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).
Coming soon to our website is our sample data page where users will be able to request and access some of our sample data to get a better feel of what we can do! We'll have different types of sample data available such as sample tax data, follow the money data, sample cyber security synthetic log files and more!
On Thursday, November 7th the Institute for Robotic Process Information & Artificial Intelligence (IRPA AI) New York Chapter Launch Party will be taking place in Manhattan, New York on 25 West 39th Street on Floor 14. The launch party will include a pre-launch networking event for the new chapter beginning at 5:15pm which will serve as an opportunity to plan and discuss future programs for the chapter. The launch party will also inform guests on how they can be involved with the chapter. Drinks will be served at the launch party during the pre-launch networking happy hour!
For more information or to RSVP to the event, please follow the link here.
For questions or general inquiries about IRPA AI or the NY Chapter Launch Party, please contact Molly Alexander at Molly.Alexander@irpanetwork.com.
You can also learn more about IRPA AI by going to their website and or their LinkedIn!
In recent news, Pitney Bowes and Groupe M6 experienced ransomware attacks which limited customer access to company services and led to the encryption of information on private networks and systems belonging to the companies. Furthermore, email servers and phone lines also went down due to the attacks, and while no customer data was lost or stolen, shows how much of a threat these ransomware attacks can pose on the privacy of companies and their customers.
Ransomware attacks, while hard to detect and fight off, are able to be defeated with time and effort. However, if it takes too much time to defeat said attacks, valuable data could be breached or stolen and many will be put at risk. If the risk is too much, companies forego hopes of fighting off the attacks themselves and end up paying high extortion fees to minimize damage. However what happens when attackers strike again? Will the companies be prepared to fend it off the next time, or will be they be seen as an easy target because they gave in?
One thing is for sure; just as we continue to make strides in the cyber security industry, criminals continue to get more and more advanced with their own cyber attack tactics.
The TAG Cyber Security Annual has been released on their website! You can find it here!
The TAG Cyber Security Annual comes complete with unbiased, expert industry research so we recommend the read! Alternatively, you can download the Annual here!