersecurity, privacy, legal, regulatory, operational, third-party, continuity, and reputational risk.

AI lifecycle

1. Concept and intended use.
2. Design and impact assessment.
3. Development and documentation.
4. Validation and governance approval.
5. Controlled deployment.
6. Continuous monitoring and incident response.
7. Retirement.

Measures

Metrics may include incidents, model performance, drift, user concerns, response times, audit findings, corrective actions, and trust indicators.