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7 Ways to Mitigate Your SaaS Application Security Risks

If you’re a SaaS entrepreneur or you’re looking to build a SaaS application, in that case, you may already be aware of the fact that there is a new economy that has evolved around SaaS (Software as a Service). Core business services are offered to the consumers as a subscription model via pay-per-use in this SaaS market. Studies have revealed that Software as a service (SaaS) enterprises are evolving at a sky-rocket speed. They are becoming the first choice due to their simple up-gradation, scalability, and low infrastructure obligations. Per Smartkarrot.com, the SaaS industry’s market capitalization in 2020 was approximately $110 Billion and is expected to touch the $126 billion mark by the end of 2021. And it is expected to reach $143 billion by the year 2022. However, security is one of the primary reasons why small and medium businesses hold back from taking full advantage of powerful cloud technologies. Though the total cost of ownership was once viewed as the main blockage for possible SaaS customers, security is now on top of that list. The anxieties with SaaS security evolved with more and more users embracing the new technology, but is everything all that bad as reviews and opinions hint? Here are 7 SaaS security best practices that can help you in curbing SaaS security risks, that too cost-effectively: 1. Use a Powerful Hosting Service (AWS, Azure, GCP, etc.) and Make Full Use of their Security The biggest cloud providers have spent millions of dollars on security research and development and made it available worldwide. Leverage their infrastructure and the best SaaS cybersecurity practices that they have made available to the public and focus your energy on the core issue(s) your software resolves. API Gateway Services Security Monitoring Services Encryption Services 2. SaaS Application Security — Reduce Attack Surface and Vectors Software/Hardware – For example, do not define endpoints in your public API for admin related tasks. If the endpoint doesn’t exist, there is nothing else to secure (when it comes to SaaS endpoint protection)! People – Limit the access people have to any sensitive data. If required, for a user to access sensitive data, log all the actions taken and, if possible, make it necessary to have more than one person involved in accessing the data. 3. SaaS Security Checklist — Do not Save Sensitive Data Only capture data you absolutely need. For instance, if you never use a person’s national ID number (e.g., SSN), don’t ask for it) Assign a third party for sensitive data storing. In this, for example, your system never holds possession of any credit card number, so you don’t have to worry about protecting it. 4. Encrypt all your Customer Data — Adopt the Best SaaS Security Solutions Data at Rest: When any data is saved either as a file or inside a database, it is considered “at rest.” Almost every data storage service can store the data when it is encrypted and then decrypt it when you ask for it. For example, SQL Server enables you to turn on a setting to encrypt the stored data with their Transparent Data Encryption (TDE) feature. Data in Flight: When data is read from storage and transferred out of the currently running process, it is called “in-flight.” Sending data over any networking protocol, be it FTP, TCP, HTTP, is data that is “in-flight.” Network sniffers (if attached to your network) can read this data, and if it is not encrypted, it can be stolen. Employing SSL/TLS for HTTP is a typical example. 5. Log All Access and Modifications to Sensitive Data — Opt for a Robust SaaS Security Architecture There’s no guarantee that your system’s security will never be breached. It is more of a question of “when will it happen” rather than “if it will happen.” For this very reason, it is crucial to log all changes and access to stored sensitive data and adjustments to user permissions and login attempts. When something actually goes wrong, you have an audit log that can be used to solve how the breach occurred and know what needs to change to stop any further similar security breaches. 6. Implement Two-factor Authentication Social engineering is the most common way which hackers use to breach any system. Make social engineering hacks more complex by asking users to have a second step to authenticate with your system. Implement a system that needs at least two of the following three types of information: Something the user knows (e.g., username/password) Something the user has (e.g., phone) Something the user is (e.g., fingerprint) Sending a code to a user’s phone or email is a simple yet effective way to implement two-factor authentication. To balance the added security with the demand for usability, give your clients the option of choosing if they would like to use the phone or email and an option for the code validity for the device being used. 7. Use a Key Vault Service Key Vaults allow the stored sensitive data to be accessed only by applications that have been given access to the Key Vault, removing the need for a person to handle the secrets. A Key Vault stores all secrets to encrypt data, databases/datastores access, electronically signed files, etc. Cloud platforms like Azure and AWS offer highly secure and configurable Key Vault services. For extra security, use different key vaults for different customers. For advanced security, allow your customers to bring their keys. Takeaway There are several reasons why businesses must take advantage of cloud computing to enhance their operational efficiency and reduce their costs. Nevertheless, security concerns often hold back businesses from placing their valuable data in the cloud. But, with the right technology and best practices, SaaS can be far more secure than any on-premise application, and you can have numerous options for retaining control over your security infrastructure and address the security issues head-on with your respective provider.

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Application Security Cloud Security SaaS Security

IaaS vs. PaaS: Everything You Need To Know

PaaS and IaaS are two of the earliest and most widely used cloud computing services. They are similar in some ways, yet fundamentally different types of platforms. In simple words, IaaS is the combination of PaaS, Operating System, Middleware, and Runtime. Enterprises must understand these differences to choose the right type of cloud service for a given use case. Infrastructure-as-a-Service (IaaS) offers added control and flexibility over cloud infrastructure but is more complex to manage and optimize. In contrast, Platform-as-a-Service (PaaS) solutions offer the tools and the infrastructure required to expedite deployment. However, security, integration, and vendor lock-in are issues to look at in PaaS. This blog explains the definitions of IaaS vs. PaaS, its benefits and drawbacks, and a few examples of both IaaS and PaaS. IaaS vs. PaaS- Definitions Infrastructure as a Service (IaaS) offers on-demand access to virtualized IT infrastructure through the internet. Mostly, IaaS offerings allow access only to the core infrastructure components like compute, networking and storage. Users can install and manage the software they want to run on their cloud-based infrastructure. Platform as a service (PaaS) offers the infrastructure to host applications and also software tools to help clients build and deploy the applications. PaaS simplifies the entire setup and management of both hardware and software. Comparatively, PaaS is less flexible than IaaS and mainly caters to a narrow set of application development or deployment approaches. To be honest, they are not general-purpose replacements for an enterprise’s complete IT infrastructure and software development workflow. IaaS vs. PaaS- Benefits Infrastructure-as-a-Service solutions offer networking, storage, servers, operating systems, and other resources required to run the workloads. Infrastructure is made available by making use of the virtualization technology and can be used on a pay-as-you-go model. Benefits of IaaS solutions are: Fast scalability with the capacity to quickly provision or release computing resources as and when needed. Lowered costs, as companies pay only for the infrastructure they employ. Better usage of IT investments as there is no need for over-provisioning. Higher agility, offering enterprises the capacity to move quickly and take advantage of business opportunities. Platform-as-a-Service solutions offer cloud-based environments for developing, testing, running, and managing web-driven and cloud-driven applications. Companies get a state-of-the-art development environment without the need to buy, build or manage the underlying infrastructure. Benefits of PaaS solutions are: Rapid results with less time for coding, as PaaS solutions primarily include built-in options for pre-coded elements. More straightforward collaboration. Thanks to a development environment hosted in the cloud it is easier for distributed teams to collaborate. Better performance, with support for the entire web application lifecycle inside a single integrated environment. Lower costs due to agile development at scale. IaaS vs. PaaS- Disadvantages Disadvantages of IaaS are: The infrastructure runs legacy applications with cloud services, but these infrastructures might not be devised to secure legacy controls. Management of some internal resources required to manage business tasks Training is required more than often. Clients are responsible for business continuity, backup, and data security. Disadvantages of PaaS are: The data residing in the cloud servers is controlled by a third party. It can often be challenging to connect the services with the data stored in onsite data centers. It might not be easy to manage system migration if the vendor does not offer migration policies. Though PaaS services usually offer a wide range of customization and integration features, customization of legacy systems can become a big concern with Platform as a Service. PaaS limitations can be associated with particular services and applications as PaaS does not support all languages users want to work with. IaaS vs. PaaS- Examples Examples of IaaS are: AWS- Amazon Web Services Microsoft Azure Google Cloud Digital Ocean Alibaba Cloud Examples of PaaS are: Heroku Elastic Beanstalk from AWS Engine Yard Open Shift from RedHat Conclusion IaaS and PaaS are the most impressive emerging technologies ruling the world of cloud computing currently. Both have their own benefits and disadvantages. However, understanding the details given above can help identify which of these services will be beneficial for you to use. The choice depends on the requirements of specific workloads. To keep up with the emerging standards of modernization, enterprises must invest in cloud computing. Not only will it help in serving your customers better, but it will also help your business grow. It will remove the complexities and limitations that traditional IT infrastructures pose. Once you’ve decided that, choose whether you must opt for IaaS or PaaS, depending on how you want to run your cloud-based applications.

Aziro Marketing

cloud computing IaaS Paas

8 Things to Consider Before Choosing Your DRaaS Provider

In today’s era, business information is much more valuable and sensitive than ever before. According to a recent survey held by the University of Texas, you’ll be surprised to know that nearly 94% of companies undergoing a severe data loss do not survive, 43% never reopen, and almost 51% close within 2 years of the loss. Also, per Gartner, 7 out of 10 SMBs go out of business within a year of experiencing a major data loss. These statistics clearly show that with a growing dependency on information technology, the prospect of downtime, mass loss of data, and losing revenue is a very real concern, not to mention the long-term damage these occurrences bring to your company’s image and potential profit. The surge of disaster recovery as a service (DRaaS) presents a range of opportunities to safeguard our infrastructure and resources. DRaaS uses the infrastructure and computing resources of cloud services and presents a practical option to an on-site technology DR program. Administrators and IT leaders can make use of it to supplement their existing DR exercises by adding more comprehensive performance abilities. They can also employ the technology to replace their current DR activities entirely. Disaster Recovery as a Service (DRaaS) offers faster and more flexible recovery options for physical and virtual systems across different locations, with shorter and quicker recovery times. Yet, like any other advanced technology, DRaaS also bring various risks to the table. A vital tool for overcoming such DRaaS risks is known as a service-level agreement (SLA). It includes what the DRaaS vendor will provide based on performance metrics, such as uptime percent, percent availability of resources, and blocked security breaches. It also spells out solutions, such as financial penalties or refunds of maintenance costs for vendor failure to satisfy SLA conditions. Below, we discuss a few top risks involved with DRaaS and ways to mitigate them. Risk Issues of DRaaS and Ways to Mitigate Them 1. Access control In case of an emergency, securing access to critical data and systems is imperative to prevent any unauthorized access and possible damage. If a vendor has a Service Organization Control 2 (SOC 2) report available, make sure you ask for a copy of the same. But why? Because it provides you the audit data that addresses security, availability, confidentiality, processing integrity, and privacy metrics. 2. Security Considering that your critical company data might soon reside or already residing in a cloud environment, the security of that data is of greater concern than when the data was stored on site. Hence, ensure that your DRaaS provider has an extensive set of security resources to ensure that your critical business data is safeguarded and is always accessible. One such approach that you can follow is to work with a vendor that has multiple data centers with redundant storage facilities so that your critical business data can be kept and stored in more than one location. 3. Recovery and restoration These are the two key metrics in a DRaaS program that indicate how quickly a company’s data and systems can be restored to service after a disruptive incident. If your DRaaS provider’s track record during disasters compels you to take a pause for concern, adjust the parameters accordingly in the SLA or consider returning critical systems and data on-site to an alternate DRaaS vendor. 4. Scalability and elasticity The most important reason for the growing demand for managed services is their ability to adapt to changing business requirements quickly. While negotiating contracts and SLAs, you must make sure to evaluate the additional resources that can be made available during an emergency and how soon they can be activated. A vendor must fully disclose where the data and systems are kept and how resources are federated among other vendors. This is necessary to make sure that the data is accessible whenever required. 5. Availability It would be best to make sure that your resources are accessible when and where you need them. It is essential to keep in mind that every minute that technology and/or data aren’t restored in case of a disaster, your business runs the risk of a severe disruption to operations. Data in a SOC 2 report can shed some light on potential availability issues. 6. Data protection Never forget that lack of adequate data integrity controls can really endanger customer systems and data. So make sure that your vendor provides suitable data protection controls. 7. Updating of protected systems System and data backups must be made according to a client’s requirements. For example, full backups and added backups and security access to those backups must be safeguarded. Again, your SOC 2 reports can provide valuable information on these activities. 8.Verification of different data, data backups, and disaster recovery Your vendor’s capability to quickly verify data backup and system recovery is necessary for your IT management. So that, in any case of disaster, those key activities can be fully confirmed. Final Thoughts To summarize, true disaster recovery is a process of a continuous feedback loop, where testing and new information are included in the program to enhance your recovery options. But, without constant testing and feedback, your disaster recovery plan is ineffective. The point of all this is not to confuse you in any way, but instead, to help you open your eyes to the realities of DraaS risks you might experience in the near future. With all this knowledge, you must appropriately create a recovery plan that is extensive and well thought of, rather than full of missteps. Consider all this information when you start looking for a DRaaS provider to prepare the best plan possible.

Aziro Marketing

Cloud Security DRaaS Recovery as a service

Ensure All-Round Cloud Data Warehouse Security With these 3 Measures

The volume, scope, and severity of cyberattacks seem to be swelling with the sudden rise in remote business interactions. Reportedly, Australian multi-national banking and services firm ANZ has had data breaches in 47% of its businesses. This raises the question that with organizations collecting data blocks from any and every source they can get their hands on – How secure are our storage resources?Cloud Data warehouse holds data from multiple sources, including internal audits, customer data, marketing feedback, and more. Protecting such critical business influencing data cannot be left to the usual cloud storage security measures we employ. We need network security and access control methods that are specific to the cloud data warehouse architecture. How do we go about it, and what are these security methods exclusive to the needs of a cloud data warehouse? This would be the prime discussion in this blog.Security Overview for Cloud Data WarehouseThe cloud data warehouse vendors like Amazon Redshift, Azure SQL warehouse, etc., have multiple security procedures dedicated to protecting the cloud warehouse data. The API calls are monitored and controlled for their access. Clients are encouraged to support appropriate security layers like TLS 1.0 or later. The data is encrypted with forwarding secrecy ciphers like Diffie-Hellman (DHE). The request authorization is controlled using access IDs, security groups, etc. Some vendors also use temporary security credentials for certain requests. Resource based access also allows the cloud data warehouses to restrict resource access for certain source IPs.Broadly classifying the dedicated security measures for cloud data warehouse would leave us with:Network SecurityCluster SecurityConnection SecurityWe will now discuss these three security aspects one by one.Network SecurityFor cloud data warehouses, network security is worked through network isolation. Most of the venders prefer logically isolated and virtually private cloud networks where the clusters can be deployed using the following steps:Step 1 – A logically isolated network layer is created using specifics like – Subnet, routing table, network gateway, and network endpoints.Step 2 – The allocation and aggregation of the network are done using Classless Inter-Domain Routing (CIDR).Step 3 – Interfaces like consoles, CLIs and SDKs, are created to access the networks.Step 4 – Two or more subnets are created for dedicated accounts.Step 5 – The cluster is deployed in the network.The cluster can be locked down for inbound network traffic. You can decide which IP addresses are permitted to access the cluster in your network. Therefore, the network is all secure to entertain the client request, and what remains is to secure the clusters themselves.Cluster SecurityGenerally, the cloud data warehouses have the cluster locked for access by default. They are later granted access as per the resources requirement and process handling they are deployed for. An effective way to do this is by categorizing the clusters into security groups. These security groups define the access control depending on the network subnet provisioned for the cluster. Vendors like Amazon Redshift have default as well as customized classes called the security groups. With customized classes, one can define access policies by themselves.The policies that categorize these security groups generally are meant to identify a range of IPs that are permitted to access the corresponding clusters. The classes can be created with or without a cluster provisioned to them. The inbound access policies can be defined for the group and the cluster can be launched later. There are mainly three kinds of interfaces that can be employed to create security groups.GUI Consoles – GUI consoles can help to create security groups on the basis of details like class name, CIDR range, IP authorization details, and user account authentication details. These consoles are offered internally by most of the cloud data warehouse vendors. They can also be used to define the access policies for the group.CLI Commands – Most of the cloud vendors also offer CLI commands for creating the security groups, adding or revoking the access policies, and managing the clustersSDKs – Open source codes for Java or AWS SDKs are available for managing the security groups. The default code doesn’t have any ingress rules, and they can be added to the code as per the CIDR range required.With clusters and subnets secured using security groups, additional security can be ensured by securing the connections that access these networks and clusters.Connection SecurityThe connection security majorly deals with securing the endpoints on the connections. Any API requesting a connection with the cluster can be provided access using a secure endpoint like Virtual Private Cloud (VPC) instead of a public network. With the endpoint secure, the ODBC and JDBC connections can establish the communication between the client and the warehouse more securely. The endpoint security can be ensured using resources like – VPNs, Internet gateways, network address translation, or like Amazon Redshift, directly accessing the AWS Network.The private connection can be created with a secure DNS that can be customized or offered by the vendor.Here are some of the code snippets for a different kind of VPCs offered by AWS:Denying all accessSpecific User AccessRead-Only AccessEnd-point security also protects the network from use prone access issues. With the network, clusters, and API requests secured, the additional layers for cloud storage security can ensure that the organizational data is all safe for business.Security MonitoringApart from the above discussed measures, it is also necessary that the warehouse is constantly monitored for security misbehavior. Consistent monitoring of the network, workload and clusters from a security point of view can be configured with regular reports on surface level dashboards.Final ThoughtsCloud Data warehouses are all set to churn out influential business insights through the data being fed to them from multiple sources. While this makes them a gold mine for pioneering business ventures, they also become a target for security breaches, data losses, and network attacks. Therefore, apart from the security and data protection available for cloud storage infrastructures, these warehouses would need specific security measures that align with their own architecture. With the measures discussed above, you can rest assured of the knowledge and intelligence that the cloud warehouse has to offer.

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Cloud Security data-security Security Monitoring

Overcoming 5 Key Challenges of Analytics in the Cloud

In today’s world of enterprise IT, managing vast amounts of data is necessary for all digital transformation. According to MarketsandMarkets.com, the global cloud analytics market size is anticipated to expand from 23.2 billion USD in 2020 to 65.4 billion USD by 2025, at a Compound Annual Growth Rate (CAGR) of nearly 23.0% during the forecast period. Several enterprises choose cloud analytics because it makes it simpler for them to manage and process large volumes of data from various sources. It presents real-time information while offering superior security. Hence, it isn’t a surprise that almost 90% of the industry say that data analytics must be moved to the cloud faster.However, analytics in the cloud demands diverse architectures, skills, approaches, and economics compared to executing batch analysis in-house and in a traditional way. And with all these changes, there are bound to be obstacles to overcome. Here are a few of the challenges we might face and ways to address them as we move towards performing data analytics in the cloud.1. Losing Control and the Fear of UnknownBefore the cloud came into prominence, the usual roles of IT leaders and the CIO have been to safeguard and be a guardian of data assets. The idea of moving the data analytics process to the cloud can be daunting for IT leaders who are usually habituated to having complete control over resources. With all this in mind, the key challenge that any client faces with cloud analytics is organizational inertia or fear of losing control. To resolve this issue, we can work together to vet and get comfortable with the cloud platforms so that we can help derive business value and gain a competitive edge. This requires the adoption of proven and emerging models instead of the need to design or architect the analytics environment from zero.Initially, enterprises are slow to explore new analytics opportunities due to the rigidity of their current analytics processes, which results in lesser initiatives and incentives to try new opportunities and drive innovation. To overcome this challenge, IT teams can use a cloud-enabled sandbox environment to install a trial-and-error ideation process, making use of the key performance indicators from essential stakeholders and creating a prototype-first analytics environment.2. Making the ShiftApart from overcoming the perceived loss of control, we must deal with the actual move to the cloud and make sure that there is no interruption of services. For several IT leaders, the hardest thing is to navigate the path to the cloud. But it does not have to be that way if we opt for suitable solutions or tools. It is recommended to find tools that make it simple to replicate and extract data across several environments. The shift with the right tools can optimize the data analytics and accelerate performance up to almost 240 times.3. Securing the DataIrrespective of how much cloud service providers emphasize the safety of their infrastructures, several people will always be worried about the safety of their data in the cloud. This is particularly true with analytics because the insights acquired from analyzing data can be a true competitive differentiator. Also, there is worry about exposing highly sensitive data such as customer information. Security is top-of-mind any time we plan to shift our organization’s valuable data out of a private data center. The biggest security concern is regulating access to cloud applications and data. The ease with which anyone can use cloud applications opens up numerous challenges, several of which originates from the fact that people can accidentally create security, privacy, and economic concerns.To overcome this concern, we need strong governance around the appropriate use of data. This is more urgent in the cloud environment than on-premises as it’s easy to copy data and use it in ways that are unauthorized.4. Acquiring the Right SkillsAll thriving IT efforts always come down to having in place the essential skills. Hence, moving analytics to the cloud from on-prem is no exception. Rather than experts to support each part of the technology stack in conventional analytics or BI [business intelligence], the cloud analytics environment demands more ‘full stack’ thinking.The technology teams supporting these new-age environments must understand all the offerings on a cloud platform, adopt the standard patterns, and then evolve with the new techniques, tools, and offerings to handle this challenge. Organizations that opt to build their own analytics platform in a cloud environment or depend upon vendor systems must have particular in-house technical expertise, which involves skills to create, manage, and derive analytics from a data lake, and the knowledge of employing cloud-native or third-party artificial intelligence and machine learning capabilities to extract additional insights from the environment.5. Avoid a Cloud Money PitThough making use of cloud services can help us avoid expenses like on-premises storage systems, costs can soon get out of control or come in higher than what is anticipated. When deciding on moving analytics to the cloud, we can often feel pressured to spend a high upfront expense and get locked into a long-term contract that doesn’t fit the existing requirements. The key is to look for a provider that doesn’t force cloud lock-in. While evaluating cloud platforms, we shouldn’t be afraid to shop around for the right solution that can address the current analytics requirements, with the flexibility to scale up as required for our future needs.While it’s simple to get going in the cloud, it’s also easy to move an incorrect type of job and leave cloud resources and applications running even after they are no longer required. Two of the most efficient ways to regulate cloud expenses are to take control of the way cloud accounts are created and be entirely transparent about who is consuming cloud resources.Final ThoughtsThe rise of cloud analytics computing is still just beginning. Vendors are struggling with the challenges of architecting their software to accommodate the vision and requirements of a true cloud environment. The good news is that some vendors sell customized cloud analytics tools tailored to our particular needs, like sales or marketing. Also, others sell tools with broader capabilities that can be adapted to various use cases.

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Big Data Analytics cloud Cloud Analytics

Data Reduction: Maintaining the Performance for Modernized Cloud Storage Data

Going With the Winds of Time A recent white paper by IDC claims that 95% of organizations are bound to re-strategize their data protection strategy. The new workloads due to work from home requirements, SaaS, and containerized applications call for the modernization of our data protection blueprint. Moreover, if we need to get over our anxieties of data loss, we are to really work with services like AI/ML, Data analytics, and the Internet of Things. Substandard data protection at this point is neither economical nor smart. In this context, we already talked about methods like Data Redundancy and data versioning. However, data protection modernization extends to the third time of the process, one that helps reduces the capacity required to store the data. Data reduction enhances the storage efficiency, thus improving the organizations’ capability to manage and monitor the data while reducing the storage costs substantially. It is this process that we will talk about in detail in this blog. Expanding Possibilities With Data Reduction Working with infrastructures like Cloud object storage, block storage, etc., have relieved the data admins and their organizations from the overhead of storage capacity and cost optimization. The organizations now show more readiness towards Disaster recovery and data retention. Therefore, it only makes sense that we magnify the supposed benefits of these infrastructures by adding Data Reduction to the mix. Data reduction helps you manage the data copies and increase the efficacy value of its analytics. The workloads for DevOps or AI are particularly data-hungry and need more optimized storage premises to work with. In effect, data reduction can help you track the heavily shared data blocks and prioritize their caching for frequent use. Most of the vendors now notify you beforehand about the raw and effective capacities of the storage infra, where the latter is actually the capacity post data reduction. So, how do we achieve such optimization? The answer unfolds in 2 ways: Data Compression Data Deduplication We will now look at them one by one. Data Compression Data doesn’t necessarily have to be stored in its original size. The basic idea behind data compression is to store a code representing the original data. This code would acquire less space but would store all the information that the original data was supposed to store. With the number of bits to represent the original data reduced, the organization can save a lot on the storage capacity required, network bandwidth, and storage cost. Data compression uses algorithms that represent a longer sequence of data set with a sequence that’s shorter or smaller in size. Some algorithms also replace multiple unnecessary characters with a single character that uses smaller bytes and can compress the data to up to 50% of its original size. Based on the bits lost and data compressed, the compression process is known to be of 2 types: Lossy Compression Lossless Compression Lossy Compression Lossy compression prioritizes compression over redundant data. Thus, it permanently eliminates some of the information held by the data. It is highly likely that a user may get all their work done without having to need the lost information, and the compression may work just fine. Compression for multimedia data sets like videos, image files, sound files, etc., are often compressed using lossy algorithms. Lossless Compression Lossless compression is a little more complex, as here, the algorithms are not supposed to permanently eliminate the bits. Thus, in lossless algorithms, the compression is done based on the statistical redundancy in the data. By statistical redundancy, one simply means the recurrence of certain patterns that are near impossible to avoid in real-world data. Based on the redundancy of these patterns, the lossless algorithm creates the representational coding, which is smaller in size than the original data, thus compressed. A more sophisticated extension of lossless data compression is what inspired the idea for Data deduplication that we would study now. Data Deduplication Data deduplication enhances the storage capacity by using what is known as – Single Instance Storage. Essentially a specific amount of data sequence bytes (as long as 10KB) are compared against already existing data that holds such sequences. Thus, it ensures that a data sequence is not stored unless it is unique. However, this does not affect the data read, and the user applications can still retrieve the data as and when the file is written. What it actually does is avoid repeated copies and data sets over regular intervals of time. This enhances the storage capacity as well as the cost. Here’s how the whole process works: Step 1 – The Incoming Data Stream is segmented as per a pre-decided segment window Step 2 – Uniquely identified segments are compared against those already stored Step 3 – In case there’s no duplication found, the data segment is stored on the disk Step 4 – In case of a duplicate segment already existing, a reference to this existing segment is stored for future data retrievals and read. Thus, instead of storing multiple data sets, we have a single data set referred at multiple times. Data compression and deduplication substantially reduce the storage capacity requirements allowing larger volumes of data to be stored and processed for modern day tech-innovation. Some of the noted benefits of these data reduction techniques are: Improving bandwidth efficiency for the cloud storage by eliminating repeated data Reduces storage capacity requirement concerns for data backups Lowered storage cost by reducing the amount of storage space to be procured Improves the speed for disaster recovery as reduced duplicate data makes the transfer easy Final Thoughts Internet of Things, AI-based automation, data analytics powered business intelligence – all of these are the modern day use cases meant to refine the human experience. The common pre-requisite for all these is a huge capacity to deal with the incoming data juggernaut. Techniques like data redundancy and versioning protect the data from performance failures due to cyberattacks and erroneous activities. On the other hand, data reduction enhances the performance of the data itself by optimizing its size and storage requirements. The modernized data requirements need modernized data protection, and data reduction happens to be an integral part of it.

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cloud storage Cloud Storage Data data-storage

Most Common IaaS Security Issues and Ways To Mitigate Them

With today’s world of constant digitization, enterprises are continuously shifting their workload to the IaaS platform from the legacy infrastructure because of its speed and flexibility. Gartner expects IaaS to grow by nearly 13.4% to $50.4 billion by the start of 2021. However, as it is a cloud-driven concept, one cannot deny the presence of issues and security risks. The catch here is that just a single feature cannot provide complete security for the IaaS environment. It is so because the IaaS platform’s protection is a kind of shared responsibility, where customer security responsibilities involve ensuring cloud infrastructure is architected, deployed, and operated safely. The responsibilities also include maintaining the cloud security in aspects of firewalls, operating systems, data, platforms, etc. Whereas the providers have to secure the cloud in aspects like storage, global infrastructure, database, compute, etc.IaaS security issues are the most critical concerns for both users and providers alike, which need to be solved for high performance. Therefore, we present this blog to make the readers aware of the IaaS security issues. This will help in choosing a suitable solution for business data protection.Security issues in Infrastructure as a ServiceInfrastructure as a Service has some issues that must be resolved for high performance. These issues can be divided into two broader categories.Component wise security issues1. Service level agreement or SLA driven issues: SLA is the agreement between the client and the service provider concerning the quality of services and uptime guarantee. Enforcing the SLA and properly monitoring the SLA is one of the most common challenges one faces while maintaining trust between the provider and the client. The solution to this challenge is a Web Service Level Agreement (WSLA) framework, which is created to monitor and enforce the Service Oriented Architecture. WSLA maintains SLA trust by enabling third-party innovation to maintain the SLA provisions in cloud computing.2. Utility computing driven issues: Utility computing is known to be the commercial face of Grid and cluster computing, for which the users are charged per usage of services. The primary challenge involved with utility computing is its complexity; for instance, a service provider provides services to a 2nd provider, who also provides services to others. This makes it difficult to meter the services for the charges. The other challenging issue is that the whole system will become vulnerable to attackers who want to access services without paying. The answer to the first challenge is Amazon Devpay, which enables the provider at the 2nd level to meter the service usage and bill the consumer accordingly. The solution for the second challenge is that the service provider must keep the system clean from viruses and malware and keep the system risk-free. The system is also affected by the client’s practice; therefore, the client must keep the authentication keys safe.3. Cloud software-driven issues:Cloud software is the key that connects the cloud components to act as one single component. A cyber attacker has the power to attack against the XML services security protocols and attack the web services that can lead to a complete breakdown of the communication of services. The solution to mitigate these kinds of attacks is the XML signature for authentication and integrity protection. Another solution is XML Encryption that wraps the data in an encrypted manner, and that data needs to be decrypted to retrieve the original data.4. Networks driven issues: Internet connectivity and networking services play a critical role in delivering a service over the internet. There are issues in networks and internet connectivity, such as the “Man in the middle attack” – when an attacker manipulates the network connectivity by generating middle man access, from where the attacker can access all the classified permissions and data. The other type of such attack is known as the “flooding attack,” when an unauthorized user sends bulk requests to increase the chances of an attack due to those requests. The potential solutions can be like traffic encryption, which uses point-to-point protocols to encrypt the connectivity for avoiding the externals attacks. Another suitable solution can be continuous and efficient network monitoring on services to verify whether all networking parameters are running correctly or not. The externals attacks can also be avoided by implementing firewalls to protect the connectivity from outer attacks.Overall security issuesOverall security issues are judged on the basis of overall services rented by an IaaS provider. A few of these type of issues are as follows:1. Monitoring of data leakage and usage: All the data that is stored in the cloud must be kept confidential. It indicates that the providers and the clients must be aware of how the data is being accessed and ensure that only authorized users have access to the data. These issues can be solved by up-to-date data management services, which will continuously monitor the data usage and also restrict the usages as per security policies.2. Logging and reporting: Proper logging and reporting modules must be employed effectively to make the deployment of IaaS more efficient. Superior logging and reporting solutions can keep track of the whereabouts of the information, its user, the information about the machines handling it, and the storage area keeping it.3. Authorization and authentication:It is a well-known fact that just using a user name and password may not be enough for a highly secure authentication mechanism. It is the most common security measure a system has to maintain. A service provider must use multi-factor authentication to tackle this threat.Source : Security ChecklistConclusionThese are some of the risks and issues that must be resolved before deploying any service in the cloud. Superior monitoring of the resources must be done effectively to accomplish the quality of services and high performance from the providers. It is always better to enforce preventive measures before the matter goes out of hand. Industry authorities strongly recommended taking IaaS security as a serious concern. Although securing an IaaS environment is a challenge, the high level of control enables a customer to design and implement the security controls as per their requirements..filledCheckboxes input[type="checkbox"]{opacity:0;display :none;}.multiStepFormBody button.close{z-index:99;}

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5 Key Motives to Adopt a Cloud-Native Approach

While some may argue that cloud-native history has been building for a while, it was companies like Amazon, Netflix, Apple, Google, and Facebook that heralded the underrated act of simplifying IT environments for application development. The last decade saw a bunch of highly innovative, dynamic, ready to deliver, and scaled-at-speed applications take over businesses that were stuck in complex, monolithic environments, and failed to deliver equally compelling applications. What dictated this change in track was the complexity and incompetence of traditional IT environments. These companies had already proven their competitive edge with their knack in identifying and adapting futuristic technology, but this time, they went back and uncomplicated matters. They attested cloud-native to be “the” factor that simplified app development if we are to continue this trend of data overload. Their success was amplified by their ability to harness the elasticity of the cloud by redirecting app development into cloud-native environments. Why Is Cloud-Native Gaining Importance? Application development has rapidly evolved into a hyper-seamless, almost invisible change woven into the users’ minds. We are now in an era where releases are a non-event. Google, Facebook, and Amazon update their software every few minutes without downtimes – and that’s where the industry is headed. The need to deploy applications and subsequent changes without disrupting the user experience have propelled software makers into harnessing the optimal advantages of the cloud. By building applications directly in the cloud, through microservice architectures, organizations can rapidly innovate and achieve unprecedented business agility, which is otherwise unimaginable. Key Drivers for Organizations to Going Native 1. Nurtures innovation With cloud-native, developers have access to functionally rich platforms and infinite computing resources at the infrastructure level. Organizations can leverage off the shelf SaaS applications rather than developing apps from scratch. With less time spent on building from the ground up, developers can spend more time innovating and creating value with the time and resources at hand. Cloud platforms also allow the trial of new ideas at lower costs –through low-code environments and viable platforms that cut back costs of infrastructure setup. 2. Enhances agility and scalability Monolithic application architectures make responding in real-time tedious; even the smallest tweaks in functionality necessitates re-test and deployment of the whole application. Organizations simply cannot afford to invest time in such a lengthy process. As microservice architectures are made of loosely tied independent elements, it is much easier to modify or append functionalities without disrupting the existing application. This process is much faster and is responsive to market demand. Additionally, microservice architectures are ideal for exploring fluctuations in user demands. Thanks to their simplicity, you only need to deploy additional capacity to cater to fluctuating demand (on an individual container), rather than the entire application. With the cloud, you can truly scale existing resources to meet real-time demand. 3. Minimizes time to market Organizations are heavily involved in time-consuming processes in traditional infrastructure management- be it provisioning, configuring, or managing resources. The complex entanglement between IT and dev teams often adds to the delay in decision making, therefore obstructing real-time response to market needs. Going cloud-native allows most processes to be automated. Tedious and bureaucratic operations that took up to 5-6 weeks in a traditional setup can be limited to less than two weeks in cloud-native environments. Automating on-premise applications can get complicated and time-consuming. Cloud-based app development overcomes this by providing developers with cloud-specific tools. Containers and microservice architectures play an essential part in making it faster for developers to write and release software sooner. 4. Fosters Cloud Economics It is believed that most businesses spend a majority of their IT budget in simply keeping the lights on. In a scenario where a chunk of the data center capacity is idle at any given point in time, it demands the need for cost-effective methodologies. Automation centric features like scalability, elastic computing, and pay-per-use models allow organizations to move away from costly expenditures and redirect them towards new features development. In simple words, with a cloud-native approach, you bring the expenses down to exactly what you use. 5. Improves management and security Managing cloud infrastructure can be handled with a cluster of options like API management tools, Container management tools, and cloud management tools. These tools lend holistic visibility to detect problems at the onset and optimize performance. When talking of cloud, concerns related to compliance and security are not far off. The threat landscape of IT is constantly evolving. When moving to the cloud, businesses often evolve their IT security to meet new challenges. This includes having architectures that are robust enough to support change without risking prevailing operations. The loosely coupled microservices of cloud-native architectures can significantly reduce the operational and security risk of massive failures. Adopting Cloud Native for Your Business Migrating to cloud-native is a paradigm shift in the approach of designing, development, and deployment of technology. By reducing the complexity of integration, cloud-native provides a tremendous opportunity for enterprises. They can drive growth by leveraging cloud-native environments to develop innovative applications without elaborate setups. Organizations are looking at a lifelong means of creating continuously scalable products with frequent releases, coupled with reduced complexities and opex. Cloud and cloud-native technologies signify the building of resilient and efficient IT infrastructure minus the complications, for the future. By selecting the right cloud-native solution provider, organizations can develop and deliver applications faster, without compromising on quality. Conclusion In an era of limitless choices, applications that quickly deliver on promises can provide a superior customer experience. Organizations can achieve this through faster product development, iterative quality testing, and continuous delivery. Cloud-native applications help organizations to be more responsive with the ability to reshape products and to test new ideas quickly, repetitively.

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An Introduction to Serverless and FaaS (Functions as a Service)

Evolution of Serverless ComputingWe started with building monolithic applications for installing and configuring OS. This was followed by installing application code on every PC to VM’s to meet their user’s demand. It simplified the deployment and management of the servers. Datacenter providers started supporting a virtual machine, but this still required a lot of configuration and setup before being able to deploy the application code.After a few years, Containers came to the rescueDockers made its mark in the era of Containers, which made the deploying of applications easier. They provided a simpler interface to shipping code directly into production. They also made it possible for platform providers to get creative. Platforms could improve the scalability of users’ applications. But what if developers could focus on even less? It can be possible with Serverless Computing. What exactly is “Serverless”?Serverless computing is a cloud computing model which aims to abstract server management and low-level infrastructure decisions away from developers. In this model, the allocation of resources is managed by the cloud provider instead of the application architect, which brings some serious benefits. In other words, serverless aims to do exactly what it sounds like—allow applications to be developed without concerns for implementing, tweaking, or scaling a server.In the below diagram, you can understand that you wrap your Business Logic inside functions. In response to the events, these functions execute on the cloud. All the heavy lifting like Authentication, DB, File storage, Reporting, Scaling will be handled by your Serverless Platform. For Example AWS Lamba, Apache IBM openWhisk.When we say “Serverless Computing,” does it mean no servers involved?The answer is No. Let’s switch our mindset completely. Think about using only functions — no more managing servers. You (Developer) only care about the business logic and leave the rest to the Ops to handle.Functions as a Service (FaaS)It is an amazing concept based on Serverless Computing. It provides means to achieve the Serverless dream allowing developers to execute code in response to events without building out or maintaining a complex infrastructure. What this means is that you can simply upload modular chunks of functionality into the cloud that are executed independently. Sounds simple, right? Well, it is.If you’ve ever written a REST API, you’ll feel right at home. All the services and endpoints you would usually keep in one place are now sliced up into a bunch of tiny snippets, Microservices. The goal is to completely abstract away servers from the developer and only bill based on the number of times the functions have been invoked.Key components of FaaS:Function: Independent unit of the deployment. E.g.: file processing, performing a scheduled taskEvents: Anything that triggers the execution of the function is regarded as an event. E.g.: message publishing, file uploadResources: Refers to the infrastructure or the components used by the function. E.g.: database services, file system servicesQualities of a FaaS / Functions as a ServiceExecute logic in response to events. In this context, all logic (including multiple functions or methods) are grouped into a deployable unit, known as a “Function.”Handle packaging, deployment, scaling transparentlyScale your functions automatically and independently with usageMore time focused on writing code/app specific logic—higher developer velocity.Built-in availability and fault tolerancePay only for used resourcesUse cases for FaaSWeb/Mobile ApplicationsMultimedia processing: The implementation of functions that execute a transformational process in response to a file uploadDatabase changes or change data capture: Auditing or ensuring changes meet quality standardsIoT sensor input messages: The ability to respond to messages and scale in responseStream processing at scale: Processing data within a potentially infinite stream of messagesChatbots: Scaling automatically for peak demandsBatch jobs scheduled tasks: Jobs that require intense parallel computation, IO or network accessSome of the platforms for ServerlessIntroduction to AWS Lambda (Event-driven, Serverless computing platform)Introduced in November 2014, Amazon provides it as part of Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code. Some of the features are:Runs Stateless – request-driven code called Lambda functions in Java, NodeJS & PythonTriggered by events (state transitions) in other AWS servicesPay only for the requests served and the compute timeAllows to Focus on business logic, not infrastructureHandles your codes: Capacity, Scaling, Monitoring and Logging, Fault Tolerance, and Security PatchingSample code on writing your first lambda function:This code demonstrates simple-cron-job written in NodeJS which makes HTTP POST Request for every 1 minute to some external service.For detail tutorial, you can read on https://parall.ax/blog/view/3202/tutorial- serverless-scheduled-tasksOutput: Makes a POST call for every minute. The function that is firing POST request is actually running on AWS Lambda (Serverless Platform).Conclusion: In conclusion, serverless platforms today are useful for tasks requiring high-throughput rather than very low latency. It also helps to complete individual requests in a relatively short time window. But the road to serverless can get challenging depending on the use case. And like any new technology innovations, serverless architectures will continue to evolve to become a well-established standard.References: https://blog.cloudability.com/serverless-computing-101/ https://www.doc.ic.ac.uk/~rbc/papers/fse-serverless-17.pdf https://blog.g2crowd.com/blog/trends/digital-platforms/2018-dp/serverless-computing/ https://www.manning.com/books/serverless-applications-with-node-js

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