How digital solutions increase efficiency in warehouse management

In the evolving landscape of modern business, the significance of robust maintenance, repair and operations (MRO) systems cannot be overstated. Efficient warehouse management helps businesses to operate seamlessly, ensure precision and drive productivity to new heights. In our increasingly digital world, bar coding stands out as a cornerstone technology, revolutionizing warehouses by enabling meticulous data tracking and streamlined workflows.

With this knowledge, A3J Group is focused on using IBM Maximo Application Suite and the Red Hat® Marketplace to help bring inventory solutions to a wider audience. This collaboration brings significant advancements to warehouse management, setting a new standard for efficiency and innovation.

To achieve the maintenance goals of the modern MRO program, these inventory management and tracking solutions address critical facets of inventory management by way of bar code technology.

Bar coding technology in warehouse management

Bar coding plays a critical role in modern warehouse operations.Bar coding technology provides an efficient way to track inventory, manage assets and streamline workflows, while providing resiliency and adaptability. Bar coding provides essential enhancements inkey areas such as:

Accuracy of data: Accurate data is the backbone of effective warehouse management. With barcoding, every item can be tracked meticulously, reducing errors and improving inventory management. This precision is crucial for maintaining stock levels, fulfilling orders and minimizing discrepancies.

Efficiency of data and workers: Barcoding enhances data accuracy and boosts worker efficiency. By automating data capture, workers can process items faster and more accurately. This efficiency translates to quicker turnaround times and higher productivity, ultimately improving the bottom line.

Visibility into who, where, and when of the assets: Visibility is key in warehouse management. Knowing the who, where and when of assets helps ensure accountability and control. Enhanced visibility allows managers to track the movement of items, monitor workflows and optimize resource allocation, leading to better decision-making and operational efficiency.

Auditing and compliance: Traditional systems often lack robust auditing capabilities. Modern solutions provide comprehensive auditing features that enhance control and accountability. With these capabilities, every transaction can be recorded, making it easier to identify issues, conduct audits and maintain compliance.

Implementing digital solutions to minimize disruption

Implementing advanced warehouse management solutions can significantly ease operations during stressful times, such as equipment outages or unexpected order surges. When systems are down or demand spikes, having a robust management system in place helps leaders continue operations with minimal disruption.

During equipment outages, quick decision-making and efficient processes are critical. Advanced solutions help leaders manage these scenarios by providing accurate data, efficient workflows and visibility into inventory levels, which enables swift and informed decisions.

Implementing software accelerators to address warehouse needs

Current trends in warehouse management focus on automation, real-time data tracking and enhanced visibility. By adopting these trends, warehouses can remain competitive, efficient and capable of meeting increasing demands.

IBM and A3J Group offer integrated solutions that address the unique challenges of warehouse management. Available on IBM Red Hat Marketplace, these solutions provide comprehensive features to enhance efficiency, accuracy and visibility.

IBM Maximo Application Suite

IBM® Maximo® Manage offers robust functionality for managing assets, work orders and inventory. Its integration with A3J Group’s solutions enhances its capabilities, providing a comprehensive toolkit for warehouse management.

A3J Group accelerators

A3J Group offers several accelerators that integrate seamlessly with IBM Maximo, providing enhanced functionality tailored to warehouse management needs.

MxPickup

MxPickup is a material pickup solution designed for the busy warehouse manager or employee. It is ideal for projects, special orders and nonstocked items. MxPickup enhances the Maximo receiving process with superior tracking and issuing controls, making it easier to receive large quantities of items and materials.

Unlike traditional systems that force materials to be stored in specific locations, MxPickup allows flexibility in placing and tracking materials anywhere, including warehouse locations, bins, any Maximo location, and freeform staging and delivery locations. Warehouse experts can choose to place or issue a portion or all of the received items, with a complete history of who placed the material and when.

MxPickup also enables mass issue of items, allowing warehouse experts to select records from the application list screen and issue materials directly, streamlining the process and saving valuable time.

A3J Automated Label Printing

The Automated Label Printing solution is designed to notify warehouse personnel proactively when items or materials are received through a printed label report. This report includes information about the received items with bar coded fields for easier scanning. Labels can be automatically fixed to received parts or materials, containing all the necessary information for warehouse operations staff to fulfill requests. The bar codes facilitate quick inventory transactions by using mobile applications, enhancing efficiency and accuracy.

Bringing innovative solutions to warehouse management

The collaboration between IBM and A3J Group on Red Hat Marketplace brings innovative solutions to warehouse management. By using advanced bar coding, data accuracy, efficiency and visibility, warehouses can achieve superior operational performance. Implementing these solutions addresses current challenges and prepares warehouses for future demands, supporting long-term success and competitiveness in the market.

Source: ibm.com

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Optimizing data flexibility and performance with hybrid cloud

As the global data storage market is set to more than triple by 2032, businesses face increasing challenges in managing their growing data. This shift to hybrid cloud solutions is transforming data management, enhancing flexibility and boosting performance across organizations.

By focusing on five key aspects of cloud adoption for optimizing data management—from evolving data strategies to ensuring compliance—businesses can create adaptable, high performing data ecosystems that are primed for AI innovation and future growth.

1. The evolution of data management strategies

Data management is undergoing a significant transformation, especially with the arrival of generative AI. Organizations are increasingly adopting hybrid cloud solutions that blend the strengths of private and public clouds, particularly beneficial in data-intensive sectors and companies embarking on AI strategy to fuel growth. 

A McKinsey & Company study reveals that companies aim to have 60% of their systems in the cloud by 2025, underscoring the importance of flexible cloud strategies. Hybrid cloud solutions address this trend by offering open architectures, combining high performance with scalability. For technical professionals, this shift means to work with systems that can adapt to changing needs without compromising on performance or security. 

2. Seamless deployment and workload portability

One of the key advantages of hybrid cloud solutions is the ability to deploy across any cloud or on-premises environment in minutes. This flexibility is further enhanced by workload portability through advanced technologies like Red Hat® OpenShift®.  

This capability allows organizations to align their infrastructure with both multicloud and hybrid cloud data strategies, ensuring that workloads can be moved or scaled as needed without being locked into a single environment. This adaptability is crucial for enterprises dealing with varying compliance requirements and evolving business needs. 

3. Enhancing AI and analytics with unified data access

 Hybrid cloud architectures are proving instrumental in advancing AI and analytics capabilities. A 2023 Gartner survey reveals that “two out of three enterprises use hybrid cloud to power their AI initiatives”, underscoring its critical role in modern data strategies. By using open formats, these solutions provide unified data access, allowing seamless sharing of data across an organization without the need for extensive migration or restructuring. 

Furthermore, advanced solutions like IBM watsonx.data™ integrate vector database like Milvus, an open-source solution that enables efficient storage and retrieval of high-dimensional vectors. This integration is crucial for AI and machine learning tasks, particularly in fields like natural learning processing and computer vision.  By providing access to a wider pool of trusted data, it enhances the relevance and precision of AI models, accelerating innovation in these areas. 

For data scientists and engineers, these features translate to more efficient data preparation for AI models and applications, leading to improved accuracy and relevance in AI-driven insights and predictions. 

4. Optimizing performance with fit-for-purpose query engines

In the realm of data management, the diverse nature of data workloads demands a flexible approach to query processing. With watsonx.data, multiple fit-for-purpose open query engines are offered such as Presto, Presto C++ and Spark, along with integration capabilities for data warehouse engines like Db2® and Netezza®. This flexibility allows data teams to choose the optimal tool for each task, enhancing both performance and cost-effectiveness. 

For instance, Presto C++ can be used for high-performance, low-latency queries on large datasets, while Spark excels at complex, distributed data processing tasks. The integration with established data warehouse engines ensures compatibility with existing systems and workflows. 

This flexibility is especially valuable when dealing with diverse data types and volumes in modern businesses. By allowing organizations to optimize their data workloads, watsonx.data addresses the challenges of rapidly propagating data across various environments. 

5. Compliance and data governance in a hybrid world

With increasingly strict data regulations, hybrid cloud architectures offer significant advantages in maintaining compliance and robust data governance. A report by FINRA (Financial Industry Regulatory Authority) demonstrates that hybrid cloud solutions can help firms manage cybersecurity, data governance and business continuity more effectively than by using multiple separate cloud services. 

 Unlike pure multicloud setups, which can complicate compliance efforts across different providers, hybrid cloud allows organizations to keep sensitive data on premises or in private clouds while using public cloud resources for less sensitive workloads. IBM watsonx.data enhances this approach with built-in data governance features, such as having a single point of entry and robust access control. This approach supports varied deployment needs and restrictions, making it easier to implement consistent governance policies and meet industry-specific regulatory requirements compromise on security. 

Embracing hybrid cloud for future-ready data management

The adoption of hybrid cloud solutions marks a significant shift in enterprise data management. By offering a balance of flexibility, performance and control, solutions like IBM watsonx.data are enabling businesses to build more resilient, efficient and innovative data ecosystems. 

As data management continues to evolve, using hybrid cloud strategies will be crucial in shaping the future of enterprise data and analytics. With watsonx.data, organizations can confidently navigate this change, using advanced features to unlock the full potential of their data across hybrid environments and be future ready to embrace AI. 

Source: ibm.com

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A clear path to value: Overcome challenges on your FinOps journey

In recent years, cloud adoption services have accelerated, with companies increasingly moving from traditional on-premises hosting to public cloud solutions. However, the rise of hybrid and multi-cloud patterns has led to challenges in optimizing value and controlling cloud expenditure, resulting in a shift from capital to operational expenses.

According to a Gartner report, cloud operational expenses are expected to surpass traditional IT spending, reflecting the ongoing transformation in expenditure patterns by 2025. FinOps is an evolving cloud financial management discipline and cultural practice that aims to maximize business value in hybrid and multi-cloud environments. But without a thorough understanding, adopting FinOps can be challenging. To maximize benefits and realize the potential of FinOps, organizations must forge a clear path and avoid common mistakes.

Enhanced capabilities to drive growth 

FinOps is closely intertwined with DevOps and can represent a radical transformation for many organizations. It necessitates a revised approach to cost and value management, challenging organizations to move beyond their comfort zones and embrace continuous innovation. To achieve this, development teams, product owners, finance, and commercial departments must come together to rethink and reimagine how they collaborate and operate. This collective effort is essential for fostering a culture of innovation and driving meaningful change throughout the organization. 

FinOps enables your organization to control costs and enhance consistency by managing average compute costs per hour, reducing licensing fees, decreasing total ownership costs, and tracking idle instances. It also drives improved outcomes and performance through enhanced visibility and planning, which includes comparing actual spending against forecasts, ensuring that architecture aligns with business and technological objectives, and increasing automation.

These improvements lead to faster decision-making, quicker demand forecasting, and more efficient “go” or “no-go” decision processes for business cases. Also, FinOps helps align business and IT goals, fostering an environment where enterprise goals are interconnected, and business cases are developed with clear, quantifiable benefits. This alignment ensures that both existing and new capabilities are enhanced, supporting strategic growth and innovation. 

Challenges and common mistakes when adopting FinOps

Organizations should develop a phased approach over time instead of attempting to implement everything from day one. Having the right people, processes, and technology in place is essential for validating changes and understanding their impact on the consumption model and usability. 

It’s crucial to lay out a clear journey path by defining the current state, establishing the future state, and devising a transition plan from the current to the future state with a clear execution strategy. To ensure repeatability across different organizations or business units within your organization, it’s essential to establish well-defined design principles and maintain consistency in adoption. Monitoring key performance indicators (KPIs) is essential to track progress effectively.

Many organizations are already considering FinOps approaches today, although often not in the most cost-effective manner. Rather than addressing root causes, they apply temporary fixes that result in ongoing challenges. These temporary fixes include: 

• Periodic Reviews: IT teams convene periodically to address performance issues stemming from sizing or overspending, often in response to complaints from finance teams. However, this reactive approach perpetuates firefighting rather than proactive self-optimization. 
• Architecture Patterns: Regular updates to architectural patterns based on new features and native services from hyperscalers may inadvertently introduce complexity without clear metrics for success. 
• External SMEs: Bringing in external subject matter experts for reviews incurs significant costs and requires effort to bring them up to speed. Relying on this approach contributes to ongoing expenses without sustainable improvements. 

To avoid these pitfalls, it’s crucial to establish well-defined KPIs, benchmarking, and processes for real-time insights and measurable outcomes. 

While some organizations assign FinOps responsibility to a centralized team for monitoring spending and selecting cloud services. This approach can create silos and hinder visibility into planned changes, leading to dissatisfaction and downstream impacts on service delivery. Federating FinOps activities across the organization ensures broader participation and diverse skills, promoting collaboration and avoiding silos. 

The next steps in your FinOps journey

Regardless of where you are in your cloud journey, it is never too late to adopt best practices to make your cloud consumption more predictable. IBM Consulting®, along with Apptio as a product, can help you adopt the right architectural patterns for your unique journey.

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Maximizing business outcomes and scaling AI adoption with a Hybrid by design approach

For established businesses, the debate is settled: a hybrid cloud approach is the right strategic choice.

However, while embracing hybrid cloud might be intrinsic, clients continually seek to derive business value and higher return on investment (ROI) from their investments. According to a study conducted by HFS Research in partnership with IBM Consulting, only 25% of surveyed enterprises have reported solid ROI on business outcomes from their cloud transformation efforts.

The lack of ROI progress can be attributed to several factors, including slow adoption, unrealized use cases and unaddressed cloud sprawl. This is exacerbated by the increasing challenges of platform scaling, skilling, cybersecurity and the explosion of data volumes.

 

Businesses aiming to derive value from business outcomes through their cloud investments and harness the potential of artificial intelligence (AI) must adopt an intentional approach.

At IBM, we provide clear guidance for navigating these challenges and achieving better business outcomes. Adopting a hybrid by design strategy allows organizations to align architectural choices with business priorities across technology, platforms, processes and people, resulting in significantly enhanced outcomes and a higher ROI.

The latest paper from IBM, titled “Maximize the value of hybrid cloud in the generative AI era,” offers clarity for businesses seeking to optimize their hybrid cloud architecture and amplify the impact of generative AI (gen AI).

What does a Hybrid by design approach involve?

Many organizations have adopted cloud in isolated areas, seeking immediate benefits, but inconsistently applying the technology throughout their operations. This complexity and inconsistency increase costs and hinder businesses from meeting demands, goals and outcomes. We view this as a default approach, which characterizes most companies today.

Businesses must recalibrate transformation programs to align more closely with business imperatives and outcomes. They must also recognize how gen AI can amplify the value of hybrid cloud and accelerate digital transformation.

A hybrid by design approach intentionally structures your hybrid, multicloud IT estate to achieve key business priorities and maximize ROI. Organizations can shift from technology dictating business limitations to purposefully built architectures ready for evolving technology landscapes, new business and customer demands, and emerging processes and skill requirements. Driven by an organization’s key business objectives, intentional and consistent architectural decisions enable organizations to fully use hybrid cloud and gen AI to drive business outcomes. 

IBM built a codified framework to help clients strategically focus on decisions that drive the most outcomes for their business, promoting a hybrid by design approach. This framework facilitates the translation of business priorities into key architectural decisions.

We implement this framework by aligning business objectives with a set of decision points across 3 domains: product native by design, technology by design and integration by design. These domains also encompass various design points. Each enterprise situation requires different levels of capabilities to address and achieve its business objectives. This value framework aids in capturing these decision points.

In an AI-centric world, why does this matter for enterprises? 

Gen AI plays a critical role in shaping the digital enterprise, helping to maximize business returns on IT investments. However, wide-scale adoption and deployment of gen AI present challenges. Enterprises must manage vast volumes of data, significant computing power, advanced security architecture across distributed environments and ensure rapid scalability.

An intentional and consistent hybrid architecture design helps enterprises to overcome these challenges, facilitating the successful adoption and scalability of gen AI capabilities to meet business needs.

Current pilots and deployments prove that a hybrid by design approach is required to scale the adoption of gen AI to meet business needs. Gen AI accelerates the execution and value of hybrid cloud by providing enhanced automation with observability, improved cost optimization with automated insights, and heightened security with compliance across the hybrid platform. 

Applying intentional architecture decisions based on business needs amplifies the value of hybrid cloud and AI, driving business outcomes.

Source: ibm.com

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How to become an AI+ enterprise

We have all been witnessing the transformative power of generative artificial intelligence (AI), with the promise to reshape all aspects of human society and commerce while companies simultaneously grapple with acute business imperatives. In 2024, companies confront significant disruption, requiring them to redefine labor productivity to prevent unrealized revenue, safeguard the software supply chain from attacks, and embed sustainability into operations to maintain competitiveness.    

AI is at a turning point, driving exponential advancements in an organization’s prosperity and growth. Generative AI (gen AI) introduces transformative innovation to all aspects of a business; from the front to the back office, through ongoing technology modernization, and into new product and service development.

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. This requires a holistic enterprise transformation. We refer to this transformation as becoming an AI+ enterprise.

Become an AI+ enterprise

An AI+ enterprise innovates with AI as the primary focus, understands that AI is fundamental to the entire business, and recognizes that AI impacts all aspects of the business: product innovation, business operations, technical operations, as well as people and culture. 

Figure 1: Transforming into an AI+ enterprise is at the core of what our team at IBM does

An AI+ enterprise integrates AI as a first-class function across the business. They understand that if one area of the business adopts AI while others lag or resist it (due to valid concerns), this exacerbates issues like Shadow AI, making it challenging to implement a holistic strategy.

Benefits of being an AI+ enterprise

The vast business opportunity with AI, forecasted by Gartner to bring USD 3 to 4 trillion in economic benefits to the global economy across industries, prompts companies to recognize the investment required to use AI effectively, and are demanding a dramatic return on investment (ROI) before investing in an AI use case.

By becoming an AI+ enterprise, clients can realize the ROI not only for the AI use case but also for improving the related business and technical capabilities required to deliver AI use cases into production at scale.

Figure 2: ROI potential by transforming into an AI+ enterprise

Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 times higher ROI.

IBM has developed AI+ Enterprise Transformation to equip clients with the business and technical strategy, architectures, roadmaps and hands-on experience to become an AI+ enterprise.

AI+ Enterprise Transformation

With IBM’s depth in AI and hybrid cloud, we have discovered that companies becoming an AI+ enterprise leads to faster realization of business results. What’s exciting is that many clients we work with are already excelling in AI, and by adopting AI+ Enterprise Transformation, they uncover activities that accelerate their business growth through running AI in production at scale.

Figure 3: AI+ enterprise domain overview

Figure 3 summarizes AI+ Enterprise Transformation, highlighting the multiple domains across the organization that an AI+ enterprise needs to address to bring AI to production at scale: 

• Key use cases that enhance business performance
• Responsible AI technology to implement these use cases
• A well-designed data foundation to fuel AI initiatives
• Application innovation to deliver AI experiences, and application modernization to handle AI requests
• Hybrid cloud platform, including integrations, to run AI, data and applications as required
• Building pipelines for continuous updates, enhancements and fixes of apps, data and AI, with deployment protection through scanning and guardrails
• Day-2 operations using AI to predict (and repair) failures before they happen, fostering a culture where employees embrace AI’s value instead of fearing replacement.
• Security, governance, risk and compliance mechanisms are essential not only for governing AI but also for managing the IT estate running AI, providing evidence for regulatory compliance.

Start with the use cases

The most important step for an AI+ enterprise is identifying transformative use cases. After experimenting with various options, the enterprise selects high-value use cases that show faster ROI. It then delivers them into production across the IT landscape, laying the groundwork for additional use cases and fostering ongoing innovation.

Figure 4 illustrates the AI+ use case funnel that an AI+ enterprise adopts to systematically and rigorously transform use cases into broad-reaching AI enterprise solutions that deliver high ROI, aligned across delivery, operations, security and governance.  

Figure 4: AI+ use case funnel to deliver AI solutions to production at scale

Harness the right AI technology

After identifying use cases, the next step for an AI+ enterprise is choosing the appropriate AI technology and architecture. Often, this decision is made too quickly. It should be approached thoughtfully to help ensure suitability.

Consider the following:

• Do you need a public foundation model?
• Should you build your own? If so, where will it run?
• Should you use a retrieval augmented generation (RAG) model by pairing your data with a public foundation model?
• Do you use gen AI out of the box? How can you master prompt engineering? When should you prompt-tune or fine-tune?
• Which approach requires on-premises GPUs?
• Where do you harness gen AI vs. predictive AI vs. AI orchestration? For instance, when automating password change requests, do you need a 175 billion parameter public foundation model, a fine-tuned smaller model, or AI orchestration to call APIs?

As you pinpoint your AI technology, your decision impacts the other domains of AI+ Enterprise Transformation. For more insights, keep reading.

Deliver a strong data foundation

AI relies fundamentally on data. An AI+ enterprise ensures that the data used for AI is trustworthy, transparent, and has clear lineage and efficacy. Otherwise, the risks become too significant. We have all seen examples of companies delivering AI built on weak data foundations, leading to undesirable outcomes. These outcomes typically fall into one of three categories, none of which are desirable:

• Not useful: Customers remain unimpressed with your results. For example, stale data, hallucinations and more.
• Embarrassing: Offensive output emerges based on the data used in AI. For example, hate, abuse, profanity and bias.
• Financial/criminal: Violations of existing and emerging data and AI regulations. For example, copyright laws, the European Union’s Artificial Intelligence act, Digital Operational Resilience Act (DORA), data sovereignty laws and more.

An AI+ enterprise empowers architects to confidently source, prepare, transform, protect and deliver data to the required locations for AI. 

Innovate and modernize applications

Innovating with new AI-based applications to deliver outstanding experiences is essential. It’s also crucial to modernize existing applications that interact with AI. if an AI-powered human resources assistant offers to perform actions for employees, it is vital to ensure that the application being called can handle increased traffic. Frequently, these actions involve calling APIs to legacy applications running on architectures unfit for handling the sudden demands of the AI assistant. This often leads to a disappointing experience due to slow response times.

An AI+ enterprise excels in delivering innovative AI applications to its customers and modernizing existing applications to meet the new demands AI presents.

Hybrid cloud platform

Once AI, data and applications are understood, the discussion naturally shifts to “Where do we run this solution?” In our experience, the answer depends on many factors, which can change over time, requiring a flexible platform.

Adopting an open technologies-based hybrid cloud platform enables an AI+ enterprise to make informed decisions without limiting its business.

Figure 5: AI+ enterprise hybrid cloud architecture

As shown in figure 5, a hybrid cloud architecture enhances the entire business in various ways:

• Flexibility in where to train and tune large models
• Flexibility in where to train and tune smaller models
• Where to perform inferencing on-premises, in private clouds or even on edge devices
• Applications using RAG architectures experience less latency when running close to the models
• Data sovereignty laws limit data relocation, so having the ability to move AI and applications to the data is essential
• Creating an AI+ fabric that provides interconnectivity across the IT and business landscape

Continuously build and enhance apps, data and AI

When AI, data and applications run across a well-designed hybrid cloud platform, an AI+ enterprise builds pipelines and toolchains to continuously enhance and deliver with full automation. For example:

• Platform pipelines provision and update infrastructure and the software running on them using Terraform and Ansible
• Application pipelines integrate and deliver code updates for both innovative applications delivering AI experiences and modernized applications being acted upon by AI digital workers
• Data pipelines process incoming data to help ensure that the data sources used by AI are current and valid

AI pipelines pull in data, retrain and augment as needed based on metrics such as drift and accuracy

An AI+ enterprise knows how to continually enhance applications, data and AI models throughout their lifecycle, helping to ensure that only trusted and approved AI functions go live.

Operations

Incidents occur, even in an AI-first world. However, an AI+ enterprise uses AI not only to delight customers but also to solve IT problems. With the right tools, an AI+ enterprise can significantly increase employee productivity. Examples include:

• Detecting and correcting when an application is being constrained and automating capacity increases
• Providing visibility and insight across the enterprise to enable higher levels of automation and achieve predictive maintenance
• Reducing security threats by closing security gaps before they arise. For example, by using compliance control scanning of terraform templates to fail provisioning if controls are not met.

An AI+ enterprise also recognizes that alongside the necessary tools, fostering a culture that embraces AI and trains talent is crucial. This culture encourages experimentation and expertise growth. It requires people trained to harness, evaluate, and accelerate AI, rather than fearing it.

Secure and govern AI on the hybrid cloud platform

To deploy AI, particularly gen AI, at scale in production, organizations must establish a secure and governed environment. The scale and impact of next-generation AI emphasize the importance of governance and risk controls. An AI+ enterprise mitigates potential harm by implementing robust measures to secure, monitor and explain AI models, as well as monitoring governance, risk and compliance controls across the hybrid cloud environment.

Pairing existing cloud governance with new AI governance controls is essential, requiring continual focus to comply with emerging regulatory changes, such as NIST AI Risk Management Framework, the European Union’s Artificial Intelligence act, ISO/IEC 42001 AI Management, and ISO/IEC 23894 AI Risk Management.

Get started today

IBM wants to work with you to become an AI+ enterprise, providing impactful use cases, strategies, architectures and hands-on experiences to:

• Understand your current trajectory
• Shape your AI+ enterprise strategy with points of view and models
• Co-create target state solutions and architectures
• Specify governance and risk posture
• Customize a business value case
• Co-develop key near-term engineering sprints to prove the value of your AI+ enterprise strategy

Future articles will delve deeper into each AI+ domain, showcasing IBM’s perspective through architectures, demos and strategies.

Source: ibm.com

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Decoding the future: unravelling the intricacies of Hybrid Cloud Mesh versus service mesh

Hybrid Cloud Mesh, which is generally available now, is revolutionizing application connectivity across hybrid multicloud environments. Let’s draw a comparison between Hybrid Cloud Mesh and a typical service mesh to better understand the nuances of these essential components in the realm of modern enterprise connectivity. This comparison deserves merit because both the solutions are focused on application-centric connectivity albeit in a different manner.

Before we delve into the comparison, let’s briefly revisit the concept of Hybrid Cloud Mesh and a typical service mesh.

Hybrid Cloud Mesh

Hybrid Cloud Mesh is a modern application-centric connectivity solution that is simple, secure, scalable and seamless. It creates a secure network overlay for applications distributed across cloud, edge and on-prem and holistically tackles the challenges posed by distribution of services across hybrid multicloud. 

Service mesh

A service mesh is a configurable infrastructure layer that manages all connectivity requirements between microservices. It manages service-to-service communication, providing essential functionalities such as service discovery, load balancing, encryption and authentication. 

Language libraries for connectivity have partial and inconsistent implementation of traffic management features and are difficult to maintain and upgrade. A service mesh eliminates such libraries and allows services to focus on their business logic and communicate with other services without adding any connectivity logic in situ. 

Hybrid Cloud Mesh versus service mesh: a comparative analysis 

1. Scope of connectivity

• Hybrid Cloud Mesh: Goes beyond microservices within a containerized application, extending connectivity to applications regardless whether they’re form-factor deployed across on-premises, public cloud and private cloud infrastructure. Its scope encompasses a broader range of deployment scenarios. 
• Service mesh: Primarily focuses on managing communication between microservices within a containerized environment. Although many service meshes have started looking outward, enabling multi-cluster any-to-any connectivity. 

2. Multicloud connectivity

• Hybrid Cloud Mesh: Seamlessly connects applications across hybrid multicloud environments, offering a unified solution for organizations with diverse cloud infrastructures. 
• Service mesh: Typically designed for applications deployed within a specific cloud or on-premises environment. Many service meshes have expanded scope to multicloud connectivity, but they are not fully optimized for it.  

3. Traffic engineering capabilities

• Hybrid Cloud Mesh: Utilizes waypoints to support path optimization for cost, latency, bandwidth and others,. enhancing application performance and security. 
• Service mesh: No traffic engineering capabilities. Primarily focuses on internal traffic management within the microservices architecture. 

4. Connectivity intent expression

• Hybrid Cloud Mesh: Allows users to express connectivity intent through the UI or CLI, providing an intuitive, user-friendly experience with minimal learning curve.  
• Service mesh: Requires users to implement complex communication patterns in the sidecar proxy using configuration files. Service mesh operations entail complexity and demand a substantial learning curve. The expert team responsible for managing the service mesh must consistently invest time and effort to effectively utilize and maintain the service mesh. Due to steep learning curve and tooling required (such as integration with CI/CD pipeline or day 0 to day 2 automation), service meshes can be adopted only after customers gain a certain scale to make the investment worthwhile.   

5. Management and control plane

• Hybrid Cloud Mesh: Employs a centralized SaaS-based management and control plane, enhancing ease of use and providing observability. Users interact with the mesh manager through a user-friendly UI or CLI. 
• Service mesh: Often utilizes decentralized management, with control planes distributed across the microservices, requiring coordination for effective administration. 

6. Integration with gateways

• Hybrid Cloud Mesh: Integrates with various gateways, promoting adaptability to diverse use cases and future-ready for upcoming gateway technologies. 
• Service mesh: Primarily relies on sidecar proxies for communication between microservices within the same cluster. Typically features on the proxy are extended to meet requirements.  

7. Application discovery

• Hybrid Cloud Mesh: Mesh manager continuously discovers and updates multicloud deployment infrastructure, automating the discovery of deployed applications and services. 
• Service mesh: Typically relies on service registration and discovery mechanisms within the containerized environment. 

8. Dynamic network maintenance

• Hybrid Cloud Mesh: Automatically adapts to dynamic changes in workload placement or environment, enabling resilient and reliable connectivity at scale without manual intervention. 
• Service mesh: Usually, the day 2 burden to manage a service mesh connecting applications across multicloud is huge due to complexity of operations required to manage dynamic infrastructure changes. It requires manual adjustments to accommodate changes in microservices deployed in a multicloud environment. There’s significant effort in keeping it running such as—upgrades, security fixes and others apart from infrastructure changes. This takes away a lot of time and very little time is left for implementing new features.  

9. Infrastructure overhead

• Hybrid Cloud Mesh: Data plane is composed of a limited number of edge-gateways and waypoints.
• Service mesh: Significant overhead due to sidecar proxy architecture which requires 1 sidecar-proxy for every workload.  

10. Multitenancy

• Hybrid Cloud Mesh: Offers robust multitenancy; moreover, subtenants can be created to maintain separation between different departments or verticals within an organization. 
• Service mesh: May lack the capability to accommodate multitenancy or a subtenant architecture. Few customers may create a separate service mesh per cluster to keep the tenants separate. Hence, they must deploy and manage their own gateways to connect various service meshes.  

Take the next step with Hybrid Cloud Mesh

We are excited to showcase a tech preview of Hybrid Cloud Mesh supporting the use of Red Hat® Service Interconnect gateways simplifying application connectivity and security across platforms, clusters and clouds. Red Hat Service Interconnect, announced 23 May 2023 at Red Hat Summit, creates connections between services, applications and workloads across hybrid necessary environments. 

We’re just getting started on our journey building comprehensive hybrid multicloud automation solutions for the enterprise. Hybrid Cloud Mesh is not just a network solution; it’s engineered to be a transformative force that empowers businesses to derive maximum value from modern application architecture, enabling hybrid cloud adoption and revolutionizing how multicloud environments are utilized. We hope you join us on the journey. 

Source: ibm.com

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How IBM and AWS are partnering to deliver the promise of AI for business

In today’s digital age where data stands as a prized asset, generative AI serves as the transformative tool to mine its potential. According to a survey by the MIT Sloan Management Review, nearly 85% of executives believe generative AI will enable their companies to obtain or sustain a competitive advantage. The global AI market is projected to grow to USD 190 billion by 2025, increasing at a compound annual growth rate (CAGR) of 36.62% from 2022, according to Markets and Markets. Businesses globally recognize the power of generative AI and are eager to harness data and AI for unmatched growth, sustainable operations, streamlining and pioneering innovation. In this quest, IBM and AWS have forged a strategic alliance, aiming to transition AI’s business potential from mere talk to tangible action.

Adopting AI in business at scale is not without its challenges, including data privacy concerns, integration complexities and the need for skilled personnel. Scaling AI in business presents unique challenges:

1. Data accessibility: Fragmented and siloed data stifle advancement. Gartner highlights that businesses lose an estimated USD 15 million annually due to inadequate data access.

2. Integration and financial constraints: Merging AI with current systems is intricate. Forrester indicates that 40% of companies face this obstacle. Concurrently, McKinsey points out high expenses limit AI integration in 23% of organizations.

3. Ethical and regulatory barriers: Upholding AI ethics is pivotal. A significant 34% of companies express concerns over fairness, with regulatory hurdles intensifying the landscape.

The AWS-IBM partnership is a symphony of strengths

The collaboration between IBM and AWS is more than just a tactical alliance; it’s a symphony of strengths. IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. AWS, on the other hand, provides robust, scalable cloud infrastructure. By combining IBM’s advanced data and AI capabilities powered by Watsonx platform with AWS’s unparalleled cloud services, the partnership aims to create an ecosystem where businesses can seamlessly integrate AI into their operations.

Real-world Business Solutions

The real value of any technology is measured by its impact on real-world problems. IBM and AWS partnership focuses on delivering solutions in areas like:

Supply chain optimization with AI-infused Planning Analytics

IBM Planning Analytics on AWS offers a powerful platform for supply chain optimization, blending IBM’s analytics expertise with AWS’s cloud capabilities. One of the largest children clothing retailer in the US utilizes this solution to streamline its complex supply chain. Real-time data analytics helps in quick decision-making, while advanced forecasting algorithms predict product demand across diverse locations. The retailer uses these insights to optimize inventory levels, reduce costs and enhance efficiency. AWS’s scalable infrastructure allows for rapid, large-scale implementation, ensuring agility and data security. Overall, this partnership enables the retailer to make data-driven decisions, improve supply chain efficiency and ultimately boost customer satisfaction, all in a secure and scalable cloud environment.

Infuses AI to transform business operations

DB2 PureScale on AWS provides a scalable and resilient database solution that’s well-suited for AI-driven applications. By taking advantage of AWS’s robust cloud infrastructure, PureScale ensures high availability and fault tolerance, critical for businesses operating around the clock. A leading insurance player in Japan leverages this technology to infuse AI into their operations. Real-time analytics on customer data — made possible by DB2’s high-speed processing on AWS — allows the company to offer personalized insurance packages. AI algorithms sift through large datasets to identify fraud risks and streamline claims processing, improving both efficiency and customer satisfaction. AWS’s secure and scalable environment ensures data integrity while providing the computational power needed for advanced analytics. Thus, DB2 PureScale on AWS equips this insurance company to innovate and make data-driven decisions rapidly, maintaining a competitive edge in a saturated market.

Modernizing data warehouse with IBM watsonx.data

Modernizing a data warehouse with IBM watsonx.data on AWS offers businesses a transformative approach to managing data across various sources and formats. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability. By migrating to watsonx.data on AWS, companies can break down data silos and enable real-time analytics, which is crucial for timely decision-making. One of largest asset management company has executed a pilot using machine learning capabilities to further allow for predictive analytics, uncovering trends and patterns that traditional methods might miss. One of the standout features for this company is its seamless integration with existing IT infrastructure, reducing both costs and the complexity of migrating from legacy systems. Whether you’re looking to streamline operations, improve customer experiences, or unlock new revenue streams, IBM watsonx.data on AWS lays the foundation for a smarter, more agile approach to data management and analytics.

As AI continues to evolve, this partnership is committed to staying ahead of the curve by continuously updating its offerings, investing in joint development and providing businesses with tools that are both cutting-edge and practical.

The IBM-AWS partnership is not just a win-win for the companies involved; it’s a win for businesses across sectors. By combining IBM’s prowess in data analytics and AI with AWS’s robust cloud infrastructure, the alliance is breaking down barriers to AI adoption, offering scalable solutions, and enabling businesses to leverage AI for tangible results.

Get ready to harness the power of AI for your business

Explore how the IBM-AWS partnership can offer you tailored solutions that drive results. Join us at AWS re:Invent 2023 from November 27 to December 1 in Las Vegas, Nevada. At booth #930, IBM will spotlight its advancements in AI, demonstrating how we assist clients to scale AI workloads using our comprehensive generative AI stack swiftly and responsibly. This event offers a firsthand look into IBM’s transformative solutions that are reshaping industries. Engage with our experts, partake in live demos, and explore tailor-made solutions for your business needs.

Source: ibm.com

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IBM Hybrid Cloud Mesh: Reimagining multicloud networking with applications taking center stage

Private clouds, public clouds, SaaS, on-premises and edge—as organizations leverage a more distributed, robust cloud-based strategy, they can also face more significant management and compliance challenges. This shift to the cloud may have, in many ways, left the traditional enterprise network stranded—no longer transporting the bulk of the enterprise network traffic, which now floats between the clouds and over the public internet.

The probable result of this widely dispersed, distributed world? Application performance is no longer guaranteed, security could be affected and the skills needed for one cloud are not always easily transferable or available in another.

At the same time, for many companies, their applications are their business. Regardless of the cloud provider or where users sit, these applications require dependable, secured connectivity. That’s why it’s time for a new approach, driven by the applications themselves.

The new network paradigm: Application-centric connectivity

Yesterday, we launched IBM Hybrid Cloud Mesh, a multicloud networking solution. When it is generally available later this year, this new SaaS product is designed to allow organizations to establish simple and secured application-centric connectivity. This is engineered for network managers to seamlessly manage and scale network applications across a wide variety of public and private clouds, edge and on-premises.

This application-first approach is the next important networking paradigm. It’s also an evolution from the current “fat pipes” method (which doesn’t differentiate between applications) to one that aligns the network to the needs of the business, its users, and its developers, their CI/CD pipeline and DevOps cycles. When it’s time to configure new cloud networks and connect applications, our approach is designed to turn weeks into hours and move from manual to automated processes, with robust visibility into performance and minimized risk of IAM misconfigurations.

What this means for your networks

Given the complexity of today’s networking environments, we purposely designed Hybrid Cloud Mesh around four basic attributes:

◉ Simple: You’ll find a streamlined deployment process that enables automated workflows and simple network configuration that can be managed via CLI or an intuitive UI.

◉ Secured: Critical for today’s business, you’ll find zero-trust architecture and end-to-end encryption, along with segmentation and micro-segmentation.

◉ Scalable: Scalable to large enterprise environments, you also have the ability to scale resources based on demand.

◉ Seamless: Designed to reduce the barriers between clouds and teams, you can manage services across clouds, with on-demand, intent-driven application-centric connectivity.

Components of Hybrid Cloud Mesh

Two main architecture components are key to how the product is designed to work.

◉ Gateways, which act as virtual routers and connectors. These are centrally managed through Mesh Manager and deployed both in the cloud and on customer premises.

◉ The Mesh Manager provides the centralized management and control plane for Hybrid Cloud Mesh through a SaaS portal.

Both Gateways and the Mesh Manager are designed to communicate through a set of open, secured APIs and interfaces.

Hybrid Cloud Mesh is engineered to complement existing SD-WANs, service mesh and multicloud networking solutions. You’ll also find crucial benefits that can include the following:

◉ Auto-discovery of cloud infrastructure and applications using the Gateways described above, deployed next to applications both in the cloud and on customer premises.

◉ A single centralized management and control plane for your multicloud deployments and sites through Mesh Manager.

◉ Addressing silos between CloudOps and DevOps through automated workflows and a shared overlay, enabling rapid application deployment and optimization.

◉ A “network follows the application” paradigm that establishes application-level connectivity to streamline application migration to the cloud and moves the network wherever the application is placed.

◉ Zero-trust architecture that seamlessly enables end-to-end encryption across the network from application component to application component.

◉ Application network optimization with granular visibility and control of application-level connectivity. This is done by streamlining telemetry, root cause analysis and reconfiguration. It provides an intuitive overlay to help address performance issues and generate traffic engineering recommendations.

Source: ibm.com

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5 misconceptions about cloud data warehouses

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery. Companies are shifting their investments to cloud software and reducing their spend on legacy infrastructure. In 2021, cloud databases accounted for 85% of the market growth in databases. These developments have accelerated the adoption of hybrid-cloud data warehousing; industry analysts estimate that almost 50% of enterprise data has been moved to the cloud.

What is holding back the other 50% of datasets on-premises? Based on our experience speaking with CTOs and IT leaders in large enterprises, we have identified the most common misconceptions about cloud data warehouses that cause companies to hesitate to move to the cloud.

Misconception 1: Cloud data warehouses are more expensive

When considering moving data warehouses from on-premises to the cloud, companies often get sticker shock at the total cost of ownership. However, a more detailed analysis is needed to make an informed decision. Traditional on-premises warehouses require a significant initial capital investment and ongoing support fees, as well as additional expenses for managing the enterprise infrastructure. In contrast, cloud data warehouses may have a higher annual subscription fee, but they incorporate the upfront investment and additional ongoing overhead. Cloud warehouses also provide customers with elastic scalability, cheaper storage, savings on maintenance and upgrade costs, and cost transparency, which allows customers to have greater control over their warehousing costs. Industry analysts estimate that organizations that implement best practices around cloud cost controls and cloud migration see an average savings of 21% when using a public cloud and a 13x revenue growth rate for adopters of hybrid-cloud through end-to-end reinvention.

Misconception 2: Cloud data warehouses do not provide the same level of security and compliance as on-premises warehouses

Companies in highly regulated industries such as finance, insurance, transportation and manufacturing have a complex set of compliance requirements for their data, often leading to an additional layer of complexity when it comes to migrating data to the cloud. In addition, companies have complex data security requirements. However, over the past decade, a vast array of compliance and security standards, such as SOC2, PCI, HIPAA, and GDPR, have been introduced, and met by cloud providers. The rise of sovereign clouds and industry specific clouds are addressing the concerns of governmental and industry specific regulatory requirements. In addition, warehouse providers take on the responsibility of patching and securing the cloud data warehouse, to ensure that business users stay compliant with the regulations as they evolve.

Misconception 3: All data warehouse migrations are the same, irrespective of vendors

While migrating to the cloud, CTOs often feel the need to revamp and “modernize” their entire technology stack – including moving to a new cloud data warehouse vendor. However, a successful migration usually requires multiple rounds of data replication, query optimization, application re-architecture and retraining of DBAs and architects.

To mitigate these complexities, organizations should evaluate whether a hybrid-cloud version of their existing data warehouse vendor can satisfy their use cases, before considering a move to a different platform. This approach has several benefits, such as streamlined migration of data from on-premises to the cloud, reduced query tuning requirements and continuity in SRE tooling, automations, and personnel. It also enables organizations to create a decentralized hybrid-cloud data architecture where workloads can be distributed across on-prem and cloud.

Misconception 4: Migration to cloud data warehouses needs to be 0% or 100%

Companies undergoing cloud migrations often feel pressure to migrate everything to the cloud to justify the investment of the migration. However, different workloads may be better suited for different deployment environments. With a hybrid-cloud approach to data management, companies can choose where to run specific workloads, while maintaining control over costs and workload management. It allows companies to take advantage of the benefits of the cloud, such as scale and elasticity, while also retaining the control and security of sensitive workloads in-house. For example, Marriott International built a decentralized hybrid-cloud data architecture while migrating from their legacy analytics appliances, and saw a nearly 90% increase in performance. This enabled data-driven analytics at scale across the organization.

Misconception 5: Cloud data warehouses reduce control over your deployment

Some DBAs believe that cloud data warehouses lack the control and flexibility of on-prem data warehouses, making it harder to respond to security threats, performance issues or disasters. In reality, cloud data warehouses have evolved to provide the same control maturity as on-prem warehouses. Cloud warehouses also provide a host of additional capabilities such as failover to different data centers, automated backup and restore, high availability, and advanced security and alerting measures. Organizations looking to increase adoption of ML are turning to cloud data warehouses that support new, open data formats to catalog, ingest, and query unstructured data types. This functionality provides access to data by storing it in an open format, increasing flexibility for data exploration and ML modeling used by data scientists, facilitating governed data use of unstructured data, improving collaboration, and reducing data silos with simplified data lake integration.

Additionally, some DBAs worry that moving to the cloud reduces the need for their expertise and skillset. However, in reality, cloud data warehouses only automate the operational management of data warehousing such as scaling, reliability and backups, freeing DBAs to work on high value tasks such as warehouse design, performance tuning and ecosystem integrations.

By addressing these five misconceptions of cloud data warehouses and understanding the nuances, advantages, trade-offs and total cost ownership of both delivery models, organizations can make more informed decisions about their hybrid-cloud data warehousing strategy and unlock the value of all their data.

Getting started with a cloud data warehouse

At IBM we believe in making analytics secure, collaborative and price-performant across all deployments, whether running in the cloud, hybrid, or on-premises. For those considering a hybrid or cloud-first strategy, our data warehousing SaaS offerings including IBM Db2 Warehouse and Netezza Performance Server, are available across AWS, Microsoft Azure, and IBM Cloud and are designed to provide customers with the availability, elastic scaling, governance, and security required for SLA-backed, mission critical analytics.

When it comes to moving workloads to the cloud, IBM’s Expert Labs migration services ensure 100% workload compatibility between on-premises workloads and SaaS solutions.

No matter where you are in your journey to cloud, our experts are here to help customize the right approach to fit your needs. See how you can get started with your analytics journey to hybrid cloud by contacting an IBM database expert today.

Source: ibm.com

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A catalyst for security transformation: Modern security for hybrid cloud

Today it takes an average of 252 days for an organization to identify and contain a breach across hybrid cloud environments, while ransomware attacks occur every 11 seconds. This proves that traditional security can no longer keep up with our modern world. As most big businesses move to be multicloud, SaaS-heavy hybrid cloud users, enterprises must raise cyber awareness to protect a dramatically expanded security attack surface.

Security is no longer an afterthought and must be embedded in everything we do. In the increased complex hybrid cloud environment, how do we secure end-to-end and obtain a holistic security posture that is adequate to support business functions? It’s time to think of security at the enterprise level as industries shift to a new, Security First archetype: Transformative Security Programs.

Modernize security: Quality, velocity, affordability

80% or more of executives struggle to engage information security and operations disciplines early enough to prevent rework or security incidents. To incorporate a Security First mindset, companies should consider policy compliance, security regulations and asset protection before they design their cloud strategy. In an effort to prevent costly reworks, companies should also address complexities early on in the strategy and design phase, rather than waiting to deal with security later.

A modernized security operation and management system should avoid the antiquated approach of security as a stand-alone function. Instead, run it as a true integral business entity and invest accordingly to drive cyber resiliency and the quality, velocity and affordability needed to protect digital assets. With a Security First approach, not only will your vulnerabilities be subsidized through secure architecture design and early, modern security testing, but your enterprise can also leverage automation, artificial intelligence (AI) and machine learning (ML) to shorten MTTR and supplement cyber talent shortages.

Hybrid cloud mastery demands a whole-team approach to security

With 82% of security breaches caused by human error, a modern security program should include situational awareness with a single pane of glass and advanced cyber training such as simulated cybersecurity attack and response exercises. These training designs incorporate the intensity of countering attacks with fun factors to best educate and relate security to your team’s day-to-day activities. Modern security awareness and education encourages people to exercise critical thinking and promote good cyber behavior for normal operations as well as disrupted, under-attack operations.

Though improving cybersecurity and reducing security risks are critical for the successful execution of digital initiatives in cloud portfolios, they’re not always directly linked in execution. Rather than merely running a security modernization program in parallel with a cloud adoption program, aim to explicitly integrate roadmaps and embed security into the hybrid cloud journey—with enterprise security and hybrid cloud security playing on the same team.

As an example, no matter who is leading a data fabric initiative, designing and implementing a secure data fabric requires the engagement of the whole team. Engaging the whole team means security becomes an explicitly shared responsibility, and this approach is easier and more effective when it’s grounded in a broader Security First and Security Always culture.

3 steps for overcoming the security challenge to hybrid cloud mastery

Step 1: Harmonize the security posture across the estate

Think holistically. Security posture is the sum of security policies, capabilities, and procedures across the various components of a hybrid cloud estate. When we push the “start” button and ask the specific cloud or components to interoperate in a productive way, the lack of harmony among security postures can expose serious problems. Harmonizing the security posture across the entire hybrid cloud builds a fabric of protection that helps keep “bad guys” from entering through the weakest link. Enterprise security management from the top down allows enterprises to achieve consistency.

Step 2: Create visibility through a single pane of glass

If hackers really want to attack you, they will touch your network at different app ports, and generate a lot of network activity. If your data is siloed, you might not notice this surge and could miss a leading indicator of a potential security attack.

Enclaves of data (apps, network, security) should be fused into a data lake to allow accurate security insights across the entire cloud estate. Your enterprise can impose AI or machine learning capabilities into the data lake, and IT Ops data and AI Ops data can be tools for making better business decisions. This aggregated visibility capability, known as a “single pane of glass,” helps enable detection, assessment and resolution of security anomalies with high velocity.

Remember, in a hybrid cloud ecosystem, security is more than just the security function: it’s central to your business. You need the rights to harvest these data through good terms and conditions with your cloud provider.

Step 3: Leverage AI to predict vulnerabilities

The single pane of glass is more powerful if we can also make better, faster sense of what we’re seeing. AI, machine learning and automation can ingest high volumes of complex security data, enabling near-real-time threat detection and prediction. AI tools can be “trained” to detect cyberattack patterns that have preceded incidents in the past. When those patterns recur, AI can trigger alerts or even provide actions for self-healing well before a human operator could detect and act upon a potential incident.

With security talent challenges and 3.5 million available security jobs, leveraging advanced tech automation and AI machine learning allows enterprises to find new ways to put security first with skill and velocity.

It’s time to embrace the transformational power of security to keep up with the demands of the modern world. To master hybrid cloud, you need to develop a unified security program that steers business initiatives, optimizes security resources and transforms your operating culture to be Security First.

Source: ibm.com

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