Safeguarding Healthcare: Prescribing Strategies to Mitigate Digital Threats

Imani Tate
October 19, 2023
#Healthcare

In today's digital age, the healthcare sector is undergoing a rapid transformation. Innovative technologies are enhancing patient care and streamlining operations, but they also bring significant cybersecurity challenges. Healthcare organizations, both public and private, must address these evolving threats to protect critical systems and patient data.

Healthcare has long been a target for cyber threats, with malicious actors becoming more sophisticated over time. The growing use of healthcare technology, including telehealth services and wearable devices, has expanded the attack surface, making robust cybersecurity essential. The primary threats healthcare organizations face are malware and ransomware attacks, which can disrupt vital operations.

This blog, based on a recent conversation I had with Amber Pearson, Executive Director of Information Security Policy, VA at a Health IT Summit panel, will unpack how healthcare organizations – both in the public and private sectors – can ward off emerging cybersecurity threats.

Zero Trust: A fundamental shift

One essential cybersecurity strategy is the adoption of the Zero Trust model. Zero Trust is based on the principle of "never trust, always verify." This approach doesn't automatically trust any entity, whether it's a user, device, or network, based on location or past access. Every access attempt must be verified and validated before entry.

Zero Trust consists of several core components:

  • Identity Verification: Users and devices must verify their identity using multi-factor authentication or biometrics.
  • Least Privilege Access: Users and devices only get minimal access required, limiting potential damage in the event of a breach. MongoDB's role-based access control features can help enforce the principle of least privilege, ensuring that users and devices only have access to the specific data and functionality necessary for their roles.
  • Micro-Segmentation: Dividing the network into smaller segments to contain potential threats.
  • Continuous Monitoring: Real-time analysis of network traffic to detect abnormal behavior. MongoDB's scalability and flexibility make it suitable for storing and analyzing large volumes of logs and events, helping identify and respond to suspicious activities promptly.
  • Assumption of Breach: Operating with the assumption that a breach has occurred or could happen at any time.

MongoDB can assist in implementing Zero Trust by securely managing patient data and providing a flexible and scalable data storage solution for healthcare applications, including telehealth services, wearable devices, and interconnected systems. It ensures strict control and monitoring of access to patient data, even for users within the network, and third-party vendors and contractors must adhere to the same stringent security standards when accessing healthcare data. Zero Trust is not just a product or service but a comprehensive strategy that demands constant vigilance and a shift in the approach to cybersecurity.

Balancing security and interoperability

Interoperability in healthcare refers to different systems and devices exchanging data effectively. It's crucial for modernizing healthcare systems and delivering high-quality care. However, interoperability introduces complexity and risk in terms of cybersecurity.

Interoperability means more potential pathways for cyber threats. Attackers can exploit vulnerabilities in any connected device or system to gain unauthorized access to sensitive healthcare data. To address this, healthcare organizations should:

  • Security by Design: Collaborate with vendors to embed security in system design, including rigorous testing and encryption. MongoDB's built-in flexible security features ensure that data is protected at the database level, and its encryption capabilities can safeguard sensitive healthcare information.
  • Continuous Monitoring: Actively watch for anomalies, unauthorized access attempts, or unusual behavior across interconnected systems. MongoDB can play a pivotal role in storing and analyzing logs and event data, facilitating real-time monitoring and threat detection.
  • Risk Assessment: Assess risks associated with each device, system, or vendor within the interoperable ecosystem. MongoDB's role-based access control and auditing features can help organizations assess and mitigate risks tied to data access and manipulation.

Interoperability enhances patient care but introduces security challenges. A proactive and collaborative approach is essential to mitigate these risks, protecting patient data in an interconnected healthcare landscape. MongoDB is an ideal partner for healthcare interoperability due to its flexibility, scalability, and robust security features. Most importantly, its document-based architecture allows healthcare systems to seamlessly integrate diverse data sources, such as electronic health records, medical imaging, and patient data, fostering interoperability. The database's ability to handle complex, unstructured data makes it well-suited for the diverse data types prevalent in healthcare.

Don't discount insider threats

Insider threats involve individuals with legitimate access to healthcare systems who misuse data maliciously. To combat these threats, organizations must actively monitor information systems and collaborate with organizations like the Cybersecurity and Infrastructure Security Agency (CISA) for additional defense.

The proliferation of devices and remote work introduces new challenges. Enforcing strict cyber hygiene for all connected devices is vital. Promoting a security-first culture prioritizes open communication, collaboration, and proactive measures. Building organizational resilience, with open communication, regular risk assessments, and engagement at all levels, creates a robust cybersecurity posture.

In conclusion, technological advancement in healthcare must align with a robust cybersecurity strategy. Embracing Zero Trust, balancing security with interoperability, addressing insider threats, and building organizational resilience are essential steps. By promoting a security-first culture and prioritizing cyber hygiene, healthcare organizations can protect patient data and maintain the seamless operation of healthcare facilities in the face of evolving cyber threats.

Visit our Trust Center to learn more about MongoDB's dedication to data security

Read our whitepaper Cybersecurity: Building the Next Generation of Threat Intelligence with MongoDB to learn more about cybersecurity and MongoDB.

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Vector Search and LLM Essentials - What, When and Why

This post is also available in: Deutsch , Français , Español , Português Vector search and, more broadly, Artificial Intelligence (AI) are more popular now than ever. These terms are arising everywhere. Technology companies around the globe are scrambling to release vector search and AI features in an effort to be part of this growing trend. As a result, it's unusual to come across a homepage for a data-driven business and not see a reference to vector search or large language models (LLMs). In this blog, we'll cover what these terms mean while examining the events that led to their current trend. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. What is vector search Vectors are encoded representations of unstructured data like text, images, and audio in the form of arrays of numbers. Figure 1: Data is turned into vectors by embedding models These vectors are produced by machine learning (ML) techniques called "embedding models". These models are trained on large corpuses of data. Embedding models effectively capture meaningful relationships and similarities between data. This enables users to query data based on the meaning rather than the data itself. This fact unlocks more efficient data analysis tasks like recommendation systems, language understanding, and image recognition. Every search starts with a query and, in vector search, the query is represented by a vector. The job of vector search is finding, from the vectors stored on a database, those that are most similar to the vector of the query. This is the basic premise. It is all about similarity . This is why vector search is often called similarity search. Note: similarity also applies to ranking algorithms that work with non-vector data. To understand the concept of vector similarity, let’s picture a three-dimensional space. In this space, the location of a data point is fully determined by three coordinates. Figure 2: Location of a point P in a three-dimensional space In the same way, if a space has 1024 dimensions, it takes 1024 coordinates to locate a data point. Figure 3: Point P in a sphere that represents a multi-dimensional space Vectors also provide the location of data points in multidimensional spaces. In fact, we can treat the values in a vector as an array of coordinates. Once we have the location of the data points — the vectors — their similarity to each other is calculated by measuring the distance between them in the vector space. Points that are closer to each other in the vector space represent concepts that are more similar in meaning. For example, "tire" has a greater similarity to "car" and a lesser one to "airplane." However, "wing" would only have a similarity to "airplane." Therefore, the distance between the vectors for “tire” and “car” would be smaller than the distance between the vectors for “tire” and “airplane.” Yet, the distance between “wing” and “car” would be enormous. In other words, “tire” is relevant when we talk about a “car,” and to a lesser extent, an “airplane.” However, a “wing” is only relevant when we talk about an “airplane” and not relevant at all when we talk about a “car” (at least until flying cars are a viable mode of transport). The contextualization of data — regardless of the type — allows vector search to retrieve the most relevant results to a given query. A simple example of similarity Table 1: Example of similarity between different terms What are Large Language Models? LLMs are what bring AI to the vector search equation. LLMs and human minds both understand and associate concepts in order to perform certain natural language tasks, such as following a conversation or understanding an article. LLMs, like humans, need training in order to understand different concepts. For example, do you know what the term “corium” pertains to? Unless you're a nuclear engineer, probably not. The same happens with LLMs: if they are not trained in a specific domain, they are not able to understand concepts and therefore perform poorly. Let’s look at an example. LLMs understand pieces of text thanks to their embedding layer. This is where words or sentences are converted into vectors. In order to visualize vectors, we are going to use word clouds. Word clouds are closely related to vectors in the sense that they are representations of concepts and their context. First, let’s see the word cloud that an embedding model would generate for the term “corium” if it was trained with nuclear engineering data: Figure 4: Sample word cloud from a model trained with nuclear data As shown in the picture above, the word cloud indicates that corium is a radioactive material that has something to do with safety and containment structures. But, corium is a special term that can also be applied to another domain. Let’s see the word cloud resulting from an embedding model that has been trained in biology and anatomy: Figure 5: Sample word cloud from a model trained with biology data In this case, the word cloud indicates that corium is a concept related to skin and its layers. What happened here? Is one of the embedding models wrong? No. They have both been trained with different data sets. That is why finding the most appropriate model for a specific use case is crucial. One common practice in the industry is to adopt a pre-trained embedding model with strong background knowledge. One takes this model and then fine-tunes it with the domain-specific knowledge needed to perform particular tasks. The quantity and quality of the data used to train a model is relevant as well. We can agree that a person who has read just one article on aerodynamics will be less informed on the subject than a person who studied physics and aerospace engineering. Similarly, models that are trained with large sets of high-quality data will be better at understanding concepts and generate vectors that more accurately represent them. This creates the foundation for a successful vector search system. It is worth noting that although LLMs use text embedding models, vector search goes beyond that. It can deal with audio, images, and more. It is important to remember that the embedding models used for these cases share the same approach. They also need to be trained with data — images, sounds, etc. — in order to be able to understand the meaning behind it and create the appropriate similarity vectors. When was vector search created? MongoDB Atlas Vector Search currently provides three approaches to calculate vector similarity. These are also referred to as distance metrics, and consist of: euclidean distance cosine product dot product While each metric is different, for the purpose of this blog, we will focus on the fact that they all measure distance. Atlas Vector Search feeds these distance metrics into an approximate nearest neighbor (ANN) algorithm to find the stored vectors that are most similar to the vector of the query. In order to speed this process up, vectors are indexed using an algorithm called hierarchical navigable small world (HNSW). HNSW guides the search through a network of interconnected data points so that only the most relevant data points are considered. Using one of the three distance metrics in conjunction with the HNSW and KNN algorithms constitutes the foundation for performing vector search on MongoDB Atlas. But, how old are these technologies? We would think they are recent inventions by a bleeding-edge quantum computing lab, but the truth is far from that. Figure 6: Timeline of vector search technologies Euclidean distance was formulated in the year 300 BC, the cosine and the dot product in 1881, the KNN algorithm in 1951, and the HNSW algorithm in 2016. What this means is that the foundations for state-of-the-art vector search were fully available back in 2016. So, although vector search is today’s hot topic, it has been possible to implement it for several years. When were LLMs created? In 2017, there was a breakthrough: the transformer architecture . Presented in the famous paper Attention is all you need , this architecture introduced a neural network model for natural language processing (NLP) tasks. This enabled ML algorithms to process language data on an order of magnitude greater than was previously possible. As a result, the amount of information that could be used to train the models increased exponentially. This paved the way for the first LLM to appear in 2018: GPT-1 by OpenAI. LLMs use embedding models to understand pieces of text and perform certain natural language tasks like question answering or machine translation. LLMs are essentially NLP models that were re-branded due to the large amount of data they are trained with — hence the word large in LLM. The graph below shows the amount of data — parameters — used to train ML models over the years. A dramatic increase can be observed in 2017 after the transformer architecture was published. Figure 7: Parameter count of ML systems through time. Source:   towardsdatascience.com Why are vector search and LLMs so popular? As stated above, the technology for vector search was fully available back in 2016. However, it did not become particularly popular until the end of 2022. Why? Although the ML industry has been very active since 2018, LLMs were not widely available or easy to use until OpenAI’s release of ChatGPT in November 2022. The fact that OpenAI allowed everyone to interact with an LLM with a simple chat is the key to its success. ChatGPT revolutionized the industry by enabling the average person to interact with NLP algorithms in a way that would have otherwise been reserved for researchers and scientists. As can be seen in the figure below, OpenAI’s breakthrough led to the popularity of LLMs skyrocketing. Concurrently, ChatGPT became a mainstream tool used by the general public. The influence of OpenAI on the popularity of LLMs is also evidenced by the fact that both OpenAI and LLMs had their first popularity peak simultaneously. (See figure 8.) Figure 8: Popularity of the terms LLM and OpenAI over time. Source: Google Trends Here is why. LLMs are so popular because OpenAI made them famous with the release of ChatGPT. Searching and storing large amounts of vectors became a challenge. This is because LLMs work with embeddings. Thus the adoption of vector search increased in tandem. This is the largest contributing factor to the industry shift. This shift resulted in many data companies introducing support for vector search and other functionalities related to LLMs and the AI behind them. Conclusion Vector search is a modern disruptor. The increasing value of both vector embeddings and advanced mathematical search processes has catalyzed vector search adoption to transform the field of information retrieval. Vector generation and vector search might be independent processes, but when they work together their potential is limitless. To learn more visit our Atlas Vector Search product page. To get started using Vector Search, head over to our quick-start guide .

October 16, 2023
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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024