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AI Can Help with Secure Quality Assurance Testing

AI Can Help with Secure Quality Assurance Testing

Artificial intelligence technology has become instrumental to the research and development process. In May, the White House actually unveiled a proposal to increase investments in research and development for AI projects.

There are many ways that AI can help with the development and release of new products and services. Most of the discussion focuses on using AI to come up with the initial idea and automate the research process. However, companies can use AI for an equally important initiative – quality assurance. We talked about the benefits of data analytics for QA teams, but AI can be just as important.

Rajneesh Malviya, the Vice President and Global Head for Infosys Validation Solutions, has talked about the many benefits of using AI to improve the quality assurance process. Your company should consider these benefits.

AI is Crucial for Handling the QA Process When Developing New Products

The process of ensuring that your product or software is of the best quality for your clients is referred to as quality assurance testing or QA testing. QA refers to the processes that are carried out in order to prevent issues with a software product or service. These issues could appear as the result of an incorrect implementation of the features or access controls. Hence, teams ensure that users have the best quality product or software. AI has proven to be very useful in this process, as Malviya discussed.

This process can become very complex without the right protocols in place. Fortunately, AI technology can help immensely with QA professionals trying to make sure that they bring the best products to the market.

Performing Quality Assurance Testing with a Security Approach

There are many ways to use AI technology to make quality assurance testing more effective, such as combining manual and automated methods for performing QA testing or developing test cases that closely match the application’s requirements.

AI technology has helped automate many of the QA processes. Companies are using RPA & Robotics solutions to streamline these processes more easily.

AI Can Improve Manual Testing in Addition to Automated Testing

AI technology isn’t just useful for automating elements of the testing process. They can also help companies improve the manual testing process in various ways.

One of the biggest benefits of AI for manual testing during the QA process is that it helps companies better train their employees. Companies can use machine learning tools to identify some of the most common product defects that would otherwise be overlooked. Employees can use these insights to examine products more carefully.

When conducting quality assurance testing, both the manual and automated approaches should be applied. When it comes to manual testing a lot of teams use the shift left approach while developing and testing software, especially in the early stages of development.

Testing manually gives you the ability to cover a wide variety of test cases with proper logic. Since a user fundamentally interacts with the look, feel, and UX of the product during manual testing, this methodology is seen as being vital. Manual testing is typically utilized for exploratory testing, testing for usability, and adhoc testing. AI may not be able to replace these aspects of the manual testing process, but they can certainly help streamline it.

On the other hand, there are situations when a couple of tests need to be executed repeatedly. In such cases, an automated technique has to be followed. It will execute the tests successfully and will save a significant amount of time and resources.

AI Can Help with Risk Scoring During the Testing Process

The primary objective of the risk-based testing technique is to identify the problems that pose the greatest risk during the testing process. Testing of this kind gives quality assurance teams the ability to prioritize and properly focus their efforts on the most severe risks that could result in issues with the application’s performance.

When a risk-based approach is taken, the team’s primary focus is on problems that could occur when the product is being used, such as adding negative values of a product, which could lead to an error and could cause the software to crash if the error is not handled properly. Therefore, using a risk-based approach improves the overall user experience and doesn’t require that much maintenance.

AI technology can be very helpful in this regard. One of the biggest benefits of using predictive analytics tools is that they can anticipate the likelihood of various problems arising with a given product. They have clear risk scoring algorithms that can significantly improve the QA testing process.

Using a Multi-Directional Testing Approach

It is always recommended to have multiple categories or steps of quality assurance testing. The product’s development team should be required to create automatic unit tests so that they can evaluate the quality of their code at the beginning of the development process. This will allow them to easily fix any problems that may arise in the initial stages, saving a significant amount of time. When everything is set, the quality assurance team will run smoke tests to ensure that everything is ready for implementation in production.

When you begin employing the multi-directional testing strategy in software development, it immediately indicates that a great deal of testing in a variety of directions will be carried out in depth. This type of multi-directional testing will ensure that all of the bugs have been fixed. After enormous changes have been made and the release criteria have been met, the QA team will ensure that all the functionalities are working seamlessly.

AI is Vital to the QA Process

QA testing has to be conducted to validate that the product we are providing meets the users’ needs and also delivers a higher user experience to them. Fortunately, new advances in AI technology have made it easier than ever.

More companies are using AI technology for different reasons. One of the most important benefits is that it will make it easier for the developers to fix any errors that may have been found during the initial phase or during the testing process. It will also ensure that the final product is of a high quality and will not result in any problems for the end users. As a result, QA needs to be carried out in a comprehensive manner and using the appropriate procedures, all of which are covered in this article.

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Big Data Improves the Features of Debit and Credit Cards

Big Data Improves the Features of Debit and Credit Cards

Big data technology has become very important to the modern financial sector. A growing number of financial institutions are investing in data analytics, AI and similar technologies to improve their business operations.

One of the industries that has been heavily affected by big data is the credit card sector. In 2016, Nate Vickery of Datafloq talked about the changes that big data has created for credit card companies. Vickery points out that big data has helped companies fight fraud, give better offers to customers, identify trends and provide many new opportunities for both customers and merchants.

How do credit card companies offer all of these benefits? They have to collect data from credit card users, which is possible due to the electronic infrastructure that they have already setup.

In order to recognize the changes big data can create for credit card companies, it is necessary to recognize the different features that make these new developments possible. You will also need to understand the general nature of credit cards in the first place before you can understand the impact that big data is having on the industry.

How is Big Data Changing the Credit Card Industry?

Debit and credit cards may be helpful spending tools, but it’s easy to ignore the wealth of information contained inside each piece of plastic. Since they contain so much information, it is easy for merchants and credit card companies to mine this data to make highly nuanced decisions. It’s a good idea to get acquainted with the characteristics of your cards—front and back—so you can utilize them effectively.

Here are some details that you should know about credit cards, so you can recognize the changes big data has created for them.

What Exactly Is a Credit Card?

A credit card is a plastic or metal document issued to a person by a financial organization. We may get one from our bank and use it to make cashless payments. It also enables us to get money now and pay later. It’s called a “credit” card because the bank loans us money to use it to make purchases. It, like a debit card, has a spending restriction for purchasing goods or services or withdrawing cash from an ATM.

What Exactly Is a Debit Card?

A debit card is a form of payment mechanism that enables users to make payments by immediately deducting the payment amount from their account with a single touch. In terms of functionality, a debit card is quite similar to a credit card. It means you may use the debit card to make payments in the same way you would a credit card.

The major distinction is that when you use a debit card, you are using your own money, as opposed to borrowed monies in the case of credit cards. The adoption of debit cards reduces the need to carry cash.

What Is the Difference Between Credit, Debit, And Prepaid Cards?

The distinctions between these cards are obvious. A debit card uses the holder’s current account balance to make payments. Prepaid cards may be used to make payments for retail and online purchases, as well as ATM withdrawals, using pre-loaded cash. A credit card, on the other hand, pays using money borrowed from a financial institution.

There are several sorts of credit cards. Classic cards allow users to borrow money for payments in exchange for repaying the bank within one month (usually from the payment date). The credit limit for gold and platinum credit cards is greater. Payments on revolving credit cards are automatically deferred. Finally, rewards cards accrue perks and offers for cardholders.

Particularly popular credit cards among Americans, as we can see in the graph below. The majority of cards are carried by older users. In the third quarter of 2020, baby boomers (aged 56 to 74) carried an average of 4.61 cards.

Front of a Debit/Credit Card

Identification of the bank. This section contains information on your card’s issuer. Cards normally contain your lender’s name, but they may also include a logo for a special program. Some cards, for example, are imprinted with the names of incentive programs or retailers.Card Number. This is critical information on your debit or credit card. A card number is a 16-digit number that identifies your card issuer account. When making an online purchase, you must provide your credit card information. You should keep your credit card number secret since it contains sensitive financial information.Cardholder’s name. This is the individual who is permitted to use the card. That individual did not necessarily start the account; they may merely have access to it as an “approved user.” Only authorized card users may make transactions with a debit or credit card, and shops are urged to request identification before taking card payments.Smart Chips. If you look attentively at the front of your card, you will see a little metal processor on the right front side. Cards with smart chips are more secure than regular magnetic-stripe-only cards.

Smart chips make it far more difficult for criminals to utilize stolen card numbers. If your card contains a smart chip, try putting it into the machine rather than swiping it. This is because the smart chip adds a one-time-only code to each transaction, making stolen data less valuable.Expiration date. Each card has an expiry date, and when it does, you must renew the card or obtain a new one. The expiry date is critical since the seller may request it when you attempt to make a payment, particularly for online purchases. Your card’s expiry date assures that it is legitimate and operational.Logo of a payment network. It is critical to understand the sort of card you have. MasterCard, Visa, and Discover are common examples. When purchasing online, you’ll normally see a drop-down option where you may choose which network your card belongs to.

These logos are also useful if you want to use your card to pay for products or services. Merchants often display stickers or signs indicating which cards they accept. You may always inquire about extra cards.

The Back of a Debit Or Credit Card

Making payments entails more than just entering a credit card number. The back of a debit or credit card contains extra crucial information.

Magnetic stripe. This black bar, often known as the magstripe, contains all of your account information. It is constructed of millions of small magnetic particles. When you swipe your card through a card reader terminal, the reader reads the magstripe and utilizes it to conduct the transaction.Hologram. For enhanced protection, some cards have a hologram or a mirror-like region with a three-dimensional picture. It assists merchants in determining if the card is legitimate or not. Holograms are tough to create, and technology is always evolving. The hologram may display on the face of the card at times.Bank Contact Information. The rear of a debit or credit card also provides the bank’s contact information. If you need to contact the bank, look on the back of your debit card for contact information. Furthermore, if you misplace your card, the person who finds it may use this information to return the card to the bank.Signature box. This is another fraud-prevention strategy, although it is seldom effective. For the card to be legally legitimate, the cardholder must sign it here, with the purpose that this signature may be matched with a driver’s license or a signature presented at the register when a transaction is made.

You may verify that the individual using the card is the true owner of the card by comparing the signatures or the name on the license.

Security codes. Cards are printed with an extra code to assist in guaranteeing that anybody using the card number is using a genuine, original card. Merchants often demand more than just the card number and expiry date on the front of your card when accepting payments online or over the phone.
The security code on the back adds another barrier for hackers who may have acquired your card information through merchant systems or using a skimmer.CVV, CVV2, CVC, CSC, CID, and other similar terms may be used to describe security codes. Most websites simply request a “security code” and give a little box in which to enter it.

Network Icons. On the reverse of the card, there may be an extra network logo. The logo is usually located in the lower-right corner. These emblems assist you in determining which ATMs are free to use. You may also use other ATMs, however, you may be charged a fee!

Credit Card Companies Track a Lot of Information that Can Be Leveraged with Data Analytics

Credit card companies are relying on big data technology more than ever. They are able to offer a lot of benefits to customers, because they store so much data on them with the various features they provide. Although the personal data credit card companies collect has raised valid privacy concerns, they are also very helpful.

Your card is a useful tool for making payments, but you can do more than simply carry it with you when you go shopping. Big data will make it an even more useful tool, as merchants leverage data to offer better deals and services. Make the most of your card by shopping online, getting cash, paying bills, and sending money to friends and family.

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Best Used Servers for Databases and Cloud Computing

Best Used Servers for Databases and Cloud Computing

Cloud technology is becoming more important than ever. Precedence Research projects that global companies will spend over $1.6 trillion on cloud services in 2030.

Companies will need to get used to investing in the right infrastructure to make the most of their cloud capabilities. This is going to require them to invest in the best servers to connect with cloud services, especially if they want to host their own.

Investing in the Best Servers for Cloud Computing

For many businesses, the decision to buy used servers is a no-brainer. Refurbished servers may not offer the same bells and whistles as their new counterparts, but they usually still offer similar capabilities – at a fraction of the cost. Organizations that need servers for their databases or cloud computing can’t just go out and buy the first option that presents itself, though. It’s important to know what their needs are, so they can get a product that meets those needs.

Of course, one option is to buy refurbished servers online from a company like Alta Technologies. This lets you compare all your options before making a purchase, plus you can talk with an experienced representative who can guide you to the product that fits your requirements. They will understand the basis of your cloud strategy and make sure that you use the right servers to meet your goals.

It’s helpful to know the names of the best used servers to buy, but it’s even more helpful to know how to choose them yourself. To learn more about both, just keep reading.

What to look for in a server to meet your cloud computing needs

No matter what type of used server your organization needs, there are certain aspects you’ll want to look at. Choosing a reliable brand is good, but you won’t necessarily find the best server to meet your cloud computing needs from a big-name brand. What matters most is whether or not the server meets your required specifications. Since there are a lot of nuances with cloud computing, you will need to

Here are a few things to look for when choosing used servers to support your cloud computing strategy.

Server configuration

Your server’s configuration is going to be essential for your cloud solutions. The main parameters to look at here are the processors, the storage, and the RAM.

Processors (CPUs)

Some servers come with one CPU, while others have multi-core processors. There are also options like multithreaded processors, which can enable a single processor to perform better than two or more lesser processors. You should also look at the processor’s clock speed, which will tell you how many instructions it can execute each second (measured in GHz). However, if a CPU has features like reduced latency, increased data throughput, subcycling, multiple threading, or multiple cores, this could make it more efficient than other processors that technically have a faster clock speed.

Storage

Scalability is key for any server, so you should have some idea of how much storage you’ll need. The type of storage you choose will also have an impact; there are a ton of options, so this is probably something you should discuss more in-depth with an expert. You could get servers with NAS, JBOD, or SAN storage, which can accommodate various types of drives. If you want the best of the best, SANs is one of the top options.

RAM

In general, more is more with RAM. Random Access Memory will help determine how fast a server can process data in different formats, so you don’t want to skimp on this feature.

Type of database

If you’re looking for a database server, you’ll need something built for the job. There are several different options, and they all fulfill different jobs based on their unique schema.

MS SQL Server

As a relational database management system, this server uses the Transact-SQL query language and is good for large databases. It’s a high-performance, secure, and reliable option, whether you’re retrieving information from on-premise locations or from the cloud.

PostgreSQL

Another relational system, the PostgreSQL is in full compliance with top management standards; it even has ACID support. It may not be as commonly used as some other kinds of database management systems, but it’s a great choice for complex applications and websites. It supports multi-versioning, and can be modified by more than one user with the assistance of MVCC. Its best features include mechanisms of transactions and replication, lack of restrictions for database size, and an expandable system of programming languages. Overall, it’s highly scalable and very advanced.

MariaDB

One of the more popular database management systems, MariaDB was developed with simplicity in mind. It’s compatible with applications that utilize MySQL, because it originated from the MySQL RDBMS. However, MySQL is no longer at the forefront of innovation, so a lot of users are switching to MariaDB instead. With an enhanced query optimizer and excellent functionality, this database management system is a top performer.

MySQL

Here’s one of the most common server databases. Server flexibility is provided through multiple tables, including InnoDB and MyISAM. Users can store data of either fixed or variable lengths, and web frameworks are utilized for basic configuration. As you’d expect of such a popular database, MySQL offers superior security and enough processing power to handle large quantities of data.

What else should you look at when considering different types of used servers?

Whether you’re buying new or used servers, there’s no reason to compromise on the most important features. Here are some key criteria to consider during your hunt for the right server:

Application development – languages used, web development, design toolsWorking environment – addressable memory size, hardware/software platforms, hardware requirementsSecurity – protection system, backup, crash recoverySystem architecture features and functional parameters – network capabilities, mobility, distribution, scalabilityData model – hierarchical, object, networkPerformance – query optimization, TPC rating, parallel architecture capabilitiesAdditional features – control over the various kinds of memory, auto-tuning of the system, implementation of the query language, search tools, support for triggers and stored proceduresProduct warranty – whether or not the seller offers returns or exchanges, product guarantees, routine technical support, or emergency technical support

Now that we’ve covered all that, here are some of the best options in used servers

Dell PowerEdge R940Dell PowerEdge R740HPE ProLiant DL580 Gen10HPE ProLiant DL380 Gen10HPE Apollo 6000 Gen10 System

Lenovo ThinkSystem SR670Lenovo ThinkSystem SR6501U Lenovo ThinkSystem SR5702U Lenovo ThinkSystem SR550Quanta ServersSupermicro Servers

Choose the Best Servers for Your Cloud Strategy

You need the right servers to support your cloud strategy. Perhaps the hardest part of finding the right server for your organization is figuring out which features you need it to have. If you don’t have all the information already at your fingertips, try finding a reputable seller who can assist you in your search; they should know what they’re talking about. Once that’s done, you will have completed the hardest part of the job.

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Hybrid Vs. Multi-Cloud: 5 Key Comparisons in Kafka Architectures

Hybrid Vs. Multi-Cloud: 5 Key Comparisons in Kafka Architectures

Cloud technology is becoming more important to modern businesses than ever. Ninety-four percent of enterprises invest in cloud infrastructures, due to the benefits it offers.

An estimated 87% of companies using the cloud rely on hybrid cloud environments. However, some companies use other cloud solutions, which need to be discussed as well.

These days, most companies’ cloud ecosystem includes infrastructure, compliance, security, and other aspects. These infrastructures can be either in hybrid cloud or multi-cloud. In addition, a multi-cloud system has sourced cloud infrastructure from different vendors depending on organizational needs.

There are a lot of great benefits of a hybrid cloud strategy, but the benefits of multi-cloud infrastructures should also be discussed. A multi-cloud infrastructure means when you acquire the technology from different vendors, and these can either be private or public. A hybrid cloud system is a cloud deployment model combining different cloud types, using both an on-premise hardware solution and a public cloud.

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. But keep in mind one thing which is you have to either replicate the topics in your cloud cluster or you will have to develop a custom connector to read and copy back and forth from the cloud to the application.

5 Key Comparisons in Different Apache Kafka Architectures

1. Kafka And ETL Processing: You might be using Apache Kafka for high-performance data pipelines, stream various analytics data, or run company critical assets using Kafka, but did you know that you can also use Kafka clusters to move data between multiple systems.

It is because you usually see Kafka producers publish data or push it towards a Kafka topic so that the application can consume the data. But a Kafka consumer is usually custom-built applications that feed data into their target applications. Hence you can use your cloud provider’s tools which may offer you the ability to create jobs that will extract and transform the data apart from also offering you the advantage of loading the ETL data.

Amazon’s AWS Glue is one such tool that allows you to consume data from Apache Kafka and Amazon-managed streaming for Apache Kafka (MSK). It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

2. Architecture Design: In most system cases, the first step is usually building a responsive and manageable Apache Kafka Architecture so that users can quickly review this data. For example- If you are supposed to process and document which has many key data sets like an employee insurance policy form. Then you can use various cloud tools to extract the data for further processing.

You can also configure a cloud-based tool like AWS Glue to connect with your on-premise cloud hardware and establish a secure connection. A three-step ETL framework job should do the trick. If you are unsure about the steps, then here they are: Step 1:Create a connection of the tool with the on-premise Apache Kafka data storage source. Step 2: Create a Data Catalog table. Step 3: Create an ETL job and save that data to a data lake.

3. Connection: Using a predefined Kafka connection, you can use various cloud tools like AWS glue to create a secure Secure Sockets Layer (SSL) connection in the Data Catalog. Also, you should know that a self-signed SSL certificate is always required for these connections.

Additionally, you can take multiple steps to render more value from the information. For example- you may use various business intelligence tools like QuickSight to embed the data into an internal Kafka dashboard. Then another team member may use the event-driven architectures to notify the administrator and perform various downstream actions. Although it should be done whenever you deal with specific data types, the possibilities are endless here.

4. Security Group: When you need a cloud tool like AWS Glue to communicate back and forth between its components, you will need to specify a security group with a self-referencing inbound rule for all Transmission Control Protocol (TCP) ports. It will enable you to restrict the data source to the same security group; in essence, they could all have a pre-configured self-referencing inbound rule for all traffic. You would then need to set up the Apache Kafka topic, refer to this newly created connection, and use the schema detection function.

5. Data Processing: After completing the Apache Kafka connection and creating the job, you can format the source data, which you will need later. You can also use various transformation tools to process your data library. For this data processing, take the help of the ETL script you created earlier, following the three steps outlined above.

Conclusion

Apache Kafka is an open-source data processing software with multiple usages in different applications. Use the above guide to identify which type of storage works for you.

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Can ML Fix Cybersecurity Challenges in Healthcare?

Can ML Fix Cybersecurity Challenges in Healthcare?

The Department of Health and Human Services HIPAA Breach Reporting Tool shows that there were over 700 data breaches in healthcare organizations last year. Healthcare organizations need to utilize the latest technology to stop these attacks. Machine learning technology is especially important.

Machine Learning Helps Healthcare Organizations Fight Cyberattacks

Machine learning technology is a double-edged sword in many facets of our lives. One of the biggest examples is with cybersecurity.

A growing number of cybersecurity professionals can use machine learning technology to fortify their defenses against cyberattacks. On the other hand, many hackers are also using machine learning to conduct more insidious attacks with each passing day.

The harsh reality is that organizations that don’t use machine learning to improve their cybersecurity strategies will be sitting ducks against tech-savvy hackers. Their strategies will be less effective with each passing day as hackers find more heinous ways to utilize AI technology to conduct vicious attacks.

Healthcare organizations will be some of the most vulnerable. They will have to find approaches to cybersecurity that make the most of advances in machine learning.

The State of Cybersecurity in Healthcare

It’s no secret that, in recent years, cybersecurity has been an important topic – especially in the healthcare industry. Namely, healthcare has been one of the biggest targets of cyber-attacks. This is because healthcare organizations hold important data that could be very easily exploited. In most cases, the main target of cyber attackers in healthcare has been patients’ information, like credit and bank account numbers, social security numbers, and personal data related to their medical state.

In order to protect patients and their data, healthcare organizations must implement strong security measures. This way, they can ensure that only authorized personnel have access to patient data and that all data is appropriately encrypted.

There are many ways to protect a healthcare organization, yet not enough organizations take the necessary measures to do so. We will discuss all about cybersecurity in healthcare in our article below. Continue reading to learn more about cybersecurity in healthcare, the different types of cyber-attacks, and how to protect your healthcare organization successfully.

Types of Healthcare Cyber Attacks

There are many different types of cyber-attacks that healthcare organizations can fall victim to. In general, these attacks can be categorized into four main types:

Phishing Attacks

One of the most common types of cyber-attacks is phishing. This type of attack usually consists of a malicious email being sent to an employee of a healthcare organization. In the vast majority of cases, the email looks like it’s from a legitimate source, but it actually contains malware that, once downloaded, can give the attacker access to the organization’s network.

Ransomware Attacks

Ransomware attacks have become increasingly common in recent years. This type of attack usually starts with a phishing email, but instead of just containing typical malware, it also has a ransomware virus. This virus will then encrypt all of the data on the organization’s network and demand a ransom be paid in order to decrypt it.

Denial-of-Service Attacks

A denial-of-service attack (DoS attack, also known as DDoS) is a cyber-attack that aims to make a healthcare organization’s website or network unavailable. This is usually done by flooding the organization’s servers with requests so that they can no longer handle legitimate traffic.

Insider Threats

An insider threat is a type of attack that is carried out by someone who already has access to the healthcare organization’s network. This could be an employee, contractor, or vendor. In the majority of cases, insider threats are pretty difficult to detect and can do a lot of damage.

Best Practices and Strategies Preventing Cyber Attacks with Machine Learning

There are many different steps that healthcare organizations can take in order to prevent and protect themselves from cyber attacks. Machine learning technology can be very helpful. Here are some of the most important ways to use this technology to thwart cybercriminals.

Educate Employees about Cybersecurity

One of the best ways to prevent cyber attacks is to educate employees about cybersecurity. Employees should be trained on how to spot phishing emails, as well as what to do if they receive one. They should also know how to properly handle patient data and understand the importance of encrypting all sensitive information.

If you want to educate your employees, you have to make sure that you are properly informed yourself. You will want to use machine learning technology to track the latest developments in cybercrime. New AI-powered applications can monitor malware, track the activities of known cybercriminals and otherwise collect data on developments in cybersecurity. This knowledge can help you inform your employees about the cybersecurity protocols that they can follow.

Implement Strong Security Measures

We have mentioned in the past that it is essential to use AI to protect against cyberattacks. One of the reasons that AI can automate many of the protocols needed to thwart cybercriminals. Machine learning can also detect possible attacks that would otherwise go unnoticed.

Healthcare organizations should use AI technology to implement solid security measures, like strong firewalls and intrusion detection systems. They should also have a secure process for handling patient data and make sure that only authorized personnel have access to it. All data should be appropriately encrypted, both in transit and at rest.

Regularly Back up Data

Another critical step that healthcare organizations can take is to regularly back up their data. This way, if they do fall victim to a ransomware attack, they will still have access to their data. It’s also essential to make sure that backups are stored off-site so that they can’t be accessed by the attacker.

You can use AI technology to automate your data backups. Machine learning technology can help determine the best times to backup your systems, so you can minimize the risk of losing valuable data.

Monitor Networks 24/7

Network monitoring is vital for all organizations, but it’s especially important for healthcare organizations. This way, the IT staff in the company can quickly spot any unusual activity and take steps to stop an attack before it does any damage. Again, machine learning is vital to network monitoring in 2022.

Machine Learning is Vital to Protect Healthcare Organizations Against Cyberattacks

We can conclude that cybersecurity is a crucial topic for all organizations, and healthcare ones are often the biggest target. There are many different types of cyber-attacks that they can fall victim to, and the consequences can be severe. A big cyberattack can cost millions of dollars in damages, as well as an irreparable stain on a company’s reputation.

That’s why it’s crucial for healthcare organizations to take steps to prevent and protect themselves from these attacks. By taking strong security measures, like educating employees, monitoring network activity, and backing up data, most healthcare organizations will be safe from all attacks.

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Key Reasons Businesses Are Embracing AI

Key Reasons Businesses Are Embracing AI

Businesses are evolving and searching for newer ways to accomplish their goals, hence the need for artificial intelligence (AI). AI involves building smart machines to carry out tasks that typically need human intelligence, and AI simulates human intelligence using computer systems. The two major AI types used in businesses today are reactive machines and limited memory.

Reactive AI machines are programmed with predictable outputs based on the input they receive. So, they use their intelligence to perceive the world and respond to identical situations similarly. Reactive AI does not store memory; therefore, it cannot rely on past experiences to inform real-time decision-making.

On the other hand, limited memory AI consists of machine learning models that can extract knowledge from previously learned facts, information, events, or stored data. They are more complex than reactive AI because they can analyze and use stored data to make better predictions, presenting users with greater possibilities. AI has tremendously changed the way businesses operate for the better by mixing human creativity with machine accuracy. Keep reading to discover some of the primary reasons companies adopt AI into their operations.

1.      Improved Customer Experience

Business success no longer relies solely on product or pricing; a satisfying customer experience is now necessary. Various knowledge management software use AI to create a knowledge-sharing culture that delivers a better customer experience. Through the combination of AI and machine learning that gathers and analyzes behavioral, historical, and social data, brands can now better understand their customers.

Unlike traditional data management, AI continuously learns and improves using the data it analyzes to anticipate customer behavior. As a result, brands can improve the customer journey and provide highly relevant content, thereby increasing sales opportunities. For instance, AI chatbots implemented at strategic customer touchpoints help businesses uncover common customer issues and gain insight into what’s causing problems for users. Therefore, brands can create personalized solutions to such issues.

Furthermore, AI-powered chatbots can use programmed questions to generate and validate leads before moving them to sales agents. Chatbots can also initiate conversations with prospective customers based on their browsing history to help the sale or upsell post-purchase. Ultimately, AI creates improved, memorable customer experiences for businesses.        

1.      Optimizing Business Management

For businesses to grow, they must look for ways to optimize and improve their management processes. AI helps automate various tasks that consume employees’ work hours and perform such tasks faster and sometimes more accurately than humans. For example, many HR personnel in organizations spend their time on coordination, administrative, and control tasks such as adjusting schedules due to staff members’ vacations or illnesses. This can be performed automatically using AI-powered business management applications.

Also, AI can simplify job interview processes and lessen the workload of HR managers, freeing them up for duties that actually require their intervention. AI can easily shortlist resumes from large databases, automate the initial rounds of employee interviews, and monitor candidates’ facial expressions when answering questions to calculate their confidence levels. Furthermore, AI makes it easier to respond to applications and queries. When brands reach out to applicants quickly, they reduce the chances of losing qualified candidates to their competitors.          

2.      Cybersecurity Solutions

Cyberattacks have grown in complexity and volume, and they can cause substantial financial losses and reputational damage to businesses. AI helps security operations analysts stay ahead of threats by curating threat intelligence from several news stories, research papers, and blogs. AI technologies provide quick insights into possible cyber threats and reduce response times.

Cybersecurity professionals can now reinforce best practices for cybersecurity and narrow the attack surface instead of being continuously on the lookout for malicious activity. For instance, AI can efficiently and accurately analyze large amounts of business data and identify likely threats hiding in a company’s system. Businesses can segment risks from routine network activity and pinpoint threats more efficiently. Therefore, they can enhance their security measures by increasing the monitoring around commonly attacked areas of their network.    

3.      Improved Marketing

Marketing is an essential part of every business. However, people don’t like having random ads pop up when they’re online. Most times, such ads don’t help companies to achieve their marketing goals since they are irrelevant to the viewers.

Nowadays, brands use AI to improve the marketing experience and reduce waste time and resources on unproductive ad campaigns. AI machine learning prevents customers from receiving random ad suggestions and allows ads to appear based on customer profiling, audience segmentation, and people’s online behaviors.

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5 Ways Companies Use Machine Learning to Improve Workplace Productivity

5 Ways Companies Use Machine Learning to Improve Workplace Productivity

Technology has become so advanced that, today, there’s an app for almost anything, from children’s education, to home improvement, to health monitoring, to workplace productivity. Gathering critical data to determine the best action to apply to specific situations has become integral in people’s daily lives.

Because of technology, critical decisions are now mostly based on scientific data. This makes every action more precise and error-free, especially in the business world. By using artificial intelligence and machine learning, industries can better cope with their consumers’ demands.

Companies can function better and deliver the best outputs if their employees are happy and productive. The country with the highest percentage of labor productivity is Ireland, with 99.5% productivity despite having only a four-day work week. This only means that longer hours don’t equate to productivity.

Today, companies use machine learning, in particular, to ensure that they achieve the appropriate productivity output for the amount of money they spend on their business operations. Because aside from the need to organize unusual indoor team-building activities to boost employee morale, companies today need to elevate the operational activities they conduct at work to ensure their teams are consistently productive.

Here are some of the ways companies achieve such through machine learning:

1.     Anticipating Changes In Labor Needs

The retail landscape often finds it challenging to keep personnel costs low, while ensuring that customers are happy and satisfied. There are periods when a retail store doesn’t have customers for an extended period and having several staff members handling the store can be utterly unproductive and costly.

A labor scheduling tool that gathers information based on results, which is then entered in the POS, will increase the productivity of a retail store. This tool ensures that crucial metrics, such as personnel hours, items per transaction, and hourly sales, are considered to determine the appropriate number of staff to deploy at specific times of the day.

Hiring decisions will also be guided and labor costs significantly reduced because a labor scheduling tool will help an organization determine if they need to hire full-time or part-time employees. Hiring part-time employees is more cost-effective. And, if the tool predicts that there’s no need for full-time employees, it’d be better and increase a company’s bottom line.

2.     Hiring The Right People

Hundreds of job applications come through the doors of the human resources department daily and filtering these applications so the right people can get hired to perform the essential workplace tasks can sometimes be daunting.  Ordinarily, a person assigned to go through these applications may have biases or could unintentionally allow their emotions to affect their judgment.

When a tool is tasked to filter job applications, it can do so without biases and ignore the appeals to emotion altogether. A tool like a recruiting application that filters the unique qualifications of job applicants can help human resources hire the right people for the company.

The recruitment application will be tasked to watch out for crucial factors inherent in a bad hire and would try their best to avoid this from getting into the pool that’ll move up to the next level of the application process. This way, only applicants that match the values of the company will eventually be onboard to perform relevant tasks for the company.

3.     Using The Power Of Chatbots

Your business can benefit a lot from chatbots. Historical responses on platforms that are automatically saved on the system will be added to the predictive answers of customer inquiries. Now, should any question be unanswerable by a chatbot, it’ll be immediately forwarded to a team member for proper acknowledgement. 

This increases productivity and response time, which is also a valuable component of customer satisfaction.

4.     Making Accurate Sales Forecasts

Information gathered from various channels can efficiently help managers make closer to accurate forecasts. In doing this, proper deployment of manpower can be achieved. Information from all platforms, like social media marketplace, ecommerce stores, and brick and mortar store can be gathered to create sales forecasts that’ll be useful in the production aspect of the business. 

If you know how many sales are coming in, factoring in other crucial details, like events, can give you a more straightforward way of predicting what’s to happen in the near future. This will increase productivity and reduce production waste at the same time.

5.     Utilizing Enterprise Search

Your team members and customers can benefit from enterprise search powered by machine learning.  If you have a massive business, content could be challenging to look for, especially if such pieces are scattered through various channels and platforms. 

With a few clicks on your device, customers and team members can gain access to a mine of information that’ll be useful for their respective tasks. Having access to information will allow people in your organization to accomplish tasks efficiently and more productively.

Conclusion

Labor hours spent doing nothing are wasted money. Ensuring that all people deployed in your organization for the day are functioning at their best and are productive will guarantee higher profits for your company.

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Best practices of migrating Hive ACID Tables to BigQuery

Best practices of migrating Hive ACID Tables to BigQuery

Are you looking to migrate a large amount of Hive ACID tables to BigQuery? 

ACID enabled Hive tables support transactions that accept updates and delete DML operations. In this blog, we will explore migrating Hive ACID tables to BigQuery. The approach explored in this blog works for both compacted (major / minor) and non-compacted Hive tables. Let’s first understand the term ACID and how it works in Hive.

ACID stands for four traits of database transactions:  

Atomicity (an operation either succeeds completely or fails, it does not leave partial data)

Consistency (once an application performs an operation the results of that operation are visible to it in every subsequent operation)

Isolation (an incomplete operation by one user does not cause unexpected side effects for other users)

Durability (once an operation is complete it will be preserved even in the face of machine or system failure)

Starting in Version 0.14, Hive supports all ACID properties which enables it to use transactions, create transactional tables, and run queries like Insert, Update, and Delete on tables.

Underlying the Hive ACID table, files are in the ORC ACID version. To support ACID features, Hive stores table data in a set of base files and all the insert, update, and delete operation data in delta files. At the read time, the reader merges both the base file and delta files to present the latest data. As operations modify the table, a lot of delta files are created and need to be compacted to maintain adequate performance.  There are two types of compactions, minor and major.

Minor compaction takes a set of existing delta files and rewrites them to a single delta file per bucket.

Major compaction takes one or more delta files and the base file for the bucket and rewrites them into a new base file per bucket. Major compaction is more expensive but is more effective.

Organizations configure automatic compactions, but they also need to perform manual compactions when automated fails. If compaction is not performed for a long time after a failure, it results in a lot of small delta files. Running compaction on these large numbers of small delta files can become a very resource intensive operation and can run into failures as well. 

Some of the issues with Hive ACID tables are:

NameNode capacity problems due to small delta files.

Table Locks during compaction.

Running major compactions on Hive ACID tables is a resource intensive operation.

Longer time taken for data replication to DR due to small files.

Benefits of migrating Hive ACIDs to BigQuery

Some of the benefits of migrating Hive ACID tables to BigQuery are:

Once data is loaded into managed BigQuery tables, BigQuery manages and optimizes the data stored in the internal storage and handles compaction. So there will not be any small file issue like we have in Hive ACID tables.

The locking issue is resolved here as BigQuery storage read API is gRPC based and is highly parallelized. 

As ORC files are completely self-describing, there is no dependency on Hive Metastore DDL. BigQuery has an in-built schema inference feature that can infer the schema from an ORC file and supports schema evolution without any need for tools like Apache Spark to perform schema inference. 

Hive ACID table structure and sample data

Here is the sample Hive ACID  table  “employee_trans” Schema

code_block[StructValue([(u’code’, u”hive> show create table employee_trans;rnOKrnCREATE TABLE `employee_trans`(rn `id` int, rn `name` string, rn `age` int, rn `gender` string)rnROW FORMAT SERDE rn ‘org.apache.hadoop.hive.ql.io.orc.OrcSerde’ rnSTORED AS INPUTFORMAT rn ‘org.apache.hadoop.hive.ql.io.orc.OrcInputFormat’ rnOUTPUTFORMAT rn ‘org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat’rnLOCATIONrn ‘hdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans’rnTBLPROPERTIES (rn ‘bucketing_version’=’2’, rn ‘transactional’=’true’, rn ‘transactional_properties’=’default’, rn ‘transient_lastDdlTime’=’1657906607′)”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eda26dc94d0>)])]

This sample ACID table “employee_trans” has 3 records.

code_block[StructValue([(u’code’, u’hive> select * from employee_trans;rnOKrn1 James 30 Mrn3 Jeff 45 Mrn2 Ann 40 FrnTime taken: 0.1 seconds, Fetched: 3 row(s)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eda26dc9310>)])]

For every insert, update and delete operation, small delta files are created. This is the underlying directory structure of the Hive ACID enabled table.

code_block[StructValue([(u’code’, u’hdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delete_delta_0000005_0000005_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delete_delta_0000006_0000006_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delta_0000001_0000001_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delta_0000002_0000002_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delta_0000003_0000003_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delta_0000004_0000004_0000rnhdfs://hive-cluster-m/user/hive/warehouse/aciddb.db/employee_trans/delta_0000005_0000005_0000′), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eda26a28c50>)])]

These ORC files in an ACID table are extended with several columns:

code_block[StructValue([(u’code’, u’struct<rn operation: int,rn originalTransaction: bigInt,rn bucket: int,rn rowId: bigInt,rn currentTransaction: bigInt,rn row: struct<…>rn>’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eda26a28810>)])]

Steps to Migrate Hive ACID tables to BigQuery

Migrate underlying Hive table HDFS data

Copy the files present under employee_trans hdfs directory and stage in GCS. You can use either HDFS2GCS solution or Distcp. HDFS2GCS solution uses open source technologies to transfer data and provide several benefits like status reporting, error handling, fault tolerance, incremental/delta loading,  rate throttling, start/stop, checksum validation, byte2byte comparison etc. Here is the high level architecture of the HDFS2GCS solution. Please refer to the public github URL HDFS2GCS to learn more about this tool.

The source location may contain extra files that we don’t necessarily want to copy. Here, we can use filters based on regular expressions to do things such as copying files with the .ORC extension only.

Load ACID Tables as-is to BigQuery

Once the underlying Hive acid table files are copied to GCS, use the BQ load tool to load data in BigQuery base table. This base table will have all the change events.

Data verification

Run  “select *” on the base table to verify if all the changes are captured. 

Note: Use of “select * …” is used for demonstration purposes and is not a stated best practice.

Loading to target BigQuery table

The following query will select only the latest version of all records from the base table, by discarding the intermediate delete and update operations.

You can either load the results of this query into a target table using scheduled query on-demand with the overwrite option or alternatively, you can also create this query as a view on the base table to get the latest records from the base table directly.

code_block[StructValue([(u’code’, u’WITHrn latest_records_desc AS (rn SELECTrn Row.*,rn operation,rn ROW_NUMBER() OVER (PARTITION BY originalTransaction ORDER BY originalTransaction ASC, bucket ASC, rowId ASC, currentTransaction DESC) AS rownumrn FROMrn `hiveacid-sandbox.hivetobq.basetable` )rnSELECT id,name,age,genderrnFROMrn latest_records_descrnWHERErn rownum=1rn AND operation != 2′), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3eda2680bc90>)])]

Once the data is loaded in target BigQuey table, you can perform validation using below steps:

a. Use the Data Validation Tool to validate the Hive ACID table and the target BigQuery table. DVT provides an automated and repeatable solution to perform schema and validation tasks. This tool supports the following validations:

Column validation (count, sum, avg, min, max, group by)

Row validation (BQ, Hive, and Teradata only)

Schema validation

Custom Query validation

Ad hoc SQL exploration

b. If you have analytical HiveQLs running on this ACID table, translate them using the BigQuery SQL translation service and point to the target BigQuery table. 

Hive DDL Migration (Optional)

Since ORC is self-contained, leverage BigQuery’s schema inference feature when loading. 

There is no dependency to extract Hive DDLs from Metastore. 

But if you have an organization-wide policy to pre-create datasets and tables before migration, this step will be useful and will be a good starting point. 

a. Extract Hive ACID DDL dumps and translate them using BigQuery translation service to create equivalent BigQuery DDLs. 

There is a Batch SQL translation service to bulk translate exported HQL (Hive Query Language) scripts from a source metadata bucket in Google Cloud Storage to BigQuery equivalent SQLs  into a target GCS bucket. 

You can also use BigQuery interactive SQL translator which is a live, real time SQL translation tool across multiple SQL dialects to translate a query like HQL dialect into a BigQuery Standard SQL query. This tool can reduce time and effort to migrate SQL workloads to BigQuery. 

b. Create managed BigQuery tables using the translated DDLs. 

Here is the screenshot of the translation service in the BigQuery console.  Submit “Translate” to translate the HiveQLs and “Run” to execute the query. For creating tables from batch translated bulk sql queries, you can use Airflow BigQuery operator (BigQueryInsertJobOperator) to run multiple queries

After the DDLs are converted, copy the ORC files to GCS and perform ELT in BigQuery. 

The pain points of Hive ACID tables are resolved when migrating to BigQuery. When you migrate the ACID tables to BigQuery, you can leverage BigQuery ML and GeoViz capabilities for real-time analytics. If you are interested in exploring more, please check out the additional resources section. 

Additional Resources

Hive ACID

ACID ORC Format

HDFS2GCS Solution

DistCp

Data Validation Tool

BigQuery Translation Service

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Why Is Data Loss Prevention is Crucial for Business?

Why Is Data Loss Prevention is Crucial for Business?

Data loss is a serious problem for many businesses. An estimated 94% do not survive a catastrophic data loss.

Data loss prevention (DLP) strives to protect your business data from inside or outside compromise. This includes data leakage, data loss, misuse of data, or data compromised by unauthorized parties.

DLP software aims to identify and classify crucial business data and pinpoint potential organization or policy packs violations. All of your regulated and classified data should be compliant with HIPPA, GDPR, PCI-DSS, or other customized policies depending on your company’s services and needs.

Once DLP identifies a violation, it initiates remediation protocols through alerts and encryption, thus preventing any end-user from accidentally sharing valuable data or falling victim to a successful malicious attack.

The primary approach of DLP software is to focus on monitoring and control of endpoint activities.

It can filter corporate network data streams and examine data cloud behavior to secure your operational data in real-time. Alongside compliance and auditing assistance, DLP ensures essential data is available without compromise at all times.

Common causes of data compromise can be network malfunctions, negligent employee errors, or cyberattacks. Said attacks can come as ransomware, viruses, Trojans, DDoS, SQL injections, or others.

Data loss prevention emphasizes the following to neutralize all potential threats to your data:

Enhanced data visibility within your companyIncreased cybersecurity for local and cloud storageIntellectual Property (IP) protectionPersonally Identifiable Information (PII) protection in line with current regulationsStrengthened mobile and bring-your-own-device (BYOD) security

How Does DLP Help Your Business?

Data loss protection comprises three significant business objectives – personal information protection, intellectual property protection, and comprehensive data usage reports.

Having any of those boosts your data security. Having all three can fortify your defenses against as many threats as possible.

Personal Information Protection

If you operate a modern business, chances are your company gathers Personally Identifiable Information, user financial details, or Protected Health Information. All of those are subject to different compliance regulations, and as such, you need to ensure their protection against malicious interfering.

We’ve mentioned HIPPA and GDPR, the main regulatory compliance policies companies use nowadays. However, global policies entail specific regulations for every company to identify sensitive data and monitor all surrounding activities. Here, DLP provides detailed reports to fulfill compliance audits.

By having all sensitive data under monitoring, you can assure that your company handles vital user details with the utmost care. If DLP finds weak links in the data handling process, you can fix them as soon as possible.

Intellectual Property Protection

Intellectual Property protection identifies intellectual property to classify and protect it better, be it trade or state secrets. If IP is leaked or compromised, it can hurt your brand’s image and financial well-being incrementally.

Enterprises can store Intellectual Property data in unstructured or structured forms. Both options rely on strict security policies to deny unauthorized data access, including data encryption, regular data backups, and real-time cybersecurity protection.

Data Usage Reports

A comprehensive DLP plan can monitor data in transit within networks, cloud storage, and active endpoints. In addition to vulnerability assessment, DLP improves system administrators’ visibility – they can track how every user accesses data and bring the risk of a data leak to a minimum.

When the people responsible for managing data transit know its course and actions, it’s easier to protect PII and IP. Furthermore, better monitoring translates to heightened efficiency in all company processes.

Different Approaches to DLP for Businesses

Any full DLP service aims to monitor and detect vulnerabilities that could lead to data leakage or compromise. With a reliable DLP solution, you can rid your company’s network of weakly guarded entry points for attackers by keeping all networks, devices, and storage options secured and optimized.

There are several types of DLP options to choose from depending on your company’s nature of operations.

Let’s go through them together.

Network Protection for DLP

Network DLP solutions monitor, track, and report all data movements on your company network. They can do so by integrating data checkpoints to all software and hardware on your premises.

Implementing DLP on every device means every endpoint is secure – you can monitor who is accessing data, how they are using it, and where data goes at all times. In addition, most network protection solutions offer comprehensive reports to ease data management.

Here are the major benefits of using a network protection option:

Prevents data leakage from the network by securing ports and security protocolsGrants control and visibility over emails, SSL-enable sessions, and FTPMonitors, inspects, and controls data traffic on web apps, emails, TCP/IP, FTP/S, and HTTP/SInspects email contents for sensitive content (messages, attachments, links)Encrypts email contents (communication, attachments) and helps regulatory compliancePrevents data loss through DPIMonitors and blocks potentially malicious URLs and web appsEases data traffic reportsEducates users, employees, and admins on sensible data protection policies (with added alerts to signal for vulnerabilities)

Endpoint Protection for DLP

We’ve mentioned endpoint protection as a part of network protection DLP. Nonetheless, some DLP vendors provide services solely aimed at securing endpoints.

Businesses use various devices to form a complex working system – PCs, laptops, tablets, smartphones, etc. In such a case, any mobile device works as an external assistant to transport data more quickly and efficiently.

While efficiency is vital, we should be aware of the added risks of using different devices to transfer data. Any pen drive increases the chance of accidental leakage or data corruption by a third party. Endpoint protection DLP aims to protect all removable drives in use, so no data is breached, deleted, or held for ransom.

You can install endpoint protection software on all company devices to ensure external drives, clipboards, and sharing apps are inaccessible by outside parties. You can also monitor data traffic to improve security and increase transit efficiency.

Most endpoint solutions comprise:

Data management and data controlsSecure Remote Desktop Protocols (RDP)Ransomware rollbackAutomated remediationAntivirus threat protection in real-timeData loss detection, analysis, and preventionFewer false positive alertsInstant data loss incident responseEndpoint isolation to prevent successful attacksInsider threat management

Storage Protection for DLP

Most companies would focus on securing data in transit because data in storage usually gives off a sense of security. It’s not moving, so nobody can intercept it, right? They can, however, breach your physical or cloud storage and gain access to their contents. Also, data can be accidentally leaked from storage due to human error.

Storage DLP strives to pinpoint confidential files in storage and monitor who accesses and shares them. Monitoring all sensitive data enables companies to identify potential vulnerabilities and secure endpoints before a data leakage can occur.

While storage protection is beneficial to on-site storage systems, it performs exceptionally for cloud-based storage.

Here are its main benefits:

Scans and protects all stored files on the cloudRegularly audits all uploaded filesIdentifies and protects business-critical data on the cloudScans servers to detect and encrypt sensitive data before sharing it on the cloudAlerts admins upon the risk of data leakageAutomates confidential data controls to comply with corporate policies (prompts, encrypts, and blocks data if needed)Grants better cloud storage visibility and control to suit data privacy regulations and security protocolsReduces the risk of data leakage in all virtual systemsUses de-identification options to reduce risk even further (tokenization, masking)Inspects all data stored regularly (both structured and unstructured storage)

Building a DLP Plan

Implementing robust DLP software is just one step of a comprehensive data protection strategy.

Businesses should rely on highly educated IT specialists, end-user awareness for employees, and best practices to structure on-site workstations, storage, and home-office devices. The most successful DLP strategies compile tech, educated staff, process controls, and company awareness.

First and foremost, you can strive to implement a single, centralized DLP plan across your company.

Companies with inconsistent DLP practices risk exposing less protected departments to more data leaks, leading to increased security costs. Also, company employees tend to follow a DLP plan better when the whole organization supports it.

Once you get everyone on board, it’s critical to conduct inventory and assessment.

Evaluate data types of value to the company and identify their relevance; evaluation helps store the data respectful to its sensitivity. (users’ personal information, payment info, trade secrets, intellectual property, etc.)

Most DLP solutions offer tools to scan file metadata, catalog the results, analyze the files’ content, and estimate the associated risk with each data type. Moreover, a reliable solution can consider data exit points and calculate the expected cost of lost data in case of a leakage.

While this may sound overwhelming, businesses benefit immensely from DLP specialists on-site. You could hire a risk analyst, form a data breach response team, and bring in data usage analysts. A core of experienced professionals, combined with educated employees, raises efficiency and yields better DLP results.

Lastly, every solid DLP plan takes time and effort. Trying to build all of the pieces at once may backfire, exposing critical data to compromise.

Here, slow and steady wins the race!

Data Loss Prevention is Essential for Modern Businesses

We’d start by categorizing data types and securing communication channels. Afterward, you can implement security software components while educating your staff on best DLP practices.

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AI Advances Improve Collaboration of Project Management Tools

AI Advances Improve Collaboration of Project Management Tools

Around the turn of the century, most people were skeptical of the impact that artificial intelligence would have on the future workplace. Many people believed that AI technology would be a footnote in modern business practices. A 1987 article by Harvard Business Review suggested that most of the bold claims about AI would probably never happen.

Advances in AI technology have since proved many of these naysayers wrong. Machine learning has led to some huge developments that are touching every aspect of our lives. The modern workplace is changing in stupendous ways in response to breakthroughs in AI.

One of the biggest ways that AI is changing the workplace is by improving collaboration between team members. New project management software uses complex AI algorithms to offer better service and ensure employees work seamlessly together. This is one of the many ways that AI is helping organizations use time more efficiently.

AI Technology Helps Improve Collaboration in the Workplace

Every organization and team aspires to work faster and smarter toward a shared vision together. A growing number of project management tools are able to achieve these goals through the use of AI technology. Innovations in AI technology have helped companies like Shortcut make managing teams in multiple locations more achievable nowadays.

Collaboration remains challenging for many organizations despite free project management tools. This is because some technologies are too simple and not scalable enough when your company grows. Meanwhile, others are unnecessarily complicated and painful to use. The most viable tools use AI to streamline the process and still deliver great benefits.

Shortcut helps resolve these challenges by providing a project management software platform with a reasonable amount of simplicity and functionality. Instead of wasting precious mental energy figuring out how the system works, development teams can now focus on doing their actual work without friction. They can do this even as organizations grow and scale up.

Tools like Shortcut use AI to offer a number of benefits:

Administrators can use AI to handle basic tasks, which frees their time to focus on more important endeavors.AI applications can use historic data to improve forecasting the budget of a project.AI helps project managers make better insights from past projects.AI helps improve communication between employees.

Having earned the Great Place to Work Certification, Shortcut is living proof of its commitment to equipping every organization’s team with the tools they need to work together and achieve the best outcome possible. Like its employees’ experience, Shortcut is taking note of the users’ feedback and continuously updating and improving its platform.

After adding order and efficiency to software development with a new team-to-workflow feature, the company is launching Docs. It’s a prominent free new feature to integrate better documentation and daily engineering and product work execution. This is one of the many breakthroughs in AI technology that project managers are relying on.

How do the newly released Shortcut Docs help promote better collaboration in organizations’ software development teams? According to its co-founder and CEO, Kurt Schrader, “With Docs, we want to make Shortcut an even more flexible space to create and collaborate around design documents.”

It’s essential for software development teams to have detailed and defined documentation to help them understand better what they need to do to create a series of successful products. But handling a considerable flow of information can make knowledge transfer and efficient communication among software developers more difficult.

Shortcut Docs aims to integrate strategic goals, planning, and execution in a unified experience. AI algorithms facilitate all of these processes. Instead of not knowing where to look for anything, product managers and engineers will have a single source of comprehensive and reliable information in collaborating and building great products.

By keeping documents and plans in sync, software development teams can lessen confusion and ambiguity in their work. There’s no need to replicate content across multiple places to keep everything up to date. Any changes the team makes are automatically synced so everyone can be sure that what they’re working on is the most updated version.

Users can also directly create and open tickets and issues without hopping between tools. This means they don’t have to spend much time reading through lengthy documents to understand what they’re building.

This new documentation system in Shortcut enables software developers to plan, manage and document their work in one place. Docs provide updated documentation in real-time without searching and referencing multiple sources. Thus, the team can focus their mental energy on collaboration and creativity rather than switching between tools.

Software development teams can share their strategy documents, agendas, and action items with specific teammates or have them published for the rest of the organization to access. Deeply integrated with Shortcut’s issue tracking and project management features, Docs seamlessly takes collaboration in software development teams to the next level with the miracles of AI technology.

AI Helps Project Managers Improve Collaboration

New technology is very important for improving project teams. AI has been especially useful in recent years. Project managers need to make use of the latest AI project management tools to improve efficiency and make sure employees can easily communicate.

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