Cloud services and artificial intelligence (AI) are increasingly embedded in healthcare delivery and operations. Patients schedule appointments, complete pre-check-ins and access lab results through online portals. Clinicians use AI to support X-ray interpretation and automate routine tasks. Leaders apply predictive analytics to plan staffing and bed-space allocation, while nurses monitor data from wearable sensors and other Internet of Things (IoT) devices to track vital signs in critical care units. These capabilities can improve operational efficiency and support clinical workflows, yet they also expand the attack surface and increase the number of locations where sensitive information is stored, processed and shared. As adoption grows, security controls need to evolve to protect patient privacy, data integrity and service continuity.
Cloud And GenAI Uptake Outpaces Security Consistency
Cloud computing continues to feature prominently in digital transformation. Although cloud technology has existed for decades, further growth is expected, and four of five healthcare organisations are using a public cloud provider. Many also identify accelerating cloud migrations as a priority for the next 12 to 24 months. Generative AI (genAI) is spreading quickly as well. Nearly nine in ten organisations are integrating cloud-based genAI into operations, and 98% use applications that incorporate genAI features.
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Greater use of SaaS and mobility can bring practical benefits, but rapid expansion can also lead to uneven security coverage. Adoption can move ahead of the security controls required to manage risk across users, devices, applications and data flows. In that context, cloud services and AI deployments can create points of exposure if policies, monitoring and access controls are not aligned to how services are used day to day.
Data Handling Pressures Rise with AI and Third-Party Access
Cyber criminals can exploit gaps created by fast-moving transformation. They may target cloud and AI environments to compromise networks and systems, steal information and launch ransomware attacks. Healthcare is being targeted more than any other critical infrastructure industry, with nearly 450 incidents a year. As online services expand, maintaining consistent data protection becomes more complex, particularly where access and processing take place across portals, analytics environments and connected clinical devices.
AI introduces additional data-handling risks because AI tools use data for training and can ingest substantial information through prompts. Without clear guardrails, sensitive information is more likely to be placed in locations where it should not be stored or shared, and compliance issues can arise when patient information is entered into an AI tool. These conditions are linked to concerns about patient privacy, data protection, data integrity and accuracy issues affecting the deployment of AI in clinical decision-making. The risk is shaped not only by the AI capability itself, but also by how data is routed, retained and governed across cloud services.
Exposure extends into third-party ecosystems. Healthcare organisations share access and data with numerous third parties, and 56% reported a breach involving a third party in the last 12 months. This reinforces the need to consider how data is protected across shared environments and partner relationships, rather than focusing only on internal networks and systems.
Architecture, Zero Trust and Compliance Oversight
Analytics emphasise the importance of end-to-end data protection as cloud services, AI tools and partner ecosystems grow. The goal is to know where healthcare data is and contain risk across cyber threats, data sharing and third-party exposure. Three measures are presented as central to strengthening resilience: adopting modern security frameworks, enforcing a zero trust approach and improving oversight of regulatory requirements.
A cloud-delivered model is described through Security Service Edge (SSE), delivering security from the cloud as a central mechanism to connect users to applications and websites in a safer, faster and more reliable way. Because SSE is geographically distributed, it is supporting multi-hospital networks without extensive infrastructure redesign. SSE is also helping examine AI tools, including inputs and outputs, to assess whether they meet governance policies for sharing or processing sensitive data.
SSE is presented as a component of Secure Access Service Edge (SASE). SASE is bringing network capabilities such as software-defined wide area networking (SD-WAN) together with SSE security capabilities into a unified, cloud-based architecture. This framing is intended to support a consolidated view of risk, combining access control, data protection and threat prevention.
Zero trust is applying continuous verification principles and using context signals to inform access decisions, including user identity, behaviours, device location, data sensitivity and risk factors. Least privilege is central, limiting clinicians, staff and vendors or contractors to the access required for their roles. It’s also important to highlight risks linked to sensitive data being placed into third-party genAI tools without appropriate controls and notes that rising agent adoption in healthcare can introduce new access risks.
Digital transformation is an ongoing journey as established technologies evolve and new capabilities emerge. At the same time, cyber adversaries’ tactics change, requiring security approaches to adapt. The objective is sustained resilience, keeping networks, systems and third-party supply chains operating despite attacks that can disrupt patient care and compromise sensitive information. A combination of cloud-delivered security frameworks, zero trust access controls and more centralised compliance oversight is presented as a way to manage risk as cloud and genAI adoption expands, while maintaining focus on patient privacy, data protection and operational continuity.
Source: Healthcare IT Today
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