Your address will show here +12 34 56 78
2020 Blog, Blog, BOTs Blog, Featured

How to interconnect Infrastructure, Applications, Business Processes, Security and Compliance for end-to-end Automation benefits?


Large enterprises have seen a sprawl of assets across Cloud (IAAS, PAAS, SAAS), legacy systems, and global locations. While adoption of Cloud has made it easier to expand footprint, it has also brought in complexities for managing automation across public, private, hosted, in-house applications and infrastructure. Most automation initiatives have departmental impacts and fail to leverage significant benefits due to lack of an Enterprise Architecture Blueprint and mature platform.


RLCatalyst provides an Open Architecture approach to interconnect various systems, applications, and processes similar to the “Enterprise Service Bus” model. This new approach of “software-defined” models, extendable meta-data for configurations and a hybrid architecture taking into considerations modern distributed security needs. This new approach is called Automation Service Bus model that helps to drive “Touchless Automation” with pre-built components and rapid adoption by existing enterprises.



To support a flexible deployment model that integrates with current SAAS based ITSM Platforms allows Automation to be managed securely inside Cloud or On-Premise data centers. The architecture provides for a hybrid approach with multi-tenant components along with secure per instance based BOT servers managing local security credentials. This comprehensive approach helps to scale Automation from silos to enterprise-wide benefits of human effort savings, faster velocity, better compliance and learning models for BOT efficiency improvements.



For more information feel free to contact marketing@relevancelab.com


0

2020 Blog, AIOps Blog, Blog, Featured

With distributed assets across Cloud and non-Cloud environments covering desktops, servers and other devices enterprises are still having a fragmented approach to basic needs of patch management. This brings in unique risks from a Security and Vulnerability perspective. Even when companies do have focus on this area there is a lack of integration between asset management, vulnerability assessment, patch management and governance to ensure a comprehensive solution that leverages “Automation First” Approach and integrated workflows. This is where RLCatalyst ServiceOne brings in a solution for enterprises to leverage this in a Managed Service Model.


The solution covers all enterprise assets and helps do a discovery, vulnerability assessment and then managing the full-lifecycle of Patch Management. The reason patch management is more complicated since large enterprises commonly have modern and legacy systems covering desktops (Windows, Linux, MacOS), Servers (Redhat, Debian, Ubuntu, CentOS, Windows Servers etc.), Network Devices and others covering assets in data centres and cloud (AWS, Azure, GCP, etc.)


RLCatalyst ServiceOne Solution – Five Layers of Vulnerability & Patch Management of your Infrastructure


The whole process of Intelligence Automation of SecOps starts with the asset inventory to ensure you have complete control and visibility of your Infrastructure. Once this is put in place, the next important aspect would be to run periodic Vulnerability Scans using third party applications like Qualys, AWS Inspector etc. Based on the VA scan report, we need to put an automated patch management solution, post which we can run the SIEM tools which can give a real-time analysis of security alerts. The dashboard or the reports provide a holistic view of the health of your overall Infrastructure from a security standpoint, which the CIOs of any Organizations would be keen to see daily.


ServiceOne Patch Management Solution:


ServiceOne Patch Management Solution is a fully integrated solution with Patching, Backup & recovery. Our solution is integrated with ITSM for the overall management of the solution which can help the organizations run periodic scheduled /unscheduled/ad-hoc scans on the system to identify the missing patches and patch them using an approval process.


The IT team verifies the patches based on the periodic scans and categorise them based on the criticality and bundle them. This can then be pushed to the Application owners who can login to ServiceNow and check the available bundles against their set of servers and approve them or reject them. Once approved, basis the next available scheduled maintenance windows, this can then be automated to schedule a backup of the image of the patching servers and then patch the development servers.


The next step would be an approval process post patching to the app owners to check and confirm the application compatibility and functionality of the patches against their applications.


The app owners in this case has the option to reject the patching in ServiceNow in which case, the image which was taken as backup would be restored back to the development instance and in case of approval, the same would get scheduled automatically for patching during the next maintenance window on the production servers


With RLCatalyst ServiceOne solution we provide enterprises a combination of Consulting, Technology and Integrated Services to take care of end to end patch management needs. Customers can leverage the best of the products in the industry across service orchestration, asset discovery, vulnerability assessment, patch lifecycle management and compliance. Enterprises can get started in less than 4 weeks for onboarding, setup, initial compliance and on-going upgrades. A large global enterprise saved $0.5 Million in the first year of operations as they transitioned 5000+ assets across 10+ data centres & Cloud regions into ServiceOne Integrated Patch Management solution with Relevance Lab Managed services.


For more information feel free to contact marketing@relevancelab.com


0

2020 Blog, Blog, command blog, Featured

Automation with simple scripts is relatively easy, but complexity creeps in to solve real-world production-grade solutions. A compelling use case was shared with us by our large Financial Asset management customer. They deal with this customer who provides a large number of properties & financial data feeds with multiple data formats coming in different frequencies ranging from daily, weekly, monthly and ad-hoc. The customer business model is driven based on Data processing on these feeds and creating “data-pipelines” for ingestion, cleansing, aggregation, analysis, and decisions from their Enterprise Data Lake.


The current Eco-System of customer comprises multiple ETL Jobs, which connects to various internal, external systems and converts into a Data Lake for further data processing. The complexity was enormous as the volume of data was high and lead to high chances of failures and indeed required continuous human interventions and monitoring of these jobs. Support teams receive a notification through emails when a job is only completed successfully or on failure. Thus, the legacy system makes job monitoring and exception handling quite tricky. The following simple pictorial representation explains a typical daily Data Pipeline and associated challenges:



The legacy solution has multiple custom scripts implemented in Shell, Python, Powershell that would make a call to Azure Data Factory via an API call to run a pipeline. Each independent task had its complexities, and there was a lack of an end to end view with real-time monitoring and error diagnostics.


A new workflow model was developed using the RLCatalyst workflow monitoring component, (using YAML definitions) and the existing customer scripts were converted to RLCatalyst BOTs using a simple migration designer. Once loaded into RLCatalyst Command Centre, the solution provides a real-time and historical view with notifications to support teams for anomaly situations and ability to take auto-remediation steps based on configured rules.


We deployed the entire solution in just three weeks in the customer’s Azure environment along with migrating the existing scripts.



RLCatalyst Workflow Monitoring provides a simple and effective solution much different from the standard RPA tools. RPA deals with more End-User Processing workflows while RLCatalyst Workflow Monitoring is more relevant for Machine Data Processing Workflows and Jobs.


For more information feel free to contact marketing@relevancelab.com


0