Case Studies
Case studies are a powerful tool for businesses to showcase their success stories and demonstrate their expertise. They provide real-life examples of how a company's product or service has helped a customer overcome a challenge or achieve a goal. By sharing these stories, businesses can build credibility and trust with potential customers and differentiate themselves from their competitors. With careful planning and execution, a well-crafted case study can be a valuable addition to any marketing strategy.
9 Ways to Mitigate Cloud Migration Risks
How to mitigate risks in the cloud? With digitization and new technologies driving the shift in this era, cloud computing has a good chance of reaching its full potential. The majority of businesses are moving from on-prem to the cloud as traditional on-prem technology can no longer keep up with the unprecedented scalability that is required of most organizations today. Many CIOs consider migration to the cloud as the next logical step to accelerate the digital transformation process. Gartner claims that more than 60% of the enterprise workloads aim to shift to the cloud by the end of 2020. Businesses want to gain a competitive advantage through collaboration, process agility, and innovative business models. Cloud life-cycle management tools provide much-needed flexibility and are cost-effective, helping both small-scale and large-scale businesses to benefit from significant economies-of-scale. For all the advantages that cloud computing provides, it is important to keep in mind that cloud computing is not infallible. It has its own vulnerabilities that one should keep in mind while migrating to the cloud. The following strategies will help mitigate the risks associated with application cloud migration. Discover your environment When developing a practical cloud migration strategy, it is vital to carry out some initial groundwork. The focus should be on the discovery of all the resources and applications environment. This helps to identify the interdependencies between the multiple applications. Once the discovery is complete, the next step is to determine what needs to be migrated and what can be rearchitected or retired. Organizations should look at the component elimination part of the re-platform strategy and identify the architecture components that can easily be replaced. Map out a migration strategy A practice that you can’t ignore is mapping out a migration strategy that identifies clear business motives and uses cases for moving to the cloud. Perhaps, the most advisable strategy is to migrate your applications in phases or to conduct a pilot migration in which you start with the least business-critical workloads. This allows you to prepare for further challenges while moving your entire workload to the cloud. Create a cloud governance framework Given that compliance and security are among the top concerns for organizations moving to the cloud, it is critical to creating a cloud governance framework with clear, policy-based rules that will help organizations prepare for secure cloud adoption. Optimize your network The default network used by public cloud providers is the Internet. Some organizations might worry that the Internet is too slow and not secure enough to meet their business goals. If a dedicated network connection is not necessary, it is still worthwhile to pursue a better, faster service from your Internet Service Provider given that moving to the cloud encompasses users transitioning from accessing data or apps locally, via gigabit-speed local network connections to much slower Internet connections. Properly manage software licensing A real concern for enterprises is whether their existing licenses for on-premise software extend to the cloud. Some software vendors operate a Bring Your Own Software and License (BYOSL) program that gives enterprises permission to migrate their applications to the cloud. Other vendors specify usage rights per number of concurrent users. Leverage Automation wherever possible Downtime or service disruptions are not desirable outcomes for any cloud migration strategy. To minimize disruption and improve the overall efficiency of the migration, it is important to automate repeated patterns wherever necessary. Automation not only speeds up the process of migration, but it also lowers both cost and risk. Monitor cloud usage Keeping in mind the statistic of a 35% cloud budget wastage, you should monitor your cloud usage closely. A centralized dashboard that identifies running instances across different cloud services can really help you out here. Monitoring for compliance and security is crucial, and you’ll ideally want to collect logs from apps, systems, databases, and network touchpoints to ensure information security requirements are being met. Leverage service provider support If you’ve done your due diligence on researching cloud service providers, then you’ll have factored into your decision–or at least you should have factored in–the level of support given by them. A good support team can prove to be a critical ally during any cloud migration project. Cloud support staff are experts in the particular field that they work in and should be able to promptly answer technical questions and/or help you with any issues that you may have. Use an appropriate tool to drive the migration The right tool will help get your selected on-premise applications to the cloud quickly, securely, and in compliance with all safety policies. Deep insights into your legacy systems and a sound cloud migration strategy must also be supported by your selected tool. It is no small feat to ensure that the process of migration goes through smoothly and efficiently. The need for flexibility and speed coupled with reduced costs is always on the rise. Strategic cloud computing leads your company to the path of sustainable success and we are well-equipped with the right platform for you to migrate to the cloud with our state-of-the-art cloud-agnostic platform. At Kivyo, we believe technology should enable businesses to see what’s coming before it happens and automate the best course of action. When it comes to Cloud transformation and infrastructure, this means evaluating your current environment(s), mapping a risk-mitigated transformation design, leveraging AI/ML to automate, and executing it to perfection. Our platform has enabled exactly this for several Fortune 500 enterprises. Get the power of Kivyo’s Cloud platform to automatically deploy applications on the cloud using a workflow-driven self-service catalog, monitor the environment to understand capacity demands and outages, and enable legacy application modernization with Kubernetes container support, and lifecycle management.
Data Security in Cloud
In a cloud environment, multiple organizations share the same resources. There is a chance for data misuse. So it is often necessary to protect data repositories, data in transit, or process. This cannot be achieved with a single data protection technology or policy. Multiple techniques such as authentication, encryption, data encryption, data masking, and data integrity should be combined to create a security model over the cloud. This article discusses the available Data Security features in Amazon AWS and Microsoft Azure in comparison with CSA’s Cloud Control Matrix control framework. Classification – (Sub Control) Data and objects containing data shall be assigned a classification by the data owner based on data type, value, sensitivity, and criticality to the organization. Data Objects/Resources can be classified by this tagging process but not data (i.e files in the storage). Data classification helps the organization to understand the value of data, risk associated, and implement controls to mitigate it. AWS: AWS supports data object tagging for AWS resources such as S3(Simple Storage Service) Buckets and EC2(Elastic Compute Cloud) instances. For example, if any particular S3 bucket has confidential data, that can be tagged with Data Classification = “Critical” tag. Based on the tags, access can be restricted for the resources or encryption can be applied to secure data. Amazon recently launched the Amazon Macie service for data security. Amazon Macie can automatically discover and classify data stored in Amazon S3. Like traditional data classification, Amazon Macie uses keywords, regex, and vector machine learning. But it doesn’t provide the freedom to use custom regex or keywords. The predefined regex, the keyword can be either enabled/disabled. Custom regex or keywords cannot be created based on the organizational requirement. Amazon Macie assigns each matching object with severity such as low, medium, or high. The severity scale/criteria are predefined and cannot be customized. Data classification can also be implemented by third-party solutions hosted on AWS. Azure: Azure resources can be organized using tags. The tag can be applied based on the resource environment (Production/Non-Production), sensitivity (Confidential/Public). Resource policies can be created to ensure that the resources are tagged with an appropriate value. Azure Information Protection service helps organizations to classify and label data stored/accessed in Azure Cloud. Pre-defined patterns can be used for automatic classification. Azure Information Protection also supports custom string or regular expression for data classification. Policies can be set to apply classification automatically. Also, it can prompt users to apply the recommended classification. Handling / Labeling / Security Policy – (Sub Control) Policies and procedures shall be established for the labeling, handling, and security of data and objects which contain data. Mechanisms for label inheritance shall be implemented for objects that act as aggregate containers for data. AWS: AWS Resources can be labeled using tags. These tags can be applied based on the environment (Production/Non-Production), Security, or business. As discussed in the previous section, AWS supports data object tagging for AWS resources such as S3(Simple Storage Service) Buckets and EC2(Elastic Compute Cloud) instances. If a resource is tagged, that tag is not applied to the dependent/attached resources automatically. These dependent resources should be identified and tagged manually. This process can be automated by third-party tools (Ex: Graffiti Monkey). Azure: As mentioned in the previous sections Azure resources can be labeled using tags. These tags should be applied manually for dependent resources. It is not inherited automatically. Using resource policies one can ensure whether the tags are applied properly. Non-Production Data – (Sub Control) Production data shall not be replicated or used in non-production environments. Any use of customer data in non-production environments requires explicit, documented approval from all customers whose data is affected, and must comply with all legal and regulatory requirements for scrubbing of sensitive data elements. AWS: AWS Cloud Database doesn’t provide data masking as the default service. Third-party tools can be used to achieve Data masking in AWS Cloud Environment. DataGuise, HexaTier, Mentis, and Camouflage are few available tools in the market for Data Masking in Cloud. Azure: Azure SQL Database as a service is a relational database service provided by Azure. Azure supports Dynamic Data Masking for this SQL database service. It hides the sensitive data in the result set, while the data in the database is not changed. DDM can be set in Azure SQL DB using Powershell cmdlets or Rest API. A particular user can be excluded from data masking and they can view original data. DDM feature is only available for Azure SQL Database as service not for the databases configured in the Virtual Machines. Third-party data masking solutions should be used to mask data in these databases. Secure Disposal – (Sub Control) Policies and procedures shall be established with supporting business processes and technical measures implemented for the secure disposal and complete removal of data from all storage media, ensuring data is not recoverable by any computer forensic means. In many organizations, production data is replicated and used for testing, leaving the sensitive data unprotected in a test environment. It is often necessary to protect sensitive data in a test environment to meet data security compliance. Sensitive data in the test environment can be masked with dummy data to serve the testing purpose. At the same time, data is protected. AWS: When a user deletes the object from Amazon S3, first it removes the mapping from the public name. This restricts remote access to the deleted data object. The storage area is used by the system for other purposes. Amazon EFS will never serve deleted data. If the organization needs to follow the procedures mentioned in DoD 5220.22-M (“National Industrial Security Program Operating Manual “) or NIST 800-88 (“Guidelines for Media Sanitization”), AWS suggests conducting a specialized wipe procedure before deleting the file system. Azure: Microsoft uses procedures and a media wiping solution that is NIST 800-88 compliant. It destroys the hard drives that cannot be erased. Destruction of hard drive renders the recovery of information impossible (e.g., shredding). Records of the destruction are retained.
BUILDING A MARKET-LEADING SUITE OF DIGITAL-AGE BANKING SOLUTIONS
We built more than 20 service catalog products around standardized EC2, EB, RDS, S3, Load Balancer, Scaling, Workspace, etc. About the Client One of the leaders in the pharmaceutical industry, the client is a multi-national corporation employing over 20,000 people. Business Challenge The infrastructure on demand requires the manual assembly of multiple components at design time. There was an organizational need for – Control, Standardization, Security & Governance, and centrally manage commonly deployed IT services. This brings the solutions to be reusable across multiple projects and self-service enablement such as DB snapshot etc. Solution provided the automation of the product creation with the standardized template, configurations, etc. It facilitated teams to provision AWS resources in a self-service model in compliance with security and governance guidelines. 20 service catalog products have been created around standardized EC2, EB, RDS, etc. Users have the facility to run the product in dry run mode to validate the inputs and launch actual product post validations. AWS Services AWS Cloud Formation AWS Service Catalog AWS SNS AWS S3 AWS Custom Resources Node JS Mustache Templating Engine EC2 EBS RDS Cloud front API Gateway Lambda Step functions SQS Cloud Watch Workspace DynamoDB Congnito Code Pipeline Code Commit Code build KMS Route 53 EB Benefits and Business Impact Kivyo team created a self-service portal for customers and standardized the cloud infrastructure setup. This helped customers with easy on-boarding of new teams for migrating applications onto AWS. The solution now acts as the platform for Cloud Formation template adoption which has helped ensure compliance with business goals and reduce the time-to-market. It reduced resource configuration sprawl to only Service Catalog approved products.
Elaborating Strategy Planning That Focuses On Desired Business
About the Client One of the leaders in the pharmaceutical industry, the client is a multi-national corporation employing over 20,000 people. Business Challenge The infrastructure on demand requires the manual assembly of multiple components at design time. There was an organizational need for – Control, Standardization, Security & Governance, and centrally manage commonly deployed IT services. This brings the solutions to be reusable across multiple projects and self-service enablement such as DB snapshot etc. Solution Kivyo solution provided the automation of the product creation with the standardized template, configurations, etc. It facilitated teams to provision AWS resources in a self-service model in compliance with security and governance guidelines. 20 service catalog products have been created around standardized EC2, EB, RDS, etc. Users have the facility to run the product in dry run mode to validate the inputs and launch actual product post validations. AWS Services AWS Cloud Formation AWS Service Catalog AWS SNS AWS S3 AWS Custom Resources Node JS Mustache Templating Engine EC2 EBS RDS Cloud front API Gateway Lambda Step functions SQS Cloud Watch Workspace DynamoDB Congnito Code Pipeline Code Commit Code build KMS Route 53 EB Benefits and Business Impact Kivyo team created a self-service portal for customers and standardized the cloud infrastructure setup. This helped customers with easy on-boarding of new teams for migrating applications onto AWS. The solution now acts as the platform for Cloud Formation template adoption which has helped ensure compliance with business goals and reduce the time-to-market. It reduced resource configuration sprawl to only Service Catalog approved products.