In today’s digital healthcare landscape, protecting patient data has become more critical than ever. With systems interconnected across providers, insurers, and research organisations, the volume of sensitive data exchanged has skyrocketed, raising new challenges in maintaining privacy and security. Illamathy EN, Director, Engineering Product Management, Ascendion talks about safeguarding healthcare data
Generative AI (GenAI) is emerging as a transformative tool in healthcare—not only for improving clinical outcomes but also for enhancing data security. By leveraging techniques such as AI-powered predictive analytics, customised software solutions, natural language processing (NLP) for anomaly detection, and intelligent access controls, healthcare organisations can create robust security frameworks to safeguard patient data.
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By leveraging techniques such as AI-powered predictive analytics, customised software solutions, natural language processing (NLP) for anomaly detection, and intelligent access controls, healthcare organisations can create robust security frameworks to safeguard patient data.
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AI-powered predictive analytics for risk identification
The first step to safeguarding healthcare data is identifying risks proactively. As the volume of patient data grows, the possibility of data breaches, unauthorised access, and cyber-attacks increases. AI-driven predictive analytics provides a solution by detecting patterns in data usage and monitoring system behaviour.
AI models can analyse access logs to detect unusual activity patterns—such as frequent access to sensitive records (e.g., personal details, diagnostic reports, or prescriptions) by unauthorised individuals. Similarly, AI tracks user behaviour anomalies, such as employees accessing records outside working hours, from unfamiliar devices or locations, or generating suspicious network traffic.
- Example scenario: If a clinician attempts to access multiple patient records from an unexpected IP address, the AI system can flag the behaviour as suspicious and notify administrators.
AI models trained on historical data can also predict emerging threats like phishing attacks, ransomware attempts, or unauthorised access to sensitive systems, enabling organisations to take proactive security measures.
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As the volume of patient data grows, the possibility of data breaches, unauthorised access, and cyber-attacks increases. AI-driven predictive analytics provides a solution by detecting patterns in data usage and monitoring system behaviour.
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Customised software solutions to manage sensitive data information
As healthcare systems grow, managing sensitive data efficiently is becoming increasingly complex. GenAI-powered customised software solutions offer the ability to classify, analyse, and protect health information at varying levels of sensitivity.
GenAI models can classify data by type (e.g., clinical vs. billing) and assign risk levels (e.g., patient names and vitals as high sensitivity, benefits information as a medium, and visit dates as low sensitivity). Systems can apply stricter access controls to high-risk information such as mental health records while leaving routine data more accessible.
Additionally, synthetic data generation powered by GenAI helps maintain privacy during data sharing or testing. This involves creating artificial datasets that mimic real patient data and maintaining structure and relevance while masking personal identifiers to protect patient privacy.
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GenAI models can classify data by type (e.g., clinical vs. billing) and assign risk levels (e.g., patient names and vitals as high sensitivity, benefits information as medium and visit dates as low sensitivity).
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Natural Language Processing (NLP) for Anomaly Detection
A significant portion of healthcare data is unstructured, consisting of clinical notes, physician reports, patient narratives, and handwritten prescriptions. NLP-based systems help analyse this data to detect irregularities and flag potential risks.
- Example scenario: NLP models can identify inconsistencies between clinical notes and diagnostic reports, ensuring that errors or fraudulent activities are detected early.
NLP is also effective in monitoring internal communications such as emails and identifying potential insider threats, or policy violations. If an employee attempts to send confidential patient data through unauthorised channels, NLP systems can flag the message in real time, issue alerts, and block transmission to prevent data leakage. This proactive detection reduces compliance risks, minimises the chance of insider threats, and ensures that data-sharing practices adhere to regulations.
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NLP is also effective in monitoring internal communications such as emails and identifying potential insider threats, or policy violations.
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Enhanced access control mechanisms through AI
Traditional access control models like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are often rigid and unable to adapt to evolving scenarios. AI introduces intelligent, context-aware access controls, allowing healthcare systems to dynamically adjust access permissions in real time based on user behaviour and environmental factors.
- Example scenario: A physician can access test results they requested for a patient, but AI restricts access to other test results generated by a different physician unless authorised by an administrator.
GenAI systems analyse contextual information such as location, IP address, and previous access patterns to determine whether access requests are legitimate. For instance, if a clinician logs in from a new location, the system can either request multi-factor authentication (MFA) or block access if the behaviour appears suspicious.
This adaptive approach ensures that only the right individuals, under the right circumstances, have access to sensitive patient information, reducing the risk of unauthorised data breaches.
Additionally, GenAI can automate temporary access management by granting permissions for a limited time and revoking them automatically once the task is complete.
- Example scenario: A new user could be given 12-hour access to specific modules, with access revoked at the end of the period, reducing the risk of lingering permissions.
