Advanced search analytics platforms can help boost data security

To better protect information, healthcare organizations need to better understand the impact of enterprise search, observability and security.

Data is everywhere. Internet of Things devices, tool sprawl, under maintained systems and a growing database resulting from collective data compromises not associated with the healthcare system have provided a treasure trove for threat actors to build customized exploits.

The attack surface for ransomware, cybercriminals and state-sponsored adversaries to probe needs a total awareness capability. Healthcare administrators and workers also retrieve data from multiple disparate resources for their own analytics. The insights garnered enable decision-making and operational efficiency. At the same time, internetworked, geographically disconnected providers share hospital records, patient medical records, medical examination results, research and more.

The exponential proliferation of data is now the ubiquity of data. This overwhelming stream of bits and bytes impacts how it is defended, collected, analyzed and stored. What systems do health providers have in place to keep an eye out for anomalies?

To keep data secure, healthcare agencies and providers should be able to keep the data where it is generated. By creating a data mesh – which unites disparate data sources and links them together through centrally managed data sharing and governance guidelines – security teams can analyze cybersecurity telemetry data and protect data wherever it resides. 

Enterprise search, observability and security 

Search: Having a powerful search analytics platform that combines search, observability and security capabilities can help healthcare organizations protect their data from attacks in the current cyber threat landscape. 

Healthcare organizations need a search analytics platform that provides powerful data analytics and can search for applications and documents across multiple systems. If the search capabilities leverage the power of artificial intelligence and machine learning, relevant data can be more quickly found and retrieved, no matter its location. This makes it easier for healthcare professionals to find data fast while complying with state and federal healthcare requirements. 

There are foundational technical building blocks that should be in place for a search analytics platform to ingest, analyze, and eventually protect data, such as support for supervised and unsupervised learning; transformer models or deep learning models for natural language processing tasks; native support for vector search and embedding creation; and support for domain-specific Generative AI (Gen AI) models.

Search-powered AI capabilities should include support for forecasting, anomaly detection in IT operations (AIOps), machine learning and detection rules in security, and security playbooks generated by Gen AI.

Observability. This involves the constant monitoring of applications and the underlying IT infrastructure for availability, examining network and security logs, and metrics and application performance. Ideally, IT and security teams should be able to search across all log data from a single console. Availability, which is also a part of security, means ensuring that critical systems are available when needed. Teams should be capable of detecting patterns, outliers, and anomalies to isolate performance issues.

Modern technology stacks and cloud infrastructures enhance business agility but add more complexity to IT environments. This means reducing the mean time to repair (MTTR) – the average time it takes to repair a system – is more challenging than ever before. To that end, healthcare IT and security teams need to monitor the entire IT ecosystem to understand what is wrong, where errors are occurring and why.

As a result, a search analytics platform must be comprehensive and holistic. From the start of data ingestion, IT and security teams and data owners must know where data resides. Moreover, with the proliferation of data comes a plethora of different programming languages. It is essential for IT operations, security teams, and data owners to understand application data performance from the users’ perspective to achieve a better user experience. Analytics and search-powered AI anomaly detection and root cause analysis are imperative for observability across the entire technology ecosystem.

Cybersecurity. Capabilities that merge a security information and event management (SIEM) system and endpoint detection and response (EDR) together will help minimize blind spots. Even legacy devices provide some necessary services; therefore, security teams must confirm they remain uncompromised.

The components in action

Gen AI has the potential to change the healthcare sector faster than many people realize. Using a search analytics platform with a powerful indexing engine that can manage vast amounts of structured and unstructured medical data enables healthcare professionals to use Gen AI to search data quickly for prediction and diagnosis in the areas of disease detection and clinical trial optimization.

Disease detection. Gen AI can help healthcare organizations with disease prediction and diagnosis by analyzing vast amounts of patient data. This data can include patient health records, lifestyle risk factors, medical imaging, environmental determinants and unique genetic makeup.

Combining real-time search analytics, data visualization, powerful indexing and machine learning capabilities, healthcare professionals can detect discrepancies in large data sets. They can create sophisticated models to identify patterns, abnormalities and indicators associated with specific diseases using data visualization capabilities. This can help in early detection and accurate diagnosis of conditions like cancer, cardiovascular diseases, and neurological or genetic disorders.

Clinical trial optimization. Clinical trials are the backbone for driving medical advancements and breakthrough treatments. However, they come with significant challenges. Recruiting and retaining patients for clinical trials is difficult because it requires specific inclusion or exclusion criteria that must be analyzed across vast data sets. A search analytics platform with data visualization capabilities enables healthcare experts to ingest massive data sets and then run the data through machine learning services to determine which patients meet the criteria for specific clinical trials.

Healthcare experts can apply Gen AI with advanced or real-time search analytics early in the trial process by analyzing patient information, such as eligibility criteria, demographic information and medical history, to identify eligible participants more efficiently than traditional recruitment practices. In addition, they can rapidly analyze and interpret data patterns and trends on trial progress, patient responses, and any adverse issues in real time.

Patient information sharing. Collaboration and interoperability are crucial for secure information sharing in search analytics platforms. Role-based access controls enable data owners to set granular permissions on who can view and interact with different data types. This ensures sensitive information remains private while enabling broader access to non-sensitive data. A scalable search architecture is also critical to manage the large and growing data volumes in healthcare.

As medical records become more digitized, providers need search analytics platforms that can efficiently index, store and retrieve big data sets spanning images, documents and more. Moreover, having a powerful search analytics platform that combines search, observability and security capabilities can help healthcare organizations protect their data from attacks in today’s dynamically changing cyber threat environment. These capabilities can improve care coordination, accelerate research, and enhance regulatory compliance – helping drive better health outcomes.

Khalil Gonsalves is lead senior solutions architect for Elastic.

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