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For CIOs, CISOs and risk leaders, the concern is not the size of the technology estate alone. The harder issue is fragmentation of control.

A cloud environment may look secure when viewed on its own. Risk builds when data, identities, applications, APIs and automation cross environments without a consistent security and governance model. AI makes that gap harder to carry because it relies on the foundations that are most difficult to manage across a distributed estate: data quality, access control, lineage, policy enforcement, observability, and response.

The real multi-cloud risk is control drift

Most enterprises have invested heavily in cloud security. Identity platforms, vulnerability tools, encryption standards, posture management, security monitoring and cloud-native controls are already in place. Yet many leadership teams still struggle to get fast, reliable answers to basic questions: who has access to what, where sensitive data is moving, which exposure creates the highest business risk, and whether policies are being applied consistently across environments.

We are witnessing a , where the speed of cloud adoption is outpacing the ability of security teams to maintain consistent visibility, detection and response.

In a multi-cloud estate, this becomes an operating model challenge. Each platform has its own way of managing identity, policy, logs, configurations and controls. When enterprises add more tools without connecting the control model, security teams are left correlating risk across systems that were not designed to work together.

For a CIO, the key cloud question is no longer, 鈥淒o we have controls?鈥� The better question is, 鈥淐an those controls work together when the business runs across multiple environments?鈥�

AI raises the cost of weak governance

AI changes the security conversation because it brings data, access and action closer together. A model or AI-enabled workflow may draw from customer, transaction, operational, product, service or employee data, and then use that context to support decisions, trigger workflows or guide automated responses.

That puts far more pressure on the data foundation. If enterprise data is duplicated, poorly classified, inconsistently governed or difficult to trace, the issue is no longer only whether the data is secure. It is whether the enterprise can trust how that data is being used, who or what has access to it, and whether the outputs can be explained with confidence.

Agentic AI adds another layer to this challenge. has identified agentic AI as a top cybersecurity trend for 2026, citing unmanaged agents, unsecured code and compliance exposure as emerging risks. As AI systems begin to act across workflows, call tools, access systems and interact with enterprise data, governance has to extend beyond where data sits to what actions AI-enabled systems can take. The access question also changes. The identity estate now includes service accounts, API keys, workloads, automation scripts, machine identities and AI agents that can hold permissions, trigger actions, call systems and move across environments. Many are created for a specific use case and remain in place long after the original need has changed.

This is why AI-ready cloud security has to begin with an AI-ready data foundation. Applications may continue to run across different cloud and SaaS environments, but AI-relevant data needs stronger consolidation, governance and lineage, while non-human identities need clearer ownership, least-privilege access, credential lifecycle management and faster privilege revocation. Without that, enterprises carry cloud fragmentation into their AI programs, creating gaps in privacy, accuracy, auditability and decision confidence.

For leadership teams, the implication is clear: cloud security, data governance and AI governance need to be designed together. The highest-risk gaps will appear where AI needs the greatest confidence – in data access, policy enforcement, traceability and delegated action.

Security architecture needs to become more coherent

The answer is not to simplify ambition or slow down AI adoption. It is to make the control model more coherent.

Enterprises need a unified view of identity, data exposure, configuration, vulnerabilities, runtime behaviour and response across cloud environments. They need policy consistency across platforms rather than separate interpretations of the same security principle. They need vulnerability management that prioritizes exploitable paths to critical assets, not just severity scores in isolation. They also need resilience planning that assumes incidents may cross cloud, SaaS and legacy boundaries.

AI can help security teams with correlation, anomaly detection, triage and remediation. It can also be misused by attackers to identify gaps faster and at scale. That dual reality makes governance even more important. Human oversight, explainability, guardrails and clear accountability need to be built into AI-supported security workflows, especially where automated action is involved.

For CXOs, the agenda is clear. Multi-cloud security has to move from a platform-by-platform control exercise to an enterprise governance capability. The priority is to reduce fragmentation where it creates the greatest risk: identity, data governance, policy enforcement, observability, vulnerability prioritization and response.

AI will not wait for cloud estates to become simpler. Security architecture has to make them more governable.

Author:

Varoon Rajani
Sr. VP & SL Head – CS

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AI is changing the test.

As enterprises move from pilots to scaled adoption, cloud maturity is being measured less by where workloads sit and more by whether the cloud foundation can support trusted data, secure access, elastic compute, controlled spend, modern applications and resilient operations. That is where a gap is becoming visible.

The issue is not cloud adoption. Most enterprises have already made meaningful progress. The issue is that many cloud estates were built for application migration and infrastructure flexibility, while AI demands something more integrated: governed data, stronger security, cost discipline, modernized applications, and automation-led operations.

forecasts worldwide AI spending to reach $2.59 trillion in 2026, up 47% year over year, driven heavily by hyperscalers and vendors. That scale of investment will put pressure on enterprise cloud foundations that were never fully modernized for AI-grade workloads.

For CIOs, the maturity question is becoming sharper: is the cloud estate ready to carry enterprise AI, or will it amplify the debt already sitting inside the technology landscape?

Cost is becoming an AI readiness issue

Cloud cost has always been part of the CIO conversation. AI is making it harder to treat cost purely as a consumption management problem.

AI workloads introduce new cost patterns. GPU consumption, model training, inference, token usage, data movement, storage growth and experimentation cycles can all change the economics of cloud programs. In our client conversations, cloud cost control and FinOps are already among the most urgent areas of concern, especially as AI adds a new layer of consumption unpredictability.

The FinOps Foundation鈥檚 notes that FinOps has expanded beyond cloud into a proactive, technology-wide discipline, with AI now dominating the forward-looking agenda. This is an important signal. Cloud cost maturity can no longer depend only on monthly reports, budget alerts, or infrastructure optimization exercises. AI requires a more active cost model: tagging, ownership, workload-level visibility, consumption forecasting, governance policies, and automated controls built into the operating model.

For CIOs, FinOps has to move closer to architecture. The question is not simply whether cloud spend is rising. The better question is whether teams can connect spend to business value, workload design, data movement, performance needs, and modernization choices.

A lift-and-shift estate will usually cost more than expected because it carries old application assumptions into a consumption-based environment. AI makes that even more visible.

Security and privacy now sit at the center of cloud maturity

Security concerns around cloud migration have matured over time. Many enterprises are now comfortable running critical applications on cloud. AI, however, introduces a different security and privacy concern because enterprise data may be used to train, fine-tune, or power models and AI-enabled workflows.

The worry is straightforward: when data moves from enterprise-controlled environments into cloud-based AI services or model ecosystems, leaders want assurance that it remains secure, private, and governed.

It鈥檚 clear that cloud is increasingly central to enterprise AI strategy. At the same time cloud adoption has expanded the attack surface faster than traditional security models can protect it.

For CIOs and CISOs, this changes the cloud maturity conversation. Security cannot remain a layer applied after migration. It has to be embedded into landing zones, identity models, data governance, network design, policy enforcement, observability, and incident response.

This becomes even more important in multi-cloud and hybrid estates, where different platforms may have different security parameters, policies, controls, and monitoring structures. When data crosses environments, risk rises unless governance and visibility are consistent. Cloud maturity therefore has to include security posture, data protection, identity governance, and resilience across the full estate.

Data readiness is the hidden maturity gap

The most important AI gap may not be in cloud infrastructure at all. It may be in data readiness.

In many enterprises, data sits across source systems, warehouses, lakes, SaaS applications, and legacy platforms. Some of it is duplicated. Some is poorly governed. Some is not integrated with enterprise data platforms. Some lacks quality, lineage or metadata. That makes it difficult to use as reliable input for AI.

Cloud can help solve this, but only when used as a governed data foundation rather than just an infrastructure destination. Enterprises need to bring critical data stores, warehouses and governance layers into a more coherent cloud architecture so AI workloads can draw from trusted, classified, and well-managed data.

There is a practical reason for this. AI does not perform well on fragmented context. If customer, product, transaction, service and operational data are not connected, the enterprise cannot easily build correlation, dependency mapping or decision intelligence. Aggregating AI-relevant data in one place makes it easier to govern, correlate and use for AI with stronger security, privacy, and accuracy controls.

We recently helped an insurance organization migrate data to cloud and create a data layer with master data management and governance. Once that foundation was in place, the organization was able to run machine learning and AI algorithms more efficiently on the platform.

That is the deeper lesson for CIOs. AI readiness starts before the model 鈥� with data architecture, data governance and cloud design.

Modernization debt is becoming harder to carry

Many enterprises still carry applications built over 10, 15, or 20 years. These systems were often left unchanged because modernization was expensive, time-consuming and difficult to prioritize against new business initiatives. The result is technical debt that shows up as security risk, scalability limits, performance constraints, and operational fragility.

Cloud migration exposed some of this debt. AI will expose more of it.

A legacy application can be moved to cloud and still remain structurally unready for AI-era demands. If it cannot scale elastically, integrate easily, expose data securely, support automation, or operate with modern observability, it will limit the enterprise鈥檚 ability to use AI in meaningful workflows.

This is why modernization has to become part of the cloud maturity agenda. CIOs need a clear view of which applications can be retained, which can be rehosted, which need refactoring, which should be re-architected, which require database changes, and which should be retired.

The stronger point is that modernization should not always wait until after migration. In many cases, simply moving applications as-is creates higher cost, limited automation, and weak elasticity. Refactoring during migration can help applications use cloud-native capabilities more effectively and reduce the risk of carrying old inefficiencies into the cloud.

AI can also change the economics of modernization. AI-assisted modernization can significantly improve productivity in upgrading or transforming applications, turning programs that once took many months into shorter, more manageable initiatives.

Our recent engagement with a Tier-1 private sector bank in Southeast Asia shows how this can work in practice. The bank needed to modernize 21 legacy banking applications while maintaining business continuity, reducing technical debt, and improving release agility. 乐播传媒视频 helped the bank move to a more standardized, cloud-native modernization approach, creating a stronger foundation for scalability, security, and long-term cloud-first growth. The program delivered 35% faster release cycles, a 30% reduction in operational effort, and a repeatable modernization framework for future initiatives.

For CIOs, this creates a window to revisit modernization debt with a different business case. The goal is not modernization for its own sake. The goal is to build a cloud estate that can support secure, scalable, automated, and AI-ready operations.

A practical roadmap for CIOs

The cloud maturity gap cannot be closed through isolated initiatives. Security, cost, modernization, data, and operations are now tightly connected. A cost issue may be rooted in poor architecture. A security issue may be rooted in fragmented identity. A data issue may be rooted in incomplete modernization. An AI issue may be rooted in all of them.

A practical CIO roadmap can begin with five moves.

• First, set up the landing zone correctly. This is the foundation for governance, security, identity, networking, and policy consistency.
• Second, identify pilot applications and use them to validate the operating model before scaling migration or modernization.
• Third, define tagging, ownership and FinOps pipelines early, so cost visibility and accountability are built into cloud adoption rather than added later.
• Fourth, migrate and modernize with automation in mind. The goal is to reduce manual effort, strengthen consistency, and improve operational scalability.
• Fifth, bring in monitoring, automation, and AIOps to manage cloud infrastructure and applications with greater intelligence and responsiveness.

For leadership teams, the broader shift is clear. Cloud maturity is no longer defined by migration completion. It is defined by whether the enterprise can support AI at scale without losing control of cost, security, data quality, modernization and resilience.

AI ambitions will keep growing. The cloud foundation has to catch up.

Author:

Varoon Rajani
Sr. VP & SL Head – CS

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The number of organizations deciding to pay a ransom has risen to 32% in 2021 compared to 26% in 2020 (). Even after paying for Ransomware, only 8% of them got all their data back, nearly a third, 29%, couldn’t recover more than half the encrypted data. However, on average, only 65% of the encrypted data was restored after the ransom was paid. Approximately 37% of global organizations (more than one third) said they were the victim of some form of Ransomware attack in 2021 (IDC’s “). 92% who pay don鈥檛 get their data Back (.

We all know that Confidentiality, Integrity and Availability are the 3 pillars of security. Integrity of Data is an important dimension, which means that data has not been altered in an unauthorized manner when data is 鈥渁t rest, getting processed, or in transit鈥�. Here we will be focusing only on 鈥渁t rest鈥� Data related to Ransomware. It鈥檚 evident that, while there is a high level of efforts required to prevent 鈥淎ttackers from getting in鈥� or 鈥渆scalating their privileges within system鈥� the best bet for an organization remains to 鈥淧rotect their critical data from unauthorized access and destruction鈥�.

Ransomware attacks focus on encrypting any data to which they could get write access, including the backup system. This may also happen due to poorly implemented permissions that exposed backup data stored anywhere. This makes Ransomware attack more effective because organizations can鈥檛 recover data from backup systems.

The big step towards getting data protected is to have isolated, immutable backup of data which is not accessed in general and have very strict administrative access authentication, authorization adjustments for a set of admins. There was a time when data backup on physical tapes were kept off-site to be protected from any Data center physical damage as part of BCP/DR approach. That was one of the best ways to ensure data integrity. We should leverage Cloud offerings which are equally effective to protect data from any Ransomware attack.

Now let鈥檚 discuss about immutable backup methods which will be the key ask here. Azure has introduced Blob storage options to operate like an Immutable storage and enables users to store business-critical data in a WORM (Write Once, Read Many) state for a defined time interval. While in a WORM state, data objects can be created and read, but cannot be modified or deleted for a user-specified interval. By configuring immutability policies for blob data, customers can protect their data from overwriting and deletion. Another benefit of Azure Blob storage is having a legal hold, which stores immutable data until the legal hold is explicitly cleared. When a legal hold is set, objects can be created and read, but not modified or deleted. It鈥檚 important to understand how immutability is implemented and whether it is truly聽WORM, even if OS administration accounts are compromised.

Those who are on AWS platform, can use AWS Backup Vault Lock to prevent (accidental or malicious action) any user from deleting their backups or making changes to their backup lifecycle settings. AWS Backup Vault Lock (S3 Glacier) improves customer鈥檚 security postures and ensures a mechanism for restore, even in a worst-case scenario like total account compromise. Another service that鈥檚 useful for data protection is the AWS object storage S3, where you can use features such as聽object versioning聽to help prevent objects from being overwritten with Ransomware-encrypted files, or聽Object Lock (S3), which provides a write once, read many (WORM) solutions to help prevent objects from ever being modified or overwritten.聽

You can use Compliance聽retention mode if you never want any user, including the root user in your AWS account, to be able to delete the objects during a pre-defined retention period. You can use Legal Hold as an infinite retention period. Once applied it is not possible to delete any object until the hold is released manually (only by users with special permissions). Every backup within the retention period is an immutable backup with point-in-time restore capabilities. Also, we have S3 MFA delete-enabled bucket option which safeguard from permanent delete of an object version or change the versioning state of the bucket.

Similarly, GCP storage containers with Bucket Lock offers write-once (WORM), immutable storage to meet your compliance standards and ensure your data鈥檚 integrity while offering instantaneous access for quick restores. As part of protection, once you lock a bucket, you cannot unlock it until all objects are out of the retention period. Retention policies prevent the deletion or modification of the bucket鈥檚 objects. Applying Bucket Lock聽to a storage bucket in the Archive class can help you achieve WORM compliance for long-term data archival as well.

Apart from introducing immutable backup options which provide a secure storage for your data, we all know initial steps such as to keep multiple copies of data backup (keeping data off-site), use a standard practice of Multi-Factor Authentication (MFA) for administrative accounts, separation of administrative roles. We also need to enable encryption of the data and segment the workflow so that authorized systems and users have limited access to use the key material to decrypt the data. We know that network sharing protocols work well for general-purpose file sharing. However, minor mistakes in permissions can lead to data being exposed. In place of using them, we recommend using object storage APIs, for example Amazon S3 compatible APIs, virtual tape libraries, or keep storage as 鈥渓ocal鈥� to the backup server (do not access over a network sharing protocols).

Interesting part to understand here is, the technology which was initially introduced for a security compliance requirement to keep the golden copy of any data for later auditing or reconciliation, has taken a shift to be also used to safeguard from ransomware attacks to maintain integrity of data. Cloud storage is an economical solution because resources are readily available, is scalable, and multi-tiered.

Author:

Deepak Kumar,
Cloud Practice Head,
乐播传媒视频

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Analysts predicted that by now, . While at one side this strategy presents several obvious benefits, on other side it increases complexities that can result in unproductive resources, poor cost management, regulatory risks & security concerns. Choosing the right multi-cloud management tool can take away these headaches, without compromising business goals, by adopting efficient and outcome-based model.

Hyperscalers recognize this need and provide their native tools. While such tools are useful, many may add more costs to achieve more granular control over infra services. Not surprisingly, enterprises are looking for solutions that provide comprehensive coverage of their needs and helps in multi and hybrid cloud management & governance. They are continuously hunting for such tools to manage IT Estate from a single interface:

• Live monitoring of resources across providers and on-prem
• Manage accounts and configurations across cloud environments
• Setup quotas, thresholds, and workflow approvals
• Provide comprehensive governance controls
• Generate recommendations for cost optimization, handle overall spend and chargebacks Visibility into resource
• usage, security and compliance and recommendations

There is a great need for such tools that can cut across cloud environments. This is clear from the fact that . The solution is a reliable integrated Cloud Management Platform (CMP) with the power to ensure that the enterprise leverages the full potential of its cloud investments and delivers projected ROI.

乐播传媒视频 provides a fully packaged CMP that not only meets the complex requirements of managing multi and hybrid cloud but is also customized to meet specific domain and business needs. Our automated solution comprises of CloudBolt and Azure Arc which can offer:

• Simplified & Consistent management
• Cost optimization & Recommendations
• Centralized orchestration, provisioning, reporting and automation
• Simplified security audits and compliance checks
• Accelerate workload delivery through self-service IT

The outcome is reduced costs, improved resilience, flexibility, and security, with right control along with well architected deployment and accelerate multi-cloud adoption. The best part about our solution is our approach to provide simple, seamless, and easy deployment across the board

Author:

Deepak Kumar
Cloud Practice Head 聽

Abhishek Asija
Cloud Architect

Jay Prakash Shukla
Sr. Cloud Architect 聽

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1. The demand for reliable cloud expertise going up. This demand is for assessment and advisories from large enterprises. They have a cloud strategy in place along with the budgets but are stuck because they are unable to look at cloud implementations through the lens of risk, security and ROI.
2. The realization that the overall cost of cloud is higher than what is commonly believed. The most commonly held belief is that cloud results in cost reduction. This is true of the long-term. In the short term, the paradox of cost results from the fact that exploiting cloud requires an optimization of technology 鈥� applications, data, networks and operating models鈥攁nd benefits are tied to net spends.
3. The lack of cloud talent being seen as a showstopper. Cloud skills are hard to find. The spectrum of work called for by cloud is diverse, encompassing infrastructure, migration, storage, networks, portfolio adaptability, SaaS, an understanding of cloud providers, serverless architecture, security, change management, DevOps, Artificial Intelligence, Machine Learning, automation, etc. University cloud programs are not mature, businesses do not have adequate infrastructure such as labs for in-house training programs and there is uncertainty about regulatory readiness making customers nervous about where to keep their data as we evolve into a boundaryless paradigm.

According to Gartner, just public cloud end-user spending will grow 18.4% in 2021, to total $304.9 billion, up from $257.5 billion in 2020. NASSCOM data (from Zinnov) shows that demand for cloud skills will balloon, leading to a 39% increase in open positions. These figures indicate that CIOs don鈥檛 have a choice. They have to go ahead with cloud adoption with an eye on scalability, shorter deployment cycles, the ability to innovate, access to a rich application eco system, and push-button channel integration that will provide good market responsiveness.

But here is the truth: For every $100 they currently spent on on-premise, it will cost them just $30 for managed assets on cloud. This spells major savings, allowing organizations to release large budgets for investments in innovation programs. But none of this will be possible if skilling remains a challenge. Creating the right talent is the solution.

To achieve skilling at scale, we urgently need a variety of initiatives. These can be private, public or based on private-public partnership (PPP) models. This is where programs like NASSCOM鈥檚 鈥榝utureskills prime鈥� fit in. It addresses the problem of skilling by aiming to upskill 400,000 people over 3 years across 10 emerging technologies and 10 professional skills. The program uses a public-private partnership model for business success and content that is AI-curated and hand-picked (by industry SMEs) for learning success.

However, the goal of the futureskills initiative runs deeper than upskilling鈥攊t is aimed at creating competency standards (knowledge required to perform a job, key functions that need to be undertaken and the skills required to do this effectively) called National Occupational Standards (NOS). To support NOS, a common language is being created. This will provide a clear and shared understanding when jobs and roles are discussed across academia, industry and government.

We need dozens of such skilling initiatives. Without them, organizations will be left stranded. They will not have the talent pipeline which can respond to the accelerated demand for cloud. For the moment, the priority of every organizations with the ambition of riding the cloud wave should be to create the talent that will take them into the future.

Reference:

Author:

Narasimha Moorthy
Vice President and Head of ADM Service Line
乐播传媒视频

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