Best Practices for Ensuring AI Safety in Healthcare Institutions
As the integration of Artificial Intelligence (AI) continues to grow in the healthcare sector, it is crucial to establish a comprehensive approach to ensure AI security with a focus on privacy protections, risk assessments, and data governance.
Privacy Protections
To safeguard Protected Health Information (PHI) and electronic PHI (ePHI), healthcare organizations must strictly adhere to HIPAA regulations, including using encryption for data at rest and in transit, applying data minimization principles, and employing data de-identification or anonymization techniques when training AI models.
Clear patient consent frameworks are also essential, informing patients about data use in AI systems and providing opt-out options without compromising care access. Using HIPAA-compliant cloud and on-premises environments for AI data processing and storage, and managing vendor compliance through Business Associate Agreements (BAAs), further strengthens privacy protections.
Risk Assessments
Regular risk assessments of existing and planned AI tools are necessary to identify risks related to algorithmic bias, data quality, security vulnerabilities, and ethical concerns. Security audits and vulnerability assessments should be conducted to uncover potential points of failure and address them proactively.
AI-specific incident response policies must be implemented to prevent jeopardizing patient safety and data integrity. These policies should recognize challenges like black-box decision-making, false positives/negatives, and algorithmic transparency issues.
Data Governance Strategies
Developing strong governance frameworks that centralize oversight of AI data handling is essential. These frameworks should ensure compliance with privacy laws and internal policies, ideally supported by platforms such as Censinet RiskOps™ for unified management and rapid incident response.
Clear data governance policies should define roles and responsibilities for managing AI data, enforcing access controls, and maintaining detailed documentation. Regular staff training on AI security risks, privacy implications, and incident response protocols is also vital to maintain organizational readiness and compliance.
Additional Considerations
Leveraging AI itself to improve cybersecurity defenses, such as accelerating threat detection and response, should be considered. However, it is important to remain aware that attackers may also exploit AI techniques, for example, for phishing or malware creation.
IT professionals must understand new attack surfaces and start building a framework for addressing them. AI tools must be implemented safely and securely to protect patient information and outcomes. Pete Johnson, CDW's artificial intelligence field CTO, suggests using an in-house solution or a public model with privacy protections for securing AI in hospitals.
Artificial intelligence tools are being considered as an option for healthcare organizations to assist staff in daily workflows. With a balanced approach to AI transformation, healthcare organizations can reap the benefits of this technology while maintaining patient confidentiality, data security, and trust in healthcare services.
- To adhere to privacy protections, healthcare organizations should enforce HIPAA regulations, establish clear consent frameworks, use encryption, apply data minimization principles, and de-identify or anonymize AI models.
- Regular risk assessments are vital to identify risks related to biases, data quality, security vulnerabilities, and ethical concerns in AI tools, with security audits and incident response policies in place.
- Developing strong data governance frameworks is essential, which centralize oversight, ensure compliance, define roles & responsibilities, enforce access controls, and maintain detailed documentation.
- Considering AI for cybersecurity defense improvements, such as threat detection and response, is wise, but remain vigilant as attackers may exploit AI for malicious purposes like phishing or malware creation.
- Healthcare organizations can reap the benefits of AI while maintaining patient confidentiality, data security, and trust in healthcare services by embracing a balanced approach to AI transformation, ensuring secure implementations of AI tools in their daily workflows.
