Each passing day, organizational cyberspaces are being populated with endpoints that have varying degrees of security risks. This population explosion must be accompanied by cybersecurity solutions that reduce the growing threat landscape by indiscriminately securing endpoints, regardless of their susceptibility to potential cyberattacks For network security strategies to scale up their granularity, they must be data-driven; they must leverage information about all the endpoints present within the network and the patterns of cyberattacks that threaten them.
The main challenge organizations face with the method described above is self-explanatory: Are their data science tools flexible enough to derive comprehensive results given the rising number of connected devices? This is where data fabric architecture enters, allowing organizations to achieve accessible data assets and efficient, event-driven data management.
Statistically, data fabric enjoys a sizable market presence. According to Fortune Business Insights, he global data fabric market was worth $1.43 billion in 2021 and is expected to increase from $1.71 billion in 2022 to $6.97 billion by 2029, displaying a CAGR of 22.3%.
What is data fabric architecture?
According to Gartner, data fabric is "a design concept that serves as an integrated layer (fabric) of data and connecting processes." One of the most restrictive aspects of legacy database systems is their use of data silos, which are centralized repositories that store data belonging to the respective departments of an organization. A silo is exclusive to only one department and cannot be accessed by the rest of the organization.
Although maintaining data silos is a common organizational practice, it prevents companies from harnessing their in-house data to its fullest potential. This, coupled with a lack of integrations, can cause widespread latency, harming the overall productivity and scalability of an organization. Additionally, this dormancy leads to poor data governance. With modern cybersecurity tools relying on data science and behavioral models for monitoring threats (for instance, SIEM and UEBA) , siloed data proves to be counterproductive in these cases.
With data fabric, data storage and processing are broken down using a metadata-driven architectural model, enabling the distribution of critical information. To prevent the stagnation of data resources and to bolster data reusability, data fabric deploys adaptive analytics to produce real-time inferences as opposed to relying on previously inferred outcomes and metadata for use.
Besides eliminating the need for silos, data fabric can help in achieving data integration across diverse applications, thereby improving collaborations within the enterprise. Also, data fabric provides easier, quicker access to data sets, improving the workforce's productivity and user experience.
Key elements of data fabric architecture
The basic techniques that make up data fabric architecture include:
- Data ingestion: This enables real-time streaming of active metadata and other data forms from different sources within the network. Data ingestion tools consolidate structured and unstructured data derived from distinct sources and import it for immediate use. The layer uses ontologies and the RDF standards stack for easier interoperability between diverse data assets and their analyses.
- Augmented knowledge graphs: One of the most prominent data processing tools leveraged by data fabric architecture is an augmented knowledge graph, which records, analyzes, and curates semantic information about data assets. These graphs make it easier for business leaders and AL- and ML-powered data models to gain better insights.
- Data processing: Data processing deals with the customization of raw data to make it presentable so that business intelligence tools can derive conclusive information from it.
- Data preparation and delivery: After collecting and processing data, it is important to present it in user-friendly formats. To address the ambiguity of how data can be used to perform further tasks, organizations must follow the extract, load, and transform (ELT) approach where the raw data is loaded into a single repository and subsequently transformed into formats dictated by the user's requirements.
- Data management: Data management is about making sure that the right amount of data is distributed to the right users based on their needs. To achieve this, data governance and administrative strategies are implemented by establishing and enacting enforcement policies, metadata management, microsegmentation, and access management. Data management ensures data security and prevents the formation of unwanted silos within networks.
- Data orchestration: This involves injecting automation into mundane tasks, such as pooling multiple data resources, organizing the collected data, and making it usable for external solutions. Data orchestration can be implemented using workflow management and data pipeline tools that leverage AI and ML to streamline multiple processes.
Data fabric comparisons
Since its conception, data fabric has been compared to several contemporary architectures that specialize in data optimization. Two of these comparisons are:
- Data fabric vs. data virtualization: Both of these strategies deal with eliminating modular data repositories. But unlike data fabric, which relies on integrating various data pipelines for easier delivery of data, data virtualization provides an abstraction layer for data assets that are consolidated from various sources. This gives users a single point to access data, regardless of whether it is stored in a mainframe or digital warehouse.
- Data fabric vs. data mesh: One of the most contentious debates in data architecture is the one between data fabric and data mesh. Despite sharing decentralized structural models and interchangeable functionalities, these strategies differ in how they approach governance. While data fabric presents a layer that unifies data distribution, access controls, and administration atop the network, data mesh aims to bring those services to every endpoint connected to the network. Unlike the mesh architecture, fabric architecture relies on a centralized strategy for data governance.
How cybersecurity benefits from data fabric
With data fabric delivering real-time knowledge to cybersecurity systems, the predictive modeling of nuanced adversaries and the incident response plan become more exhaustive in design. This is because data fabric ensures that active metadata changes reflect the results provided by monitoring tools, making the inferences less dated.
- Knowledge sharing: As organizations witness the debilitating effects of software supply chain attacks arising from overlooked network vulnerabilities, they must adopt a holistic approach to cybersecurity. That means making cybersecurity an omnipresent service for entities within a network. Data fabric, with the confluence of cybersecurity mesh, helps in creating a dynamic, accessible, and data-driven approach to event prediction, threat assessment, and escalation.
- Understanding context: With the wide range of endpoints and the elimination of a geographically defined security perimeter, it is important to be aware of the contextual changes entities undergo. Data fabric, with its emphasis on continuous analysis of existing and newly discovered metadata assets, can be seen as a repository that captures contextual information. Through it, risk-based security strategies can be constantly updated with situational changes, allowing informed decisions to be made accordingly.
- Consistent compliance: Because data governance is at the heart of data fabric architecture, organizational data policies will be readily invoked when a user tries to access a data asset, ensuring compliance throughout the network.
Cybersecurity thrives in a data-driven environment. As adversaries outgrow each other in sophistication, data fabric, with its emphasis on collecting active metadata, helps security tools understand the network's vulnerabilities and pain points that can be readily exploited by threat actors.
