Data Flow Diagram
Vanina Mazzillo
Entry points define the interfaces through which potential attackers can interact with the application or supply it with data. In order for a potential attacker to attack an application, entry points must exist. Entry points in an application can be layered. For example, each web page in a web application may contain multiple entry points.
Entry points show where data enters the system (i.e. input fields, methods) and exit points are where it leaves the system (i.e. dynamic output, methods), respectively. Entry and exit points define a trust boundary (see Trust Levels).
In the case of exit points from components handling confidential data (e.g. data access components), exit points lacking security controls to protect confidentiality and integrity can lead to disclosure of such confidential information to an unauthorized user.
All of the information collected allows us to accurately model the application through the use of Data Flow Diagrams (DFDs). The DFDs will allow us to gain a better understanding of the application by providing a visual representation of how the application processes data.
Data flows show how data flows logically through the application, end to end. They allow the identification of affected components through critical points (e.g. data entering or leaving the system, storage of data) and the flow of control through these components.
The focus of the DFDs is on how data moves through the application and what happens to the data as it moves. DFDs are hierarchical in structure, so they can be used to decompose the application into subsystems and lower-level subsystems. The high-level DFD will allow us to clarify the scope of the application being modeled. The lower level iterations will allow us to focus on the specific processes involved when processing specific data.
Developed decades ago as a way to keep up with changing technology, the DFD methodology helped capture the increasingly complex flow of data in a computer system. Data flow diagrams weaved together multiple popular concepts in information system design to become popular in the fields of academia and business analysis.
There are two distinct types of data flow diagrams: the logical DFD and the physical DFD. Logical DFDs take the perspective of the business or its activities, looking at what information is moving through the system. Meanwhile, physical DFDs focus on how information moves throughout the system or how the information flow is implemented.
With your understanding of the logical flow, you can then more easily think through how that information physically moves along the same process. The physical DFD will include more technical details to help you build or optimize your system.
For example, thinking through registering for classes at a university, instead of just documenting how information flows in and out of the registration process, you might consider the steps in the process and what information is specific to each of those steps. This would include details on information used by teachers to create their course offering, information used by facilities to create course schedules, and information used by students to register for those classes.
Higher level DFDs allow you to dive deeper into each step and flow of information. In the example of students registering for classes, you might consider multiple sub-processes, like them requesting access to a fully-enrolled course, joining a waitlist, or moving between sections of the same course.
When you use a diagramming tool like Gliffy, you can add layers to your diagrams so that each of the levels of DFDs can be represented together. These make it easy to reference the context diagram while also trying to understand deeper levels of information.
This example shows that the Customer is accessing the MyHealth Application from the Internet. The MyHealth application is also accessed by employees, but from an internal location (and not the internet). The MyHealth application uses a data store with customer data. Further, there are legacy data stores in the backend network environment.
In this example I use rectangle with dotted lines to show Trust Boundaries (instead of just lines in the previous example). The banking app has a frontend and a backend (to represent that this is technically the case). The banking backend connects with many legacy data stores and a legacy API.
This provides a very high over of the data flows in a backup scenario. Next steps in this Data Flow Diagram can consist of providing more detailed explanations of data flows. For example, when does the data copy from the customer application to the backup application, who has access to this, etc.
This article highlights how to use Data Flow Diagrams in threat modeling. As part of the how-to understanding, I described the symbols used in a Data Flow Diagram, and the steps needed to create a diagram (which can be used for threat modeling purposes).
Data Flow Diagrams can provide users and team members with a quick understanding of how an application works, how the data moves around, and how the application communicates with other key applications and processes.
This is a great question! In simple terms Shopify keeps store data for the lifetime of the store. Personal data (such as your name or address) and the store's data are purged 2 years after it becomes inactive (frozen, closed). Non-personal data (such as financial information) is retained.
If you wish to make a request to have your data removed from our servers, either as a merchant of the platform or a buyer from a store, you can find the link for that in our Legal documentation here: Shopify Legal.
Is there any DFD & ER Diagram of ERPNext, Cause our company wants to implement ERPNext after doing customization. So need to understand the work flow, data flow diagram and entity relationship diagram of the complete system, so that developers can take a lead
I am evaluating ERPNext and, like italjapan and InnovaERP, I need an ERD. No FKs? How do you ensure referential integrity in the database without FKs? Are you relying on code in each module/application to maintain table relationships?
In my experience, pushing as much logic as possible into the database through the use of stored procedures, triggers, FK constraints, and views (preferably materialized) will improve integrity, performance, and security.
Exploring the ERPNext database, I see 330+ tables and nothing else. ERPNext is a complex system. Why not use all the capabilities of MariaDB? I am a bit concerned about long term enhancement, maintenance, and support of an ERPNext implementation.
A fundamental step in protecting Indiana University institutional data is understanding how the data is stored and transmitted, and who has access from collection to disposal. Data-flow diagrams help illustrate this lifecycle. Diagrams are particularly useful because they can reduce a long text description to an image that's easy to understand. Well-maintained diagrams help system administrators prioritize security controls and speed up information risk assessments.
Data flow diagrams (DFDs) are common diagrams used in structured analyses and data modelling to document the data flows between entities, processes and data stores. For example, the following DFD documents the main data flows in a large language model that allows user queries such as ChatGPT.
In draw.io, instead of numbering the processes, link to other pages in your multi-page diagram where those processes are documented, or the process diagrams hosted elsewhere in Confluence, your cloud storage location or the Notion page where the process is documented.
You can reproduce somehow the activity diagrams in Capella. But the SysML language is not implemented by Capella. Hence, constructions such as interruptible activity regions are not implemented neither.
Balancing in Data Flow Diagrams (DFD) is essentially about maintaining consistency and coherence across different levels of diagrams. The purpose of balancing is to ensure that the information and data flows remain accurate, complete, and well-organized throughout the system representation.
When you expand a higher-level process into a lower-level diagram, you need to make sure that all the inputs and outputs of the higher-level process are correctly reflected in the lower-level diagram. This means that every input to the process at the higher level should be an input to the same process at the lower level, and similarly for outputs.
Balancing in DFD is a practice that ensures the integrity and coherence of the diagrams, making them effective tools for understanding and communicating the flow of information within a system. It allows for flexibility in making changes while maintaining a structured and interconnected representation of data flows.
Verify that all inputs and outputs present in the context diagram are accounted for in the Level 1 diagrams. Every data flow entering or leaving the system should be included in the detailed diagrams.
Recognize that adjustments may be needed during the design phase. If a new input or output is identified, add it to the relevant process at the lower level. Ensure that this modification is reflected in all higher-level diagrams.
Balancing is an iterative process. As you move to lower levels of abstraction, consistently check and balance the diagrams. Confirm that the data flows are accurately represented, and make adjustments as necessary.
Maintain traceability throughout the diagrams. Users should be able to trace a data flow from the context diagram down to the lowest level without confusion. Traceability enhances the overall clarity of the system representation.
Once the balancing process is complete, finalize the diagrams and publish them as part of the system documentation. These balanced DFDs serve as valuable tools for system analysis, design, and communication.
By following these steps, software engineers and system analysts can ensure that their Data Flow Diagrams are balanced, accurate, and provide a comprehensive representation of how data moves through a system.
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