60 percent of breaches are linked to a third party. Why are you giving them access to your data when you don't need too?
Third-party contractors are the biggest source of security incidents outside of a company’s employees:
Why are commercial companies and government agencies giving access to their private and confidential data to third parties when there exist viable technology alternatives to this practice and they don't need too?
With the rise of social media in our current day and age, it's only natural that test data is utilized to ensure platforms such as Facebook, Twitter, and Instagram are working as intended to their highest degree of efficiency. Test data is utilized both for desktop and mobile applications for the social media platforms and is generated for many different use cases. One particular use case for test data in social media applications is for encryption so that user data remains private and isn't accessible to those who shouldn't have it.
Furthermore, user data and test user data are used by platforms such as Facebook and LinkedIn to generated sponsored and tailored content for each specific individual. Each user's data is different to a degree and thus generated test data must be dynamic enough to be manipulated to represent the millions of user-types the social media world may have.
A thought exercise on the System perspective of dev and test, as enabled by ExactData Synthetic Data.
Let’s consider the development of an application that scours incoming data for fraudulent activity… How would that test and analysis look with production data, de-identified production data, hand crafted data, and ExD synthetic data?
Let’s also consider that the application will classify all transactions/events as either bad or good. The perfect application would classify every transaction correctly resulting in 100% Precision (everything classified as bad was actually bad), 100% capture rate (classified every actual bad as bad), 0% escape rate (no bads classified as good), and 0% False Positive rate (no goods classified as bad). The application needs to be developed, tested, and analyzed from a System perspective. For example, the application could classify every transaction as bad and achieve 100% capture rate, and 0% escape rate, but would also result in poor Precision and a huge False Positive rate – thus requiring significant labor support to adjudicate the classifications. On the other extreme, the application could classify everything good, be mostly right, and not catch any bads. Both of these boundary conditions are absurd but illustrate the point of the importance of System.
One method of System analysis is the Confusion Matrix, noted below.
With production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With de-identified production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With hand-crafted data, you might have the “truth” to enable completion of the confusion matrix, you would not have the complexity or volume to be truly testing to find the “needle” in the haystack of fraudulent behavior within mass of good behavior.
With ExD synthetic data, you know where every bad is (you have the ground truth), so you CAN complete all 4 quadrants of the confusion matrix, and can then only, conduct a system analysis, driving the application to the real goal of tuning and optimizing Precision (maximizing TP) and Capture rate (maximizing TP/TP+FN) , while at the same time minimizing Escapes (FN) and False Positive rate (FP/FP+TP). Within a particular setup of an application version, these are typically threshold trade-offs, but with next iteration development, there is the opportunity to improve on all scores.
Test data and test data management have become more prominent in today's cyber world. With trends such as data visualization, SCRUM and Agile methodologies, artificial intelligence, and big data connectivity, test data has become essential in how company's operate with their technology.
Test data management is the process of creating non-production data with the implication that it will be utilized for rigorous testing and development for future applications. Test data is safer to use than real data when performing said development tests and can significantly reduce the life-cycle time a test-product may need. Furthermore, test data can lower security risks an organization may have during the software development process, can help prevent rollbacks, and can help set forth precedents for any bug-fixes in one's code or application.
Not only are there many benefits for efficient test data management strategies within the cyber security industry, but the benefits may protrude into other departments such as R&D and marketing as well!
One of the major debates about the cyber world pertains to privacy and how much of it one really has. With companies such as Facebook, Google, and Apple using customer data more and more, we know that our once private lives may not be as secret as we think. In their book 'Who Knows; Safeguarding Your Privacy in a Networked World', authors Ann Cavoukian and Don Tapscott discuss how secure certain documents that are supposed to remain private such as medical records and employment history really are.
The truth is with advancements in technology and cyber security, privacy and one's data is more readily available to companies, hackers, and even other ordinary individuals. Some companies make the point that they only use consumer data for our benefit, showing products one may need before they know they need them or memorizing GPS routes if one travels that way constantly. While it's true that our daily lives are significantly improved because our data is being used in this manner, there one question we have to ask ourselves; is having less privacy a fair trade-off for potential everyday life benefts?
One thing is for certain; we should all take measures to make sure our data is as safe as it can be. Limiting who has access to your social media profiles and information, using secure passwords, and not clicking sketchy links are very easy tips and tricks to always keep in mind to make sure your data is really yours.
What Test Data is Being Used Today and by Who?
An organization’s development ecosystem, including technical partners, software development and
contractors have a growing need to access private and confidential data to do their jobs. Relevant data sources are a necessary component of the software development, technical integration, testing, implementation and ongoing operations and maintenance processes and production data sources are commonly accessed and modified for this purpose. Complex, integrated technology solutions can no longer be managed within an organization’s internal operations but requires a large and varied global ecosystem of partners, consultants, technology companies and contractors. There is a similar need for test data within the organizations cyber security operations. It is also common practice for this ecosystem to utilize historical data, captured network traffic and simple network traffic generation technology for testing purposes.
With every new year comes exciting new updates and trends to the technological world around us! We at ExactData are excited about many trends and future advancements to come, but here are five that we're excited about in particular!
1) Advancement of AI and Mobile Intelligence
It's no secret that AI and mobile intelligence are evolving everyday. We see growth in both of these departments to no end, where things like facial recognition, fingerprint, voice, and eyes scans are all becoming more of a reliable reality! This is seen through many of the innovations of Apple, Samsung, and Google have brought to the table, but also through other fields of data science as well!
2) Automation and Innovation
When one thinks of automation and innovation, jobs and mundane tasks are often the first things thought of. How is data being innovated or automated you may ask? Well being able to derive data in faster response rates, being able to generate, switch, and use data for test purposes on the fly for exact results seems innovative to us! This innovation can be traced to artificial intelligence as well through pattern recognition, GPS sensors, self-driving cars, and more!
3) Cloud Computing and Cyber Security
Cloud computing is becoming more distributed, meaning the origin of the cloud can distribute services to other locations while operating fully in effect from one area. Server updates, latency checks, and bandwidth fixes are becoming quicker every year which not only affects the cloud and its functions but can also be used to stop breaches, glitches, and hackers right in their tracks as soon as they get into the system.
4) Financial Patterns and Recognition
Recognizing financial data patterns through data has been historically tricky due to the immense analytical prowess and and observational skills that could be needed. AI and statistical learning developments however can be trained to pick up these patterns more quickly than ever before, and with less error too. Financial analytics and trend recognition will certainly see upgrades in the upcoming year, especially with more variables such as cryptocurrency coming into play.
5) Accessibility and Privacy
Accessibility and privacy for data files come hand in hand; by making something more accessible you also have the means to make it more restricted. Added levels of security for data can come in many different forms; test data, artificial data, cloud computing, advanced machine learning, more advanced security protocols and more. The rule of thumb is to keep everything private that you may need for later so that nobody else can take or modify it.
While there are so many trends we believe to be up and coming in the world of data, these were just some of the few we believe to be relevant to both the industry and general public as a whole.
Happy New Year from us at ExactData! With each new year the aspiration to evolve technology even further grows exponentially, and the once thought to be improbable becomes possible right before our very eyes through both sustainable and disruptive innovations.
2020 promises to bring massive changes to the tech world, which includes but isn't limited to:
The terms "database" and "database management system" are typically used interchangeably despite the fact the two mean completely separate things. Additionally, both are important terms that those in the technology industry should clearly know how to distinct between, but it seems many people either don't or can't. Very quickly, below are definitions for the two vocabulary terms.
A database is a logically modeled cluster of information [data] that is typically stored on a computer or other type of hardware that is easily accessible in various ways.
A database management system is a computer program or other piece of software that allows one to access, interact with, and manipulate a database.
Additionally, there are many types of database management systems that exist in the world today. Historically, relational database management systems (RDBMS) are the most popular approach for managing data due to their accessibility and performance result capabilities. Examples of RDBMS's include the Amazon RDS, Oracle, and MySQL which all utilize Structured Query Language (SQL) to manipulate the different databases they interact with. All RDBMS's are ACID compliant and typically implement an OLTP system.
To combat the limitations of relational database management systems, NoSQL databases became more popular over the years. The term "NoSQL" was coined by Carlo Strozzi in 1998 as the term for his first database which didn't utilize SQL for managing data, hence the label "NoSQL." Examples of popular NoSQL databases include key-value pair databases, document databases, graph databases, and columnar databases, all of which while are similar in concept are different in theory, as there are advantages and disadvantages to using each in different scenarios.
As we continue to move forward in the technology world, we constantly search for the most optimal solution for all of our data needs. These optimal solutions begin with which database management system or systems we choose to utilize to solve our data-related problems. Some database management systems are more equipped for certain scenarios than others, and figuring out which type works best for you is essential when working with big data.
Most scientists agree that no one really knows how the most advanced algorithms do what they do, nor how well they are doing it. That could be a problem. Advances in synthetic data generation technologies can help. These algorithms generate data with a known ground truth, sufficient volumes and with statistically relevant true and false positives (TP, FP) and true and false negatives (TN, FN) for the nature of the test. AI algorithms can now be measured for precision, c, as the fraction of the predicted matches that are true positive matches, or c = TP/(TP + FP).