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How to Do Micro-Segmentation the Right Way | Best Deployment Models

Posted on by Hassan

Micro-segmentation is a network security technique that involves breaking a network into small, isolated parts/segments to achieve a higher level of security, limiting the potential impact of a security breach. Each microsegment (workload or application) treated as a separate zone with its unique security policies, making it more difficult for cybercriminals to move laterally within the network if they breach one segment. 

It goes beyond traditional network segmentation which typically divides a network into larger subnets or VLANs. It allows administrators to define certain protection regulations and restrict access to confidential data as well as million-worth applications and services.

Why is micro-segmentation important?

Micro-segmentation is an effective approach for boosting network security, limiting the potential impact of security breaches, and ensuring compliance with industry regulations.

Some reasons for its importance are:

  1. Upgraded Security: 

By dividing a network into smaller components, micro-segmentation reduces the network’s attack surface, making it harder for hackers to continue moving forward across the network to access sensitive data or systems.

  1. Granular Access Controls: 

Micro-segmentation enables organizations to implement granular access controls and restricted access to specific applications or services based on user roles, device type, location, and other factors.

  1. Compliance: 

Many regulatory frameworks, such as PCI DSS and HIPAA, require companies to have strong network segmentation controls to protect sensitive data. Micro-segmentation can help them meet these compliance requirements professionally and competently.

  1. Better Resource Allocation: 

It allows organizations to allocate resources more efficiently by dedicating resources to specific microsegments rather than deploying resources across the entire network.

How does micro-segmentation differs from traditional network segmentation?

The difference between Micro-segmentation from traditional network segmentation are:

  1. Granularity: 

Traditional network segmentation is a practice of dividing a network into larger subnets or VLANs, while micro-segmentation creates much smaller segments, allowing for more granular access control and security policies.

  1. Dynamic Policies: 

With traditional network segmentation, security policies are usually applied at the subnet or VLAN level and remain static. Micro-segmentation, however, allows for dynamic policies that can be applied at the individual workload or application level, allowing for more agile security measures.

  1. Zero Trust: 

Micro-segmentation operates on a zero-trust security model, where no device or user is automatically trusted, and access control is based on need-to-know principles. Traditional network segmentation may still rely on a trust-based model that automatically trusts specific segments.

  1. Automation: 

Micro-segmentation can be automated using software-defined networking (SDN) or network virtualization technologies, allowing for more efficient deployment and management. Traditional network segmentation may require manual configuration and management.

Overall, micro-segmentation is a more advanced approach to network segmentation that provides higher security and flexibility than traditional methods.

Planning & Implementing micro-segmentation policies

On the one hand, planning for micro-segmentation is important in upscaling network security and looking after business-focused assets from cyber threats. On the other hand, implementing micro-segmentation policies involves configuring the rules and guidelines that dictate how traffic flows through the network.

By identifying critical assets, creating a network map, and defining micro-segmentation policies, organizations can better protect their networks against cyberterrorism and reduce the risk of data breaches.

Here are the steps for micro-segmentation:

  1. Identify Critical Assets: 

Acknowledge the most vital assets found within your network, such as sensitive data, mission-critical applications, and high-value resources. These assets should be the focus of your micro-segmentation efforts. Let’s start pinning down the applications, data, and resources that direly demand timely segmenting.

  1. Creating a Network Map:

Create a network map to identify all the devices, applications, and services to understand how data flows through your network and where to apply micro-segmentation policies.

  1. Define Micro-Segmentation Policies: 

Once you have enlisted your critical assets and created a network map, you can define micro-segmentation policies which should be based on the principle of least privilege, letting only authorized users and devices access specific resources. When defining policies, you should also consider other factors such as user roles, device type, location, and time of day.

  1. Implement Micro-Segmentation: 

Now, it is time to implement them. This can be done using software-defined networking (SDN) or network virtualization technologies. You may also need to update your network infrastructure, such as firewalls and routers, to support micro-segmentation.

  1. Test and Monitor: 

After deploying micro-segmentation, testing and monitoring your network to verify that policies are being enforced correctly with no vulnerabilities or gaps in your security is paramount. Regular testing and monitoring help highlight and address issues before they become major security threats.

By following these steps, you can effectively plan for micro-segmentation and guarantee your network is secure and compliant with industry regulations.

Best Practices for Micro-Segmentation Deployment

Selecting the appropriate deployment model is crucial to consider while implementing micro-segmentation. There are several deployment models, including host-based, network-based, and cloud-based. Each model has its benefits and drawbacks, and the choice will depend on your organization’s specific needs.

Deployment Models of Micro-segmentation:

  1. Host-based Deployment Model: 

The host-based deployment model involves implementing micro-segmentation at the host level, generally through software agents. This model is ideal for organizations with many virtual machines or containers that must be guarded individually.

  1. Network-based Deployment Model: 

The deployment model is all about deploying micro-segmentation at the network level using SDN or network virtualization technologies. It is a perfect pick for companies with complex networks that need to be kept away from the dangerous world.

  1. Cloud-based Deployment Model: 

The cloud-based deployment model refers to implementing micro-segmentation in cloud environments, such as public or private clouds. This model seems more suitable for firms that rely heavily on cloud services and must preserve data and applications in the cloud.

Here is a table comparing the three deployment models:

Deployment Model Benefits Drawbacks
Host-based Allows for individual segmentation of virtual machines or containers It can be resource-intensive and may require additional software
Network-based Secures the entire network and allows for centralized management It may demand additional hardware and may be complex to set up
Cloud-based Protects cloud environments and allows for easy scaling May be limited by cloud provider capabilities and may require additional configuration

By considering the benefits and drawbacks of each deployment model and evaluating your organization’s specific needs, you can choose the suitable deployment model for your micro-segmentation implementation.

Real-World Examples of Micro-Segmentation

Micro-segmentation is becoming increasingly popular as a security strategy in various industries. Here are some real-world examples of micro-segmentation implementation:

  1. Financial Services: 

Banks and financial institutions use micro-segmentation to secure networks and tactful financial data. Micro-segmentation helps them to comply with industry regulations and safeguard against cyber threats.

  1. Healthcare: 

Healthcare organizations use micro-segmentation to preserve their patient data. By segmenting their networks, they can isolate sensitive patient data and shelter it from uncertified access.

  1. Retail:

Retail organizations use this approach for securing payment systems and customer data. Segmentation defends payment data from unauthorized access.

  1. Manufacturing:

Manufacturing companies use micro-segmentation to save their industrial control systems (ICS) from cyber threats. By segmenting their ICS networks, they can shield critical production processes from disruption and avert cyber attacks that could result in costly downtime.

  1. Government: 

Government agencies are in favour of micro-segmentation as it takes care of their networks and confidential information. By segmenting their networks, they can isolate critical data and shield it from unaccredited access.

These are just a few examples of how micro-segmentation is being used in various industries to enhance network security.

Case studies of successful micro-segmentation deployments

Here are a few actual case studies of successful micro-segmentation deployments by different companies:

  1. PayPal

PayPal, an American online payments system company, deployed a cloud-based micro-segmentation solution to keep their payment processing systems safe. Using a cloud-based approach, they could quickly and easily deploy micro-segmentation policies across their entire cloud infrastructure. This allowed them to isolate and safeguard their payment processing systems from other areas of their cloud network. 

As a result, PayPal saw a significant reduction in security incidents related to payment processing systems.

  1. Duke Energy

Network-based Micro-Segmentation: Duke Energy, an American electric power holding company, implemented network-based micro-segmentation to save its industrial control systems (ICS). Using a network-based approach, they could segment their ICS network and watch over their critical production processes from cyber threats. Duke Energy also used a zero-trust approach to ensure that only authorized devices had access to specific segments of their ICS network. The deployment resulted in a significant reduction in security incidents related to their ICS network.

  1. Anthem

Host-based Micro-Segmentation: Anthem, an American health insurance company, utilized host-based micro-segmentation to shield patient data. Using a host-based approach, they could isolate sensitive data on individual servers and guard it against unauthorized access. They also used a zero-trust approach to make sure that only credited individuals from the staff had access to the servers. The deployment consequently improved data protection and compliance with industry regulations.

Conclusion

Micro-segmentation is a highly effective security approach that assists organizations in safeguarding their valuable assets and confidential data from cyber threats. This strategy involves dividing a network into smaller, more controllable sections, enabling companies to isolate and secure their essential data while minimizing the attack surface area for cybercriminals. Also, deploying micro-segmentation requires careful planning and consideration of the specific needs and requirements of the organization and network. 

Choosing a suitable deployment model, creating effective policies, and working with experienced security professionals are important to promise a successful implementation.

Mainframe vs. Server: How Are They Different?

Posted on by Hassan

Introduction:

While choosing the right technology for your organization, it’s essential to understand the differences between mainframe vs server. After all, mainframes have been the backbone of large organizations for decades. However, servers have become increasingly popular recently, offering scalability and affordability for many applications.

But what exactly sets these two technologies apart, and which is right for your business? Although cloud computing provides businesses with greater scalability, cost savings, flexibility, security, and reliability than traditional mainframes or servers.

In this article, we’ll take a closer look at both techs’ key differences, strengths, and weaknesses to help you make an informed decision.

What is a Server?

A server is a network device that manages access to hardware, software, and other resources while serving as a centralized storage place for programs, data, and information. It may host anything from two to thousands of computers at any time. Accessing data, information, and applications on a server using personal computers or terminals has become easier. 

What is a Mainframe?

A mainframe’s data and information can be accessed via servers and other mainframes. However, the increased scalability can only be accessed with cloud services that allow businesses to quickly and easily increase or decrease the resources they use, depending on their needs. Unfortunately, this is not possible with mainframes or servers.

Enterprises can use mainframes to bill millions of consumers, process payroll for thousands of employees, and handle inventory items. According to research, mainframes handle more than 83 percent of global transactions. 

Differences Between Mainframe And Server

Continuing the debate on server vs mainframe, these are two of the most important computer systems used in today’s businesses. Both are designed to handle large amounts of data and processing power, but they differ in several ways and have diverse strengths and weaknesses.

MainframeServer
Size and PowerMainframes are large and powerful computers designed to handle heavy workloads. These computers today are about the size of a refrigerator.
Mainframes can process massive amounts of data and support thousands of users simultaneously, making them ideal for large organizations with critical applications.
A typical commodity server is physically smaller than a mainframe designed for specific tasks or tasks. They can range in size from a small tower computer to a rack-mounted System.
Servers often support specific business functions, such as File and print services, web hosting, and database management.
User CapacityMainframes are designed to handle many transactions per second, providing fast and reliable access to data.Servers are known to support fewer users. They are designed to handle a smaller workload but can be scaled up to support more users if necessary.
CostMainframes are more expensive than servers in terms of initial investment and ongoing maintenance costs. They require significant hardware, software, and personnel investment to set up and maintain. However, their reliability and security can justify the cost for organizations with critical applications.Servers are typically less expensive, making them a more cost-saving option for smaller businesses or those with less critical applications. They are easier to set up and maintain and require fewer resources to run.
ApplicationsMainframes run critical applications, such as financial transactions and airline reservations, where dependability and safety are of utmost importance.
They are constructed to handle massive amounts of data exclusively and provide quick and efficient access to information.		The server can be used for various tasks, including file and print services, web hosting, and database management.
They are often used to support specific business functions and can be scaled up to cater to the ever-changing business demands.
Reliability Mainframes are known for their high levels of reliability and uptime. They are capable of handling critical applications and providing protected and faster access to data, even during a system failure.
They are also equipped with advanced security features to secure sensitive information.		Servers can be less reliable due to their smaller size and limited resources. They can handle different workload levels than mainframes and may provide different levels of reliability and uptime efficiency.

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Suitable Use Of Mainframe Computers in various industries and why?

Once upon a time, the term “mainframe” referred to a massive computer capable of processing enormous workloads. Mainframe is useful for health care, schools, government organizations, energy utilities, manufacturing operations, enterprise resource planning, and online entertainment delivery.

They are well-suited for the Internet of Things (IoT), including PCs, laptops, cellphones, automobiles, security systems, “smart” appliances, and utility grids. 

IBM z Systems server control over 90% of the mainframe market. A mainframe computer differs from the x86/ARM hardware we use daily. Modern IBM z Systems servers are far smaller than previous mainframes, albeit they are still significant. They’re tough, durable, secure, and equipped with cutting-edge tech.

The possible reasons for using a mainframe include the following:

  1. The latest computing style
  2. Effortless centralized data storage
  3. Easier resource management
  4. High-demand mission-critical services
  5. Robust hot-swap hardware
  6. Unparalleled security
  7. High availability
  8. Secure massive transaction processing
  9. Efficient backward compatibility with older software
  10. Massive throughput
  11. Every component, including the power supply, cooling, backup batteries, CPUs, I/O components, and cryptographic modules, comes with several impressive levels of redundancy.

Mainframe Support Unique Use Cases

Mainframes are unique to be utilized when the commodity server cannot cope. The capacity of a mainframe to handle large numbers of transactions, their high dependability, and support for various workloads make them indispensable in various sectors. The companies may use commodity servers and mainframes, but a mainframe can cover gaps that other servers can’t.

Mainframe Handle Bigdata

According to IBM, the Z13 mainframe can manage 2.5 billion daily transactions. That’s a considerable quantity of data and throughput. To handle big data, you must look for a server upgrade to a mainframe. 

It’s challenging to compare directly to the commodity server since the number of transactions they can support varies depending on what’s on the server in question. Furthermore, the sorts of transactions may be vastly different, making it impossible to compare apples to apples.

However, assuming that a typical database on a standard commodity server can handle 300 transactions per second, it works out to roughly 26 million transactions per day, a significant number but nothing near the billions a mainframe can handle.

Mainframes Run Unique Software (Sometimes)

Mainframes are generally driven by mainframe-specific programs written in languages like COBOL, which is a significant differentiating characteristic. They also use proprietary operating systems, such as z/OS. Three important points to ponder are:

  • Mainframe workloads cannot be moved to the commodity server.
  • You may transfer tasks that typically run on a commodity server to a mainframe.
  • Virtualization allows most mainframes to run Linux as well as z/OS.

As a result, the mainframe provides you with the best of both worlds: You’ll have access to a unique set of apps that you won’t find anywhere else and the capacity to manage commodity server workloads.

Mainframe May Save Money If Used Correctly

A single mainframe can cost up to $75,000, considerably more than the two or three thousand dollars a decent x86 server could cost. Of course, this does not imply that the mainframe is more costly and fails to meet your demands or to offer something incredibly extra. Remember, with a $75,000 mainframe, you’ll receive much more processing power than a commodity server. 

However, cloud computing is often more cost-efficient than investing in and maintaining expensive mainframes or servers.

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Conclusion

Mainframes and servers are high-functioning computer systems with various pros and cons. Organizations with critical applications may benefit from the high reliability and security of mainframes. In contrast, those with less critical applications may find servers a more affordable option. However, the choice between a mainframe and a server will depend on the specific needs of the organization and the applications it needs to run.

 

Big Data, Cloud Computing, and the Internet of Things: 3 Important World-Changing Technologies

Posted on by Hassan

How do Big Data, Cloud Computing, and the Internet of Things Work?

People’s insatiable appetite for digital technological advancements has driven a prominent revolutionary demand to improve data flow and connectivity between humans and all interrelated computing devices.

Big Data, IoT, and Cloud Computing are working in harmony to enable improved analytics and insight-driven decision-making for many businesses. Developing and processing data is necessary to improve efficiency in system processes and effective communication in every organization.

This article explains how these three world-changing technologies are working together as separate entities equipping businesses with the data-driven insight they need to stay ahead of the competition. Not forgetting the key benefits that you can reap from this relationship.

The Internet of Things and The Importance of its Relationship

Business trends of current market leaders highlight the importance of businesses exploiting the benefits provided by the Internet of Things and its computing capacity to track customer needs and spearhead business innovation insight.

People usually keep multiple devices connected to the internet.

The ongoing digital revolution continues to highlight the need for significant innovations to improve connectivity, data collection, and analysis; with precise collection and sharing of insights between different systems and IoT devices connected through the internet.

Robust connectivity is necessary to help streamline people’s social activities and improving daily workflow.

What is Big Data?

Big data is the excessive collection of structured and unstructured data that cannot get processed effectively using traditional digital and non-digital methods. It empowers businesses with large amounts of radical knowledge regarding their target market and customers.

The data provides knowledgeable insights that improved business decisions and performance.

Big Data has four key features known as the 4 V’s of Big Data:

  1. The V more commonly associated with big data is Volume. The quantities of data collected by organizations may grow to incomprehensible proportions over time as new business opportunities arise. Big Data comes to the rescue and makes the storage and processing of large volumes of data smoother.
  2. Velocity–The varying volumes of data must first get collected and stored accordingly before being made available for retrieving. The Velocity feature of Big Data focuses on tracking the speed of how data gets collected and transferred without interruptions. Some types of data have limited time to use their value, and velocity ensures that businesses are up to speed with processing data quickly and faster than before. For example, retail IoT applications enable businesses to improve sales by immediately identifying out-of-stock products and ensuring they are readily available for consumers.
  3.  Big Data comes in a wide Variety, thanks to multitudes of data sources: encrypted packets, decrypted packets, photos, videos, and sensor data, to name a few. Data is not always uniform when it enters the database; it’s collected in structured or unstructured form.
  4. Veracity–Big Data IoT devices collect a variety of large quantities of data at high speeds. The data must present relevance and test for accuracy before it’s usable.
    Data veracity is the feature that determines the precision and accountability of data. It uses statistics, machine learning, and algorithms to analyze the data’s biases and anomalies and establish trust levels.

Here is a clear, step-by-step blueprint to help you understand and build a big data strategy that will be effective for your business. There are many real-world examples from a cross-section review of many businesses.

What is Cloud Computing?

Cloud computing is a service that uses IoT-connected devices to store, process, and distribute data to provide access to flexible innovation capacity. Businesses strive to reduce operational costs and build economies of scale.

Cloud services eliminate the hustle of purchasing equipment and software needed to set up and run a data center. Making large amounts of data available from anywhere upon demand, giving businesses a lot of capacity for flexibility.

What is the Cloud?

The Cloud is the data platform ensuring that data and resources get shared and are always accessible. Storage, processing, and analytics are achievable without physical infrastructure. Also, cloud services propel higher levels of service speed, security, and flexibility to scale.

Cloud services provided include Google Cloud, Microsoft Azure, and Amazon Web Services.

The Harmony between Big Data, Cloud Computing, and IoT

Big Data plays the role of an analytics platform for data collected by IoT-connected devices. Cloud Computing, using a Cloud platform, provides the network’s storage location. When these three technologies get used together, many exponential growth opportunities emerge for businesses. Take a quick look at this practical example of its application in agriculture.

Big Data and Cloud Computing

Big Data involves collecting vast amounts of information. Cloud Computing uses the software as a service model to provide the storage location where the data gets accessed for processing. These two technologies are key in providing the infrastructure that enables real-time data processing. The key benefit of using the Cloud to store big data is quick and cost-efficient scalability.

On a pay-as-you-go basis, of course…

Combining these two technologies improves cost-efficiency and can quickly transform your business into a market leader. The data collected and provided highlights and generates workable insight and builds a substantial competitive advantage.

The Internet of Things and Big Data. How it Works

The Internet of Things establishes the precise source of information whenever a business needs to extract data for analysis and examination purposes. It enhances the capacity to process enormous quantities of data in real-time. Big Data is invaluable in IoT-data extraction.

Together, they highlight extremely lucrative business correlations and trends within collected sets of data. IoT and Big Data provide actionable insight and predictive analytics to improve forecasting. Providing your business with such data computing power provides precise and progressive insight to improve business decision-making within record time.

Internet of Things and Cloud Computing. How it works

IoT and Cloud Computing often end up in the same sentence whenever people discuss technical services. They propel each other’s existence and make significant contributions to the IoT ecosystem. Their crucial differences make them effective technical solutions, individually and as a whole.

The Cloud provides a centralized storage server that holds computer resources and enables quick access. The key role of Cloud services in the Internet of Things is to collaborate storage of data for connected IoT devices. Computing data using these modern technologies will provide your businesses with a cost-effective passage for transferring large data packages through the internet.

Incorporating Big Data helps complete the chain. Together, Big Data, IoT, and Cloud Computing propel cost-efficient automation of business systems. They also provide a competitive edge over real-time data monitoring and management.

Benefits of Using Big Data, IoT, and Cloud

Scalable data collection, storage, real-time research and business development, analysis, and delivery are critical for smart cities and smart businesses. Data is a critical differentiator of products and services. Listed below are some of the many benefits of using Big Data, IoT, and Cloud Services in your business:

Increased Capacity and Scalability
The Cloud allows easy expansion of Big Data, storage, and data analytics. Server capacity can increase with more applications or hardware resources as needed. Depending on your business needs regarding Big Data, Storage, and Analytics, you can scale Cloud-based solutions vertically and horizontally.

Scalable Infrastructure Capacity
You must always plan for the best-case scenario of forecasts and expectations within your desired business data models. This way, the risks of failing to keep up with customer expectations get reduced throughout the business. Invest in a scalable IoT platform because physical infrastructure is unnecessary for you to run Big Data, the Internet of Things.

They collaborate to provide storage for large amounts of data and deliver scalable processing and real-time data analysis. Your business costs get minimized significantly. You can focus on improving analytical capacity and service provision instead of server equipment maintenance.

Increased Operational Efficiency
The Cloud provides smooth access and passage to the enormous amount of data consistently generated by IoT and Big Data.

Universal Access to Distributed Apps
Cloud storage and IoT collaboration enable access to Big Data from any location and continue to conduct business using IoT-connected devices.

Advanced Analytical Capabilities and Asset Monitoring
Connecting too many devices to the internet can put pressure on capacity and Internet connection. Big Data and Cloud computing are driving intelligent IoT devices by improving efficiency with provisions for data exchange by sending data to servers for processing instead of central servers. You can access data from anywhere within your network for fast predictions, better service, and response to downtime.

Economies of Scale
Longevity is a necessity and business requirement for IoT. The ROI must help justify any IoT investment, as it upholds the usable lifetime of devices. Every business looks to better the ROI and reduce the total cost of ownership. The Cloud provides in-built management tools, processing capacity, and applications to help manage your resources. Merging Big Data, IoT, and the Cloud helps preserve business value using efficient storage and data management.

How Can this trio Help Improve your Business?

Employing Big Data, The Cloud, and the Internet of Things can improve your business success efforts. It ensures real-time communication and accuracy in the transference of data between devices and people, whilst providing safe heaven for IoT-Big Data processing and analytics.

Your business can benefit immensely by taking advantage of Cloud storage and combining it with IoT and Big Data. Doing this will enhance scalability, reliability and make your business solutions more agile. The interdependent relationship between Big Data, IoT, and the Cloud provides uninterrupted access to data and actionable insights for better performance and analysis.

We Can Help!

Would you like to discover more about how to leverage IoT, Cloud Computing, and Big Data to modernize and improve your business? Contact our seasoned IoT professionals at EES Corporation for a quick but in-depth chat to discuss the best IoT services to make your service invaluable.

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