Protect Patient Data from AI Systems in Alignment with the New Code of Practice – A Guide for UK Healthcare Orgainisations

The UK healthcare sector finds itself at a pivotal moment in its digital transformation journey. As artificial intelligence revolutionises healthcare delivery, from diagnostic support to patient care management, healthcare organisations face the complex challenge of protecting sensitive patient data while harnessing AI’s potential to improve care outcomes. The UK Government’s new Code of Practice for AI cybersecurity arrives at a crucial time, providing essential guidance for healthcare organisations navigating this complex landscape.

The widespread adoption of AI technologies in the UK healthcare system brings unprecedented opportunities for improving patient care but also introduces new risks to patient data security and privacy. The government’s new Code of Practice establishes crucial requirements for protecting these AI systems and the sensitive patient data they process.

AI Risks in Healthcare

The introduction of artificial intelligence technologies into the healthcare sector brings with it a distinct set of challenges that require careful consideration under the newly established Code of Practice. The primary challenge is ensuring patient privacy, as AI systems often rely on large datasets that may contain sensitive nonpublic personal information (NPI), including personal or protected health information. Maintaining the confidentiality and security of this data is critical to protecting patient rights and complying with data privacy regulations like the Data Protection Act 2018.

There are other risks that fall outside the scope of this post. They include the accuracy and reliability of AI-driven tools, integrating AI with existing healthcare systems, ethical considerations regarding the decision-making processes of AI systems and their impact on patient outcomes, transparency in how AI algorithms make decisions, and more.

The new Code of Practice aims to address these challenges by setting standards and guidelines for the responsible deployment of AI technologies in healthcare. It emphasises the importance of prioritising patient welfare, safeguarding data privacy, and promoting transparency and accountability in the use of AI systems.

Healthcare organisations must understand these risks to implement effective protection measures while maintaining the quality and efficiency of patient care.

Electronic Health Records and Clinical Systems

The use of AI in electronic health record (EHR) systems represents one of the most sensitive areas requiring protection under the Code of Practice. Healthcare organisations must safeguard patient records while maintaining the accessibility necessary for effective care delivery. This delicate balance requires sophisticated security measures that protect against unauthorised AI access without compromising clinical operations.

Healthcare providers must protect patient data while ensuring AI systems can effectively support clinical decision-making. The Code of Practice provides crucial guidance for achieving this balance without compromising care quality.

Clinical Decision Support Systems

The protection of AI-powered clinical decision support systems presents another critical challenge under the Code. As these systems increasingly influence patient care decisions, healthcare organisations must implement robust security measures that protect both the AI models and the patient data they process.

The Code of Practice introduces essential requirements for protecting AI-enabled clinical support systems. Healthcare providers must now demonstrate that their AI implementations include sophisticated controls that prevent unauthorised access while maintaining clinical effectiveness.

Research and Development Data

The protection of medical research data requires particular attention under the new Code. Healthcare organisations must implement comprehensive security measures that protect valuable research data from unauthorised AI access while enabling legitimate AI-powered research and development activities. This entails implementing advanced encryption technologies, establishing strict access controls, and regularly auditing systems to detect and respond to potential breaches.

With the burgeoning integration of artificial intelligence in the medical field, there is an additional layer of complexity in data protection. Organisations must strike a delicate balance between shielding research data from unauthorised AI interventions and promoting the use of AI tools that can accelerate innovation in research and development. This involves creating clear guidelines on AI usage, conducting thorough risk assessments, and fostering an environment where AI can be harnessed responsibly to enhance healthcare outcomes. By doing so, healthcare entities can protect their research assets while also ensuring that AI technologies contribute positively to advancing medical science.

Aligning with the New Code of Practice

The Code mandates a sophisticated approach to risk assessment that goes beyond traditional healthcare security evaluations. Healthcare organisations must now consider not only direct security risks but also potential vulnerabilities introduced by AI systems’ interaction with patient data and clinical systems.

This assessment process requires organisations to evaluate:

The scope and nature of AI implementation across clinical operations, from patient care to administrative processes. Understanding these interactions helps organisations identify potential vulnerabilities and implement appropriate protection measures while maintaining care quality.

Healthcare organisations must carefully balance security controls with clinical accessibility. The Code’s risk assessment requirements help organisations identify and address AI-specific vulnerabilities without compromising patient care.

Technical Implementation Requirements

The Code provides specific guidance for implementing security measures in healthcare environments. Organisations must develop comprehensive security frameworks that protect sensitive patient data while maintaining clinical efficiency. This includes:

Sophisticated access control systems that can manage AI system permissions while maintaining strict security standards. These systems must be capable of handling complex clinical workflows while preventing unauthorised access to sensitive patient information.

Advanced monitoring capabilities that can detect potential security incidents without impacting clinical operations. Healthcare organisations must be able to track AI system behavior while maintaining the responsiveness required for modern healthcare delivery.

Training and Awareness Requirements

The Code of Practice emphasises specialised training for healthcare personnel, extending beyond traditional security awareness to focus specifically on AI-related risks and protective measures. Training requirements can be split into two sections: clinical staff development and operations.

Clinical Staff Development

Healthcare organisations must develop comprehensive training programs that address the unique challenges of protecting AI systems and patient data. These programs should cover both technical security measures and clinical operational considerations.

Healthcare staff must understand both the potential and the risks of AI systems in clinical settings. This understanding is crucial for maintaining security while leveraging AI to improve patient care.

Operational Integration

Training programs must be integrated into clinical workflows, ensuring that security awareness becomes part of the organisational culture. This includes regular updates and refresher courses that address emerging threats and new protection requirements under the Code. The intent is to make security awareness a fundamental aspect of the orgainisation’s culture. This involves implementing regular updates and refresher courses to keep staff informed about the latest threats and how to counter them.

Additionally, these programs should cover new protection requirements as outlined under the relevant Code, ensuring compliance and enhancing the orgainisation’s overall security posture. By continuously educating employees on emerging risks and evolving standards, the orgainisation can better safeguard sensitive information and maintain trust with patients and stakeholders.

Incident Response and Recovery Planning

The Code mandates sophisticated incident response capabilities specifically designed for AI-related security events in healthcare settings. Orgainisations must develop comprehensive plans that address both prevention and recovery while ensuring continuous patient care.

Response Framework Development

Orgainisations must establish clear procedures for identifying and responding to AI-related security incidents while maintaining critical clinical operations. These procedures should include:

Immediate response protocols that can be activated without disrupting patient care. The response framework must balance security requirements with the need to maintain essential healthcare services.

Escalation procedures that ensure appropriate stakeholders are involved in incident management, including clinical leadership and regulatory authorities when required.

Monitoring and Continuous Improvement

The Code highlights the importance of continuously assessing and improving the systems in place within healthcare organisations. It stresses that organisations should adopt advanced monitoring systems capable of providing real-time insights into the functioning of artificial intelligence applications. These systems are crucial for ensuring AI operates as intended and remains secure from potential threats. By facilitating ongoing oversight, these monitoring tools help healthcare providers identify and resolve issues promptly, thereby maintaining the integrity and reliability of the AI systems. Moreover, the emphasis is not only on system security but also on enhancing the quality of patient care. Real-time visibility into AI operations allows healthcare professionals to use data-driven insights to make informed decisions, ultimately leading to better patient outcomes. Continuous improvement is encouraged, ensuring that AI systems evolve over time to meet the changing demands and challenges within the healthcare environment. This proactive approach helps organisations remain adaptable and resilient, maintaining high standards of care and security.

Next Steps

The UK’s new Code of Practice represents a crucial development in protecting healthcare data from unauthorised AI access. Healthcare organisations must take decisive action to implement compliant security measures while maintaining efficient clinical operations and high-quality patient care. Essential steps include:

Immediate Actions

Healthcare organisations should begin by conducting thorough assessments of their current AI implementations and security measures. This evaluation should consider both technical requirements and impacts on clinical operations.

Strategic Planning

Orgainisations must develop comprehensive implementation strategies that address both immediate compliance requirements and long-term security objectives. These strategies should include clear timelines and resource allocation plans that account for clinical workflow requirements.

Ongoing Management

Successful implementation requires continuous monitoring and adjustment of security measures. Healthcare organisations should establish clear processes for ongoing management and improvement of their security programs while maintaining focus on patient care quality.

Implementing Kiteworks AI Data Gateway

Healthcare organisations can accelerate their compliance with the Code of Practice by leveraging Kiteworks AI Data Gateway. This comprehensive solution addresses key healthcare requirements through:

• Zero-Trust AI Data Access: The platform implements rigorous zero-trust principles specifically designed for AI interactions with patient data. This aligns directly with the Code’s requirements for strict access controls and continuous verification in clinical environments.
• Compliant Data Retrieval: Through secure retrieval-augmented generation (RAG), healthcare organisations can safely enhance AI model performance while maintaining strict control over sensitive patient data access. This capability is particularly crucial for organisations balancing AI innovation with patient privacy requirements.
• Enhanced Governance and Compliance: The platform’s robust governance framework helps healthcare organisations:
• Enforce strict data governance policies across clinical AI implementations
• Maintain detailed audit logs of all AI interactions with patient data
• Ensure compliance with both the Code of Practice and healthcare regulations
• Monitor and report on AI data access patterns in clinical settings
• Real-Time Protection: Comprehensive encryption and real-time access tracking provide the continuous monitoring and protection required by the Code, enabling healthcare organisations to:
• Protect sensitive patient data throughout its lifecycle
• Track and control AI system access to clinical information
• Respond rapidly to potential security incidents
• Maintain detailed compliance documentation for regulatory requirements

Through these capabilities, Kiteworks helps healthcare organisations achieve the delicate balance between enabling AI innovation and maintaining the strict data protection standards required by the Code of Practice while ensuring continuous, high-quality patient care.

With the Kiteworks Private Content Network organisations protect their sensitive content from AI risk with a zero trust approach to Generative AI. The Kiteworks AI Data Gateway offers a seamless solution for secure data access and effective data governance to minimise data breach risks and demonstrate regulatory compliance. Kiteworks provides content-defined zero trust controls, featuring least-privilege access defined at the content layer and next-gen DRM capabilities that block downloads from AI ingestion.

With an emphasis on secure data access and stringent governance, Kiteworks empowers you to leverage AI technologies while maintaining the integrity and confidentiality of your data assets.

To learn more about Kiteworks and protecting your sensitive data from AI ingestion, schedule a custom demo today.

Additional Resources

• Blog Post Kiteworks: Fortifying AI Advancements with Data Security
• Press Release Kiteworks Named Founding Member of NIST Artificial Intelligence Safety Institute Consortium
• Blog Post US Executive Order on Artificial Intelligence Demands Safe, Secure, and Trustworthy Development
• Blog Post A Comprehensive Approach to Enhancing Data Security and Privacy in AI Systems
• Blog Post Building Trust in Generative AI with a Zero Trust Approach