devopsy.kinsta.cloud

Category: Continuous Delivery

Continuous delivery is a software engineering discipline that allows organizations to consistently develop and release quality software at a rapid pace. It is achieved by automating the software development lifecycle, enabling tasks such as development, testing and release to occur concurrently and continuously.

  • Policy-Based Governance: Modernizing Authorization for the Enterprise

    Policy-Based Governance: Modernizing Authorization for the Enterprise

    As we’ve moved away from on-prem environments to cloud-based ones and policy managed via code, we’ve inadvertently changed the way the infrastructure is managed. The infrastructure that used to be managed by IT/security teams has now shifted to being managed by the engineering operations teams. With that shift, engineering teams have found themselves now in control of new environments, including the access management part. This new change of control and management of access in the privileged environment requires a new approach to the solutions we use for privileged access governance.

    Shift to Cloud-Based Environments

    Cloud adoption has grown rapidly in the past few years, and while many organizations were already moving to the cloud from on-prem, the COVID-19 pandemic accelerated the transition, causing remote working to become the norm and changing the way responsibilities are managed.

    These organizations found they needed to adopt new ways of working to be able to support and provide critical services to off-site workers. As a result, more than 90% of organizations have adopted some form of cloud-based infrastructure, changing the way they work.

    The cloud is prevailing for tech stacks that need to elevate access governance over the cloud infrastructure. In fact, a whopping 94% of enterprises report they are using cloud services today, and 75% say security is a top concern.

    Cloud Shift, Worldwide, 2019 – 2025

    “The shift to the cloud has only accelerated over the past two years due to COVID-19, as organizations responded to a new business and social dynamic,” said Michael Warrilow, research vice president at Gartner.

    Shift to a Fast-Paced, Customer-Value Mindset

    Since switching to a new agile working environment, customers have gotten used to expecting very fast service. Instead of having a change management process that deploys to production once every few weeks, it’s now common to deploy hundreds of times to production every day.

    Access: required more often, more dynamically.

    Dynamic Changes: With the shift to the agile pace in which the new company works, access has been complicated a hundredfold. (Being able to support customers, fix bugs and being able to move very quickly. Needs to support these hundreds of changes to the customer’s product in their hand.

    Scale of Access has Increased: With the shift to cloud-based infrastructure, it’s now possible to create 5000 databases, for example, with a click of a button. All of them require access management because each one requires an understanding of who needs access and in what way.

    New Solutions are Needed

    The cloud providers, AWS, Azure and GCP, are now the way that people work. And most resources, whether databases or the machines that are running are doing so in the cloud environment. However, companies that shifted to the cloud are dissatisfied with what their legacy PAM solutions can provide them for the cloud resources. These solutions are ill-suited for their new needs. Instead, they need a solution that is agile and scalable to match their new working environment.

    With a perceived lack of options, companies are choosing to have the engineering team put in time to create new solutions. These solutions are meant to provide the following benefits: A frictionless way to gain permissions, an easy way to integrate with the tools their team uses today, and instead of manual provisioning and manual access requests, these solutions should automate those tasks.

    They are creating these in a variety of ways, including the following examples:

    1. Dev Portals: Dev portals are internal developer portals that act as a storehouse of information for the developers in a company. They work by centralizing the data in an internal portal, which offers developers and managers the ability to construct a system that facilitates monitoring of the software ecosystem, receiving alerts about alterations, and visually representing interconnected details. This portal additionally simplifies the task of overseeing diverse facets of the software development lifecycle, encompassing aspects like microservices interconnections, ownership statuses, dependencies, resource allocation, and beyond. By utilizing these, companies are delivering and gaining permissions to cloud environments.

    2. Slack Bots: Many times, a developer will need elevated access to resources or databases, so they need an easy way to request and be granted the permissions they need when they need them.

    3. Terraform Automation: Terraform is an infrastructure-as-code tool that lets you build, change, and version cloud and on-prem resources safely and efficiently. Terraform policies and infrastructure code/policies could also be leveraged to define access control policies. The way it works is that engineers or support teams are required to request a change in the Terraform code in order to gain the access they need. If there is a Terraform code in place describing the policy of groups to role access to the relevant infrastructure they would like to gain access to, they can request that access by requesting a Terraform code change. They would need to understand which parts of the code to change and submit a “pull request” to make the desired change which will resolve in the permissions change. Once a Terraform code change is approved, it changes the policy of access to that infrastructure which will change the permissions that the group has.

    (a) Static: The Terraform state is static, meaning that permissions granted in this way are essentially meant to be long-lasting, standing permissions. In addition, these permissions are usually granted to a role and are very coarse-grained(not granularly defined, as they need to suit the whole role’s needs). This creates an environment of over-privileges. That is why, in many cases where the permissions should be granted to a specific task or person or for a limited time, teams choose to use one-off permission granting to manually grant the permissions. However, these one-off permissions create drifts in the environment essentially if they are not revoked. And since there is no process in place for these one-off just-in-time (JIT) permissions, the revocation needs to also be manual and needs to be remembered to be done.

    (b) “Drifts”: (A drift in Terraform is when the environment isn’t accurately reflected in the Terraform policy). Therefore, by changing the access outside of Terraform, you’re creating a drift because it doesn’t match up correctly, making it a poor answer to one-off permissions. For anything needing high privileges, you want it to be dynamic instead of remaining as part of Terraform.

    4. Jira Service Management With Your Cloud Provider: Integrating Jira with GCP (as a cloud provider example), allows the IT security team the ability to automate servicing access tickets by creating forms where teams can request a certain role or be granted access to a certain project.

    5. Manual JIT Cloud Role: This is a method that allows self-serve permissions to engineers to get the permissions they need on demand. The role needs to be predefined, and essentially, as long as the engineer is part of the group, he or she will be able to gain those privileges on-demand. This reduces the standing privileges in the environment at any given point; however, roles granted are usually for a generic cloud admin rather than being granular, as it’s hard to determine ahead of time exactly what permissions will be needed.

    Problems With DIY Solutions

    Because of the lack of the right tool, companies are spending expensive resources creating their own solutions for access control. Team members know they need something that leverages cloud capabilities rather than their PAM for on-premises. These solutions are time-consuming; companies need to build then maintain the infrastructure. They are also very limited in what they can do since they are not the company’s main concern.

    Policy-Based Governance

    With the growing adoption of cloud-based tools, it’s more important than ever that companies learn to adapt. Engineers have now found themselves in control of permissions in new environments they were not looking to manage. It’s clear that a new approach to access governance is necessary.

    The quickest and most sure-fire way to do this is by utilizing tools deliberately made for the new cloud-based era that address the challenge in a way that is suited to the new, fast-paced, dynamic cloud environment. These new solutions are policy-based and leverage the fact that with the shift to new ways of managing infrastructure, it’s necessary to give the IT and Operations teams more control, granularity and automation.

    Platforms such as Apono for cloud-based permissions management allow organizations to benefit from centralized permissions management, enforce dynamic role-based access controls, monitor and audit privileged user activities and mitigate security risks effectively.

    Final Thoughts

    In addition to the shift we have witnessed in the control of the environment and access governance, organizations are also shifting to delegating more ownership to the engineers themselves. This means providing engineering teams more control of their code — all the way to production. However, with that comes the need to give them only granular control over the environment and provide access management to their specific scope without creating a burden on the teams in managing/granting the access. This leads the operations teams to search for solutions to address the need for access management delegation in the environment. New solutions and platforms addressing the new environment’s privileged permissions management challenge need to take that into consideration as well.

    As a final note, I believe the shift in mindset occurred a few years ago already. However, we are only now realizing some of its effects. One of them is the shift in privileged access challenges and all that comes with that. Seeing the many internal solutions that small-to-large companies have created for themselves gives us an understanding of how this evolved market isn’t being addressed with the same platforms that have existed for well over a decade.

  • Evolution of Toil Identification and Mitigation – A Classic SRE Challenge

    Evolution of Toil Identification and Mitigation – A Classic SRE Challenge

    Ingo Averdunk, leading architecture and solutions for cloud service management and site reliability engineering for IBM Cloud, contributed to this article.

    Christine White, architect at IBM Consulting, also contributed to this article.

    When many people think of operations, they often associate it with manual, labor-intensive work. This is because operations traditionally encompass a wide range of activities that involve managing and executing tasks related to producing, distributing and maintaining goods and services. Information technology (IT) is not different: Manual work has traditionally been a significant component of various tasks and processes. Examples are server maintenance, software installation, troubleshooting and network configuration, which historically required manual intervention and human expertise. Much of the work is inherently repetitive by nature, time-consuming and prone to errors. We call this work “toil”, the kind of work tied to running a production service that tends to be manual, repetitive, automatable, tactical, devoid of enduring value and that scales linearly as a service grows.

    Toil

    This toil has always been there. And toil leads to burnout, increased errors and reduced scalability. Only how we have addressed it has changed. While toil remains, toil mitigation has evolved.

    In the beginning, toil was addressed through labor arbitrage. Between 2000 and 2010, we leveraged labor arbitrage to reduce the cost of manual work by outsourcing and out-tasking activities to near-shore and later off-shore countries. When there is a problem, the person who can solve it steps up. Then, to manage it better, we started to do workload or work item management. You have systems, like prioritized backlogs, and we started putting a structure on how we solve these problems. But still, it was all resource-based; everything was resource-intensive. And that is why the engineers became more of the subject matter experts (SME). Being a subject matter expert took time, and being critical to operations became exhausting. And the repetitive manual activities required took time and effort they didn’t have.

    As issues became repetitive, we started thinking about scripting. We evolved from a complete labor arbitrage mentality to writing spot scripts. Engineers began to use scripts to solve a small part of the most oppressive problem.

    Tools started to be developed to enable and automate some of the work. Organizations purchased tools, and education of the tools commenced. It became important to be an SME in your area and an SME of the tools in your area. However, the tools were specific to certain functions and features that they provided, and the software development life cycle (SDLC) had a lot of nuances that many of the tools did not capture. So even when the tools helped, it created this mass of white spaces that could not be integrated easily. Tools were very format-specific and didn’t have the flexibility end to end integration. Newer problems emerged as formats were different, speeds were different, and other differences manifested. Even as this initial approach was an improvement over the labor arbitrage mentality and reduced the amount of manual effort required, it often resulted in complex and brittle systems that were difficult to maintain and debug.

    Automation

    From 2015 to 2020, automation became more prevalent. On one side, the cost levers of labor arbitrage were reduced, and due to low skill, quality suffered. Thanks to technological advancements, automation in various flavors became a viable alternative: Runbook automation, robotic process automation, etc.

    Aligning tool isolation with SDLC nuances required an end-to-end automation philosophy. We need to do automation across the board. Toil elimination was a goal of end-to-end automation, and that obsession led to SRE thinking and the principle of capping operational load. An SRE spends only 50% of his time on operational activities, and the other 50% on “engineering” (improvements): Identification and elimination of problem areas, improving and integrating tooling, etc. And thus, the genesis of the next wave of reliability engineering principles began.

    Parallel to this, adopting DevOps and Agile practices as well as cloud computing enabled IT teams to define and manage infrastructure and application configurations through code, allowing for automated provisioning and scaling. Applications are designed for reliability and instrumented for observability, providing insights into the production use. Traditional organizational barriers are broken down and teams cooperate across the entire SDLC with a shared goal that balances velocity and quality.

    The new focus on leveraging cloud-native technologies and practices to build scalable, flexible and resilient systems allowed SRE teams to adopt cloud-native technologies such as Kubernetes, containers and serverless computing to build systems that are designed to be highly available, fault-tolerant and easy to maintain.

    Toil mitigation and the SDLC lifecycle have evolved from reactive handling of problems to a proactive approach to operations, where alerting is maturing, and other sophisticated instrumentation is implemented. A proactive approach underlined the importance of observability and monitoring. SRE teams are using observability tools such as Prometheus, Grafana and New Relic to monitor their systems in real-time. The ability to identify potential issues before they become incidents emerges.

    AI Infusion

    The next step is being more prescriptive and predictive. That calls for a good bit of AI infusion. We have historical knowledge management systems that will tell us what went wrong, how it went wrong, how it got resolved and how long it took. We can augment that knowledge base with publicly available and curated knowledge.

    Starting in 2020, AI and machine learning are being used in IT for tasks such as anomaly detection, predictive maintenance, risk assessment and automated incident response, reducing the manual effort required for monitoring and maintaining systems.

    AI systems can learn from all this and not only predict issues, but when issues happen, they also auto-resolve. An extension of AI, Generative AI, will disrupt this event more. Prediction of a problem will also lead to the generation of executable runbooks and playbooks, which are agnostic to specific types of automation or automation tools. New challenges such as data quality, model training and explainability have arisen simultaneously and human intervention is still required until we can reach a certain comfort level. There will need to be a manual effort to look at Generative AI proposed solutions, approve them and prove the solutions in lower-level environments, before applying them into production environments.

    In the next 18 – 36 months, we will see that morph into risk-taking, where Generative AI will become digital operations. If we look at an Ops team or a SRE team, there are different personas, and we firmly believe that those personas will be assisted by an AI bot. These bots will participate in daily standups, and they will learn. They will participate in issue-resolution processes, and they will learn. They will start participating in retrospectives, and they will learn. This “AI persona” will become a digital worker in a highly integrated digital team. The manual tasks that once were solved through AI are now becoming complex decision-making processes. We are entering the 4th wave of automation improvements using Generative AI.

    Be Prepared

    Just as the invention of fire, tires, the printing press, the telephone and the lightbulb, what we do with this advancement will greatly shape our future. We must be prepared. And the best way to be prepared is to upskill our practitioners with newer technology skills like AI. The upskilling needs to include all the core and peripheral benefits and concerns to help them manage and improve upon the current day. We need to clarify roles amongst practitioners so there is not an “I’m being replaced by a bot” mentality, and most importantly being open-minded about the possibilities, building on those possibilities and embracing risk rather than fighting it!

  • How to Alleviate Your Observability Challenges

    How to Alleviate Your Observability Challenges

    Observability is becoming a keystone of contemporary DevOps practices. Even departments that weren’t traditionally a part of DevOps are seeing the benefits of being brought under the auspices of observability teams. In 2023, however, organizations are finding that the road to adoption is bumpier than expected. Here are seven of the biggest challenges DevOps teams face with observability and some suggestions for mitigating them.

    Increasing MTTR

    MTTR, or mean time to recovery, is the time it takes to get a system back up and running after an outage or bug. A longer MTTR means more downtime and poorer service for clients. Worryingly, the DevOps Pulse Report indicates that the average MTTR is increasing. This year, 73% of its respondents reported an MTTR of multiple hours; last year’s figure was only 64%.

    MTTR often results from an inability to diagnose incidents due to data siloes impairing observability. This can be improved by an observability platform that allows engineers to see the big picture.

    Costs of Telemetry Data

    Along with increasing MTTR, many organizations have to deal with costs incurred by high telemetry data volumes. It’s a big problem. An IDC survey of 200 companies found that 53% of respondents highlighted the costs of storing log data.

    Much of the problem is due to an outmoded tiered pricing model. Many vendors charge per GB of data, so if your data volumes fluctuate, so will your data costs. They also have obscure pricing models, meaning many organizations find it difficult to know what they’re paying for. At Coralogix, we’ve created a new business model that is one-third of the cost of standard log storage solutions.

    Tool Sprawl

    To understand why this is a challenge, we need to answer the question: What is observability? Effective observability requires integrating data from every aspect of your application. Because many organizations implement monitoring with multiple tools, they suffer from tool sprawl. This has the effect of siloing telemetry data, making it harder to correlate data and extract insights into system performance.

    There are a range of options for mitigating tool sprawl, such as thoroughly evaluating a tool’s costs and benefits before including it in your DevOps strategy. The most effective solution is a “single pane of glass” tool that combines insights synoptically on a single dashboard.

    Kubernetes Complexity

    Elastic reports that organizations are increasingly turning to cloud-based solutions such as Kubernetes for their DevOps. Kubernetes can supercharge organizations with its ability to dynamically scale infrastructure as needed, eliminating the cost overheads of dedicated servers.

    However, Kubernetes is complex and comes with its own set of challenges. Kubernetes’ scalable architecture comes from containerization, a paradigm in which applications are hosted in objects called containers. This means that developing in Kubernetes requires the ability to work with a lot of spinning plates.

    A good way to combat this is better training in organizations. Additionally, breaking down silos allows different teams to transfer knowledge.

    Security Challenges

    Kubernetes’ popularity brings security challenges. These can include privilege escalation, where a user manages to gain privileges such as write access, and security misconfigurations, where developers forget to change the non-secure default configurations.

    There are several strategies for mitigating Kubernetes security risks. These include scoping roles to particular namespaces, using service meshes, and enhancing security with Coralogix’s Kubernetes Operator.

    Beyond Kubernetes, there’s the larger issue of integrating security into an observability strategy, which is becoming a challenge for an increasing number of IT businesses. To counter this, more and more businesses are starting to incorporate observability and security monitoring under a single umbrella. Solutions such as infrastructure and application metrics can enhance security and monitoring.

    Scaling Platforms

    To meet the challenges posed by rising data costs and increasing cloud complexity, businesses are turning to open-source solutions. These come with their own challenges, however. According to the DevOps Pulse Report, around 30% of enterprises surveyed had problems with infrastructure management, scaling and upgrading relevant components. Because many open-source platforms require specialized knowledge to maintain, businesses have trouble sourcing the skills and expertise for them.

    Tools such as OpenTelemetry can make scaling easier by integrating with platforms such as Coralogix.

    Troubleshooting Data Pipeline Performance

    Implementing observability requires having a reliable and high-performance pipeline for telemetry data. However, organizations using open-source platforms often have trouble monitoring and troubleshooting the performance of their data pipeline. This can impair observability as telemetry data is of lower quality.

    Data engineer Abraham Alcantara suggests ten key steps to troubleshoot data pipelines successfully. These include identifying data pipeline software and infrastructure, reproducing and isolating issues and automating issue scenarios. Another strategy is to apply machine learning, such as Coralogix uses.

  • Avoiding Mistakes in Multi-Cloud Configurations

    Avoiding Mistakes in Multi-Cloud Configurations

    The digital sky is bursting with clouds of every kind, and organizations are reaching out to grasp several at once. They seize the opportunities offered by diverse cloud providers, breaking free from the confines of a single vendor. Their approach is a powerful strategic move. Its promise—slashed costs, bolstered resilience and unmatched data back-ups; its bottleneck—configuration issues. And the tightrope between reaping multi-cloud benefits and stumbling into cybersecurity risks is thin.

    “The foremost area where IT leaders have to adapt is cybersecurity,” said Aleksandr Morozov, a cloud security researcher and CTO at Oasis Defender. “As digital interactions multiply, so does our vulnerability to cybersecurity threats. We have to defend against attacks, safeguard data privacy and adhere to international regulations. The adequacy of cybersecurity measures we take directly impacts business integrity and consumer trust.”

    Given these stakes, multi-cloud configuration seems to be an even more tricky aspect to stay on top of. Any slip-ups can spiral into escalating costs, performance hitches and glaring security vulnerabilities. Knowing how to configure multiple clouds mistake-free is essential to keep the promise of multi-cloud alive.

    Multi-Cloud Adoption is Escalating

    On the brink of adopting multiple clouds, a question arises: Why pay more when you can pay less and get more? Organizations steer towards the most cost-effective cloud services, making sure each application finds its optimal cloud match to optimize performance and costs.

    The global march toward digital transformation results in a whopping 92% of organizations already using a multi-cloud approach or in the process of adopting one. Statistics suggest an average usage of 2.6 public clouds and 2.7 private clouds among organizations.

    The multi-cloud setup beckons organizations to distribute their workload across a spectrum of cloud services, each boasting unique benefits. This distribution not only fuels innovation but also provides organizations with robust data recovery mechanisms to use in the event of breaches.

    Multi-cloud also flings open the gates to escape vendor lock-in. No longer shackled to the offerings of a single cloud provider, organizations go for cherry-picking cloud features, explore diverse pricing options and choose geographical locations for sensitive data storage.

    In many regions worldwide, stringent local data regulations dictate the storage of sensitive data within that region’s borders and within specified cloud environments. By leveraging multiple clouds, global organizations can stay compliant with relevant regulations and data governance policies.

    With digital transformation beckoning, organizations are just beginning to harness the benefits of multi-cloud. However, with multiple clouds come multiple risks. Configuring and orchestrating several clouds typically pose challenges, and businesses across industries require robust multi-cloud tools and strategies in place.

    These Industries Need Multiple Clouds More Than Others

    The shift towards a multi-cloud paradigm among businesses isn’t merely a trend chase. It’s a strategic move to elevate operational processes.

    Although the use of a multi-cloud approach is bound with configuration and data breach risks, employing multiple cloud services from different providers brings game-changing benefits. This approach allows businesses to enhance flexibility and improve service monitoring and cost-efficiency—all while helping them better adapt to market changes and technological advances.

    On top of that, a multi-cloud strategy lays a solid groundwork for implementing advanced analytics and AI solutions. Businesses handling vast data, requiring high computational power, or having strict regulatory compliance needs stand to benefit most from this approach.

    “IT leaders must prioritize the infrastructure for remote work. It’s essential that this infrastructure supports seamless collaboration, regardless of geographical boundaries,” Morozov says.

    The global insurance sector is clearly betting big on multi-cloud, with statistics indicating an average use of 11.4 clouds in 2023. Insurance businesses handling massive amounts of sensitive customer data employ multiple clouds as the key to operational efficiency and compliance with data regulations.

    Banking and finance organizations stick to strict industry regulations and use multi-cloud environments to save costs, improve data security, and recover data easily. They seek to use the best features offered by multiple cloud providers to meet data storage rules and avoid reliance on a single provider.

    Besides, financial service organizations often experience rising costs and growing operational issues. That urged them to shift to a multi-cloud approach — their great strategic move. With this transition, they can see a 40% drop in spending by having a clear view of cloud activities across various regions.

    Similarly, health care businesses embrace multi-cloud to distribute workloads across different cloud services and keep their critical and sensitive data secure. They need a reliable backup in case of system failures to improve and maintain high standards of patient care and operations.

    The telecommunication sector is no less in need of a multi-cloud approach. In this field, escaping vendor lock-in and optimizing costs by combining a variety of vendors serve as the propelling forces.

    Retailers typically use around eight clouds from multiple vendors. Their multi-cloud strategies are cost-efficient, help improve scalability, and enhance customer experience by tailoring different cloud services to specific workloads and geographical locations.

    Configuration Mistakes: Why They Occur While Managing Multiple Clouds

    Managing many clouds means juggling a set of rules and systems from various cloud providers. Different settings in each cloud platform can lead to errors, especially when getting clouds to work together. Even a minor mistake can cause issues affecting performance, security, and costs.

    Besides, handling multiple clouds requires special talent. To err is human; common errors involve setting up access controls incorrectly, mismanaging user permissions or incorrectly configuring workloads. Small oversights lead to huge security and operational problems, and without the right training or experience, the chances of making errors increase.

    Configuration mistakes often come from the complex nature of multi-cloud setups. Moving into many clouds means dealing with different infrastructures, APIs, databases, networks, and security systems daily.

    The complexity of multi-cloud makes getting a full view of the whole setup tricky. This lack of clarity can hide configuration mistakes, letting them linger undetected or delaying fixing processes.

    Last but not least, different policies across cloud environments also complicate things. Each cloud setup might have its specific policies and configurations. Without reliable tools, clear rules and ongoing learning, configuration mistakes are imminent.

    How to Avoid Multi-Cloud Configuration Mistakes With AI

    Navigating through multiple clouds can feel like sailing in stormy seas, and organizations need robust tools and reliable solutions to mitigate performance issues and stay safe.

    AI-driven software allows for unified visualization and configuration of multi-cloud security. Let’s see how it aids in the journey.

    Unified Dashboard

    Multi-cloud management solutions bring together data from different cloud providers onto a single dashboard. This makes it easier to see and manage security across an organization’s cloud setup, making the process less complicated.

    Smooth Integration

    A unified interface helps different cloud systems work together smoothly. That means fewer errors and better cooperation between cloud environments.

    Better Visibility

    Having data put together, organizations get a better view of security setup across different clouds. They can see and fix issues faster while keeping multiple clouds secure.

    AI and Machine Learning

    Solutions hinge on using intelligent technology to spot potential threats quickly. Fast actions help reduce the negative impact of any security issues and keep multi-cloud setups shielded from breaches.

    Compliance Monitoring

    The cloud setup has to align with industry rules and standards, which becomes more complicated while using multiple clouds. Using a unified system, organizations can stay compliant and avoid legal issues.

    AI-driven solutions offer an intelligent way to untangle the complex knots of multi-cloud configurations while assuring a safer and more efficient multi-cloud experience.

    Final Thoughts

    The multi-cloud trend is on the rise, offering both opportunities and challenges for organizations. It allows businesses to pick cloud features from multiple providers, easily comply with regional data rules and avoid being tied to just one vendor.

    Different sectors like finance, health care and retail are adjusting their cloud strategies to adopt multi-cloud. Yet, configuring multiple clouds is a tough nut to crack. Diverse policies, different settings across cloud platforms and possible human error complicate multi-cloud configuration.

    However, AI-driven tools can offer a unified dashboard for a better view and management of security across cloud platforms, simplifying multi-cloud setups. Using AI and machine learning for managing clouds leads to quicker threat detection and resolution and helps organizations keep their strategies within legal limits.

    In the multi-cloud discourse, careful configuration supported by AI is a key to receiving multi-cloud benefits while managing the risks efficiently.