Data Fabric: What It Is and How It Impacts Cybersecurity
Generation and use of data present day are both amazing but also a challenge to manage cybersecurity. What is the most efficient way to handle this mass of scattered and dispersed data? The answer may lie in the concept of a “data fabric’ which can be described as a way to bring together data. It can be described as an integrated layer of data and linking procedures which “utilizes continuous analytics across existing, inferred and discoverable metadata assets in order to aid in the design, deployment , and use of integrated and reused data across different environments.”
What does it mean in the context of everyday conversation?
Consider it as an entire bedsheet that spans across all data sources. Every source is tied to, or weaved to the bedsheet by a shape (metadata API services security information such as API services, security information, etc.). This means that they are connected to other computing and storage functions. Data silos can be eliminated when data is fed into one connector.
It seems simple. In a hybrid system it sounds attractive as well. But the details, naturally is where it becomes complicated to put into practice.
How Does It Work?
In the beginning, we have to recognize that data fabric structures don’t occur in a single day. The process of putting one into place is a long-distance journey that requires the knowledge of your data (or “data mess”). The next step is to create an approach to automate, integrate and manage all sources using commonly used connectors and parts, eliminating the requirement for custom-coded code.
Do you remember the days when mobile phones had connectors that were proprietary? Today, the majority of them have USB-C. The removal of custom code is a similar idea. Proprietary connectors, components , and codes have their place however, if their purpose is connecting data source, commonality is your best friend. It is essential to have an data framework that can provide the use of a single, constant data management system that allows the seamless processing and access. The framework is built around the following fundamentals:
- Data, insights, and semantics. Finding out what data is in the world, and with a high level of visibility and the best way to get it.
- Uniform governance, compliance and oversight. A common playbook as well as a set of rules that ensure all users are able to play in the same way.
- Intelligent integration. On-ramps are defined and controlled, as well as lanes, and off-ramps. Are you able to safely drive on an interstate if you had to jump on and off was not controlled? Data fabric could lead to an improved management of workload.
- Life cycle and orchestration. Utilize the latest tools like machine learning, in order to limit the amount of accidents that occur on the interstate. The unification of data source view allows the system to limit pile-ups.
Data Fabric as a Means of Protection
If an sceptical CISO is thinking “won’t this simply expose my data to one sources?” they would be right. Security chain breaches have revealed the difficulties that single sources could cause. Do you think the same concerns do not be applicable to this case?
It’s possible it is, but it’s all in the build the configuration, maintenance and build. If properly used data fabric can improve the efficiency of your business and provide data security. The most important thing is to make sure that security and privacy safeguards are included, including but not only data masking, but also encryption.
Like any centralized system there are negatives.
Where Data Fabric Can Backfire
Centralization is always accompanied by the same difficulties. If you do not manage the the data fabric it is possible to experience the possibility of a cascading failure. Although they might not be reliable, the design and security measures that obfuscate in coordination, lack of coordination, and inconsistency (intentional and not) can provide some security. Consider it a form of unintentional backup and segmentation measure.
Data fabric, for instance, could restrict, or even erase old record of the data transaction. Based on the type of business the use of data fabric can be a risky choice. If your company is dependent upon processing transaction data, failing to have historical backups of records could put you in a dangerous position should malicious malware or ransomware strikes the system, severely restricting the scope of catastrophe recovery.
Is Data Mesh Right for You?
As previously mentioned there is a significant benefit to using common connectors. But, they come at costs. The process of creating and managing complex pipelines for data that support regular connections makes the system more complicated. This comes a risk of being fragile. This also increases the chance of experiencing latency.
For contrast, let’s examine a different idea, but a different one called data mesh. While the data mesh is heavily dependent on artificial intelligence and automation, powered through rich metadata the data mesh is more dependent on structure and the culture of the business to connect data products and uses.
Let’s imagine you’re a CISO or CIO or perhaps a technology or risk officer that wants to set up a data mesh. You’d want to push for a change plan which defines the data needs prior to in order to ensure that the data product owners change to align data to those requirements. Data mesh is centrally controlled and requires control for operation Data mesh is connected and requires alignment to function.
Building Data Fabric Into Your Environment
What do you do when you’ve made the decision to use the approach of data fabric? Begin by taking small steps, beginning in the DevOps team. The process of deploying data fabric requires an extensive amount of planning, so having teams from IT and software working in tandem is vital. It’s also a good idea to involve the security team and your business partners. Remember, in the event that the entire organization will depend on this bedsheet to link their data, you’ll require input from all participants.
Moving into a Data Fabric application is an ideal opportunity to implement some security-by-design thinking. This will improve your technological and business resilience, and also think long-term about destruction of data. Tagging and cataloging your data is an important indicator of how effective your venture will be, therefore don’t be afraid of putting a lot of effort into your metadata requirements. At the end of the day your AI/ML efforts will depend on it.
Gartner recommends data mesh and the data mesh to be important technology trends to be on the lookout from 2022. Before you make a decision on which one might be right for your needs and can help improve your defense, keep in mind that your ability to manage risk and your business operational requirements will determine which option is the best fit for your needs.
