In today’s data-driven society, cybersecurity has become one of the most important priorities for all businesses. A successful cyberattack can inflict financial and reputational damage that some businesses may never recover from.
Last year, the World Economic Forum reported that data breaches continued at historic levels in 2024, and this year, 66% of organizations see artificial intelligence (AI) as the biggest cybersecurity game-changer. As a result, more companies are undergoing cybersecurity risk assessments to ensure their data is safe by identifying vulnerabilities, assessing potential threats, and quantifying the possible repercussions of security breaches.
With AI considered such a threat, there is also concern over how safe the databases that train the models are. One such database is the vector database, which is increasingly being used for various functions across all industries due to the unique way it can search and process large data sets. As investment in vector databases increases, more people are asking whether they are cyber safe.
Why Vector Databases Are Becoming More Popular
Vector databases are becoming increasingly popular due to their ability to perform a distinct data retrieval method called a vector search. This search type differs from traditional databases, which rely on exact matches, as a vector search operates on similarity. This means that a vector search will find contextually or semantically similar results, even if they are not an exact match. A vector search can do this because the data is stored on vector embeddings, which are lists of numbers that allow the data to be broken down into many dimensions and naturally clustered together within vector databases.
Algorithms optimized for a vector search can promptly identify the most similar vectors in this vast space without the need to scan every vector, making it a very resource-effective way to search. This is why vector databases are adept at handling data points that span thousands of dimensions. Popular examples of vector databases being used to handle large datasets include the training of AI applications such as large language models (LLMS) and product databases for commercial services to power recommendation systems. As more businesses become AI-centric and amass larger quantities of data, vector databases are quickly becoming one of the most efficient data management systems on the market.
Are Vector Databases Cyber Safe?
With increased use across multiple sectors, especially in terms of training AI models, comes greater scrutiny on the cybersecurity protections of vector databases. One reason for concern over how safe vector databases are is due to the amount of sensitive information that can be held on vector embeddings. Using an embedding inversion attack, hackers can “pull rich data back out of embeddings” and recreate the original input.
For example, “Embeddings that represent faces can similarly be reversed using techniques that generate facial images until one is recognized as the same as the original.” As with other databases, vector databases are also under threat from unauthorized access, resource exhaustion, and vulnerabilities through third-party software. It has also been argued that the vast amount of data a vector database can hold makes it more vulnerable due to an increased attack surface. While no database can be truly safe, vector databases can be protected from cyber-attacks.
Best Practices to Protect Vector Databases
There are several practices that can be used to improve the security of vector databases. With the actual vectors being vulnerable to embedding inversion attacks, partially homomorphic encryption is becoming one of the most effective ways to protect them. This “encrypts vectors while retaining their comparability for distance-based searches, ensuring secure functionality.” Once the vectors are encrypted, only users with the proper keys can query the data or understand it. This can foil embedding inversion as well as membership inference attacks.
Cyber experts believe that a strong encryption key should be at least 256 bits long. Companies should make cyber protection a key consideration when choosing a vector database, and a good vector database provider will have built-in security controls for all of the data. For example, MongoDB’s database protections include authentication, authorization, auditing, data encryption, and network security. This will ensure the system can detect and respond to unauthorized access and alert developers to suspicious activity, such as unusual login attempts or queries.
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