Vertical Relevance's Control Agent epitomizes the synergy of automation and integration for superior cloud security, encapsulating the journey from threat detection to remediation within a unified framework.
Vertical Relevance's Incident Response Foundation lays the groundwork for an AWS Organization to take advantage of native AWS services for the detection and management of security incidents. It supplements the available CloudFormation resources with a custom resource that can be enhanced for the enabling of additional Security Hub supported services at the organization level.
Vertical Relevance’s Monolith to Microservices Foundation provides a proven framework on how to break the monolith and deliver improved agility to increase the pace of innovation and drive value to your customers and your business. By following the approaches laid out in this Foundation, customers will manage risk and lay out a consistent iterative approach to decompose the monolith into cloud native microservices, following a well-defined process.
The Experiment Generator simplifies the resiliency testing process and accelerates its adoption, organization wide. Implementing the Experiment Generator brings an organization one step closer to its resiliency goals, both in relation to people and processes, in breaking down team barriers and automating as much as we can.
If you're looking to modernize your applications, containers are a powerful tool that you can't afford to ignore. Containers are portable, lightweight, and allow for easy deployment, scaling, and management of applications. They also provide more robust operability and engineering agility. However, as you adopt containerized architectures, you may face challenges in managing the numerous unique services that come with them. That's where container orchestration comes in. In this solution, we explain how specific tools work together to provide a streamlined and efficient way to manage containers at scale, enabling organizations to improve their engineering agility and operational efficiency.
As organizations mature in their cloud journey, they are bound to have many workloads and resources across different AWS regions and accounts. This raises a tough challenge for the security teams to gain visibility into where the organization has the highest risks of security incidents. To avoid financial and reputational repercussions, security engineers and executives need a high-level, real-time view of their security posture within the cloud. This solution addresses the crucial question that keeps organizations’ security executives up at night – “is our IT infrastructure secure and are we meeting compliance requirements?”
Hosting workloads in the cloud can simplify hardware procurement and maintenance, but it doesn’t protect against failures in applications and infrastructure. Many site reliability practices focus on designing highly available architectures, creating resiliency tests, and automating failover for specific components, but these precautions do not replace the need for people and processes to respond effectively during a system failure. In this solution, we discussed the significance of ensuring operational resiliency through gameday execution. We demonstrated how to set up gamedays and how they can supplement your efforts to ensure operational resilience.
With the ever-growing adoption of the cloud and hybrid cloud, businesses are struggling to “connect the dots” when it comes to customer experience – regardless of whether the customer is in-house or external. By implementing instrumentation and distributed tracing as discussed throughout this solution, enterprises will be able to leverage their single pane of glass to improve performance at the margins and quickly identify and remediate application issues as they arise.
A Data Mesh is an emerging technology and practice used to manage large amounts of data distributed across multiple accounts and platforms. It is a decentralized approach to data management, in which data remains within the business domain (producers), while also making data available to qualified users in different locations (consumers), without moving data from producer accounts. It is a step forward in the adoption of modern data architecture and aims to improve business outcomes. A Data Mesh is a modern architecture made to ingest, transform, access, and manage analytical data at scale.
Organizations are rapidly adopting modern development practices – agile development, continuous integration and continuous deployment (CI/CD), DevOps, multiple programming languages – and cloud-native technologies such as microservices, Docker containers, Kubernetes, and serverless functions. As a result, they're bringing more services to market faster than ever. In this solution, learn how to implement a monitoring system to lower costs, mitigate risk, and provide an optimal end user experience.