Cloud computing is an important part of the changing work landscape, but it is also affecting personal computer users as well. The positive changes that are offered by cloud computing cannot be overstated as we move into a nimble and responsive new reality with regard to software, hardware, and computation services.

Cloud computing is internet-based functionality for applications and programs that frees users from having to run the software and other programs on their local computer. Cloud computing makes it possible to share information readily between team members and locations and makes certain that teams of workers or multiple users in the same household all have access to necessary information and documents.

If you are ready to learn more about the benefits of cloud computing, read on for more information!

What is Cloud Computing?
We already discussed that cloud computing allows multiple users to access the same information from the same location without having to host applications and software on their local machines. You might be wondering how this is possible.

Cloud computing allows all of the storage that would normally be done on individual machines to be done remotely in the cloud. This means that all users and machines who have access to the data in the cloud will be able to get to this information and the services hosted in the cloud, no matter where they are located or what machine they are working on.

This offers powerful computing power and dynamic information sharing that cannot be done when individual users are all attempting to share local data across limited access points. Cloud computing also keeps data and software more secure in the case that a unique user’s computer is affected by malware or another cyber threat.

Main Benefits of Using Cloud Computing

1. Speed
Deployment and sharing of information happen at lightning speed with cloud computing on your side. There is no more agile way to apply changes to software and databases and cloud computing offers a uniformity in deployment that cannot be enjoyed with other kinds of data and asset management.

Cloud computing also offers unique access to testing new products and designs that are not available when you are trying to manage many unique assets that require the same changes to be made to their operating systems. Being able to test products and services before they are made available to the cloud is an ideal part of the use of cloud computing.

2. Automatic Updates
Continuous integration and continuous delivery are the biggest arguments for cloud computing that you can make. There is no better way to support the needs of your assets related to support, updates, and improvements.

The automatic nature of updates and support when using cloud computing means that your organization and your data will be far more secure and consistently managed than if you are trying to maintain unique machines that are running their own versions of applications and other software assets.

Developers also appreciate that cloud environment solutions also integrate with DevOps tools, making it much easier to detect issues in production processes.

3. Data Security
Data security is the leading concern of most businesses these days and data security is a major factor for personal use computers these days as well. Data security is increasingly difficult these days, especially with remote work requirements.

Cloud computing offers the ability to store your company or personal information securely and without the risk of a single machine affecting the entirety of your network. Granular permissions as well as role-based access can make it much more difficult for bad actors to get access to your network and your data.

Cloud computing comes with built-in encryption that is beyond the deployment skills of most unique users. This additional layer of improved cybersecurity can save you money on other security measures and you will have much better protection in the cloud than you would with numerous products deployed on unique machines in an attempt to protect each asset by itself.

4. Scalability
This benefit of cloud computing is a huge help when you are working with a large organization or a large number of unique machines and assets that need to have the same policies and access delivered to them. While you might not have hundreds of employees that you are trying to manage, there are many organizations that do have many hundreds of people who all need to have the same access and the same permissions.

Even in a smaller company, being able to scale your products and software solutions up and down as needed is a huge benefit of cloud computing. To make these changes, you will only have to update the cloud. Change processes that would take months without cloud computing can be handled in a day or less with access to a cloud-managed system.

5. Cost Reduction
Cloud computing can save your organization a lot of money because you will only have to spend money on your cloud hosting and the software that you need to utilize for your processes and business needs. You will save lots of money on hardware, facilities, utilities, and a large IT staff to take care of your needs.

Cloud computing moves all of your IT needs into one kind of service that you pay a flat rate for in most cases. This can also help you prevent downtimes and other slowdowns that might affect your bottom line if you are not able to do business during an outage.

Cloud Computing is Incredibly Beneficial

Cloud computing is the way of the future, and more and more businesses are going to adopt it over the course of the next year or two. Even personal use often dictates cloud solutions for many different kinds of needs.

If you are ready to work on solutions for your business needs in relation to the storage, deployment, and security, cloud computing is the perfect answer to these needs. You will find that cloud computing solves many of your current struggles and leaves you room to grow and improve in any direction that you want.

Cloud computing is quickly gaining ground in the business world and Microsoft Azure is taking the lead. About 84% of enterprises had a multi-cloud strategy in 2019. Gartner’s Top Strategic Trends for 2021 has established that organizations require a strong AI strategy to make sure that their projects don’t stall.

With cloud computing showing no signs of reducing in its application and Microsoft taking a strong lead, it is important to examine what Azure Cloud is and how it impacts businesses and what Microsoft would be doing with it in the future.

What is Microsoft Azure?
Azure Cloud is a cloud platform that provides both low-level cloud solutions and high-level infrastructure. The infrastructure makes it possible for companies to run their applications in a scalable, elastic, and redundant environment hosted in the cloud.

Signing up on the Azure Cloud platform gives you access to over 200 products and cloud services that are designed to offer new solutions to your business operations. It is designed to solve modern business challenges and help build a future that would be unimaginable without cloud computing.

With Microsoft Azure, you can build, run and manage applications across multiple clouds, on-premises, and at the edge with great tools of your liking.

Why Azure Cloud holds the future of cloud services?
Virtually everything in the digital world has a connection with the cloud in some way. As tech giants focus on developing new ways of organizing, processing, and presenting data, cloud computing is slowly becoming part and parcel of our everyday lives.

In the recent past, Microsoft Azure, Google Cloud, and Amazon Web Services have provided cloud computing and cloud storage services. Newer entrants such as Vultr and Linode have also made inroads into the space.

What do you think is the future of cloud computing? How will it affect future business strategies, and to what extent? Concepts such as decentralized cloud computing and serverless cloud may start trickling into your mind.

Infrastructure as a Service (IaaS)
Azure today is synonymous with Infrastructure as a Service. IaaS is the primary low-level public cloud service used to provide instant computing infrastructure. It is managed over the internet.

IaaS is unique because it is less expensive and is a simple way of managing physical servers and data center infrastructure. It offers essential storage, computing, and networking resources on demand.

With all the amazing computing benefits that come with it, adopting an IaaS solution for your organization helps reduce maintenance of on-premises data centers, gain real-time business insights and save money that would have been spent purchasing and maintaining hardware.

Organizations are keen on the scalability of their business operations, and more emphasis will be put on this in the future. IaaS solutions give businesses the flexibility to scale their IT resources up or down as their needs change. They also have a provision for you to quickly introduce new applications, further increasing the reliability of your infrastructure.

With IaaS, you will not incur the costs of buying and managing physical servers and data center infrastructure. Besides, you only have to pay for the resources you need since each one of them is offered separately.

Azure Data and Analytics
Azure data and analytics provide unified data governance that helps organizations do away with data challenges and shape their future.

Azure Cloud makes the future look bright by offering the general availability of Azure Synapse Analytics, a limitless analytics service that provides business insights with an emphasis on enterprise data warehousing, Big Data, and data integration.

Azure Synapse Analytics combines various capabilities, among them, machine learning, business intelligence, and data engineering. Simply put, Azure Synapse Analytics securely and seamlessly unifies your company’s data in one location.

You can get started with Azure’s machine learning features in this guide from Iteration Insights.

Data Security
No other player in the tech industry beats Azure Cloud in offering secure cloud computing. Microsoft leverages several years of being a productivity solution provider and being an enterprise operating system to provide top-shelf security to all the information hosted in their cloud products.

Microsoft Azure has put together several resources to ensure organizations’ on-site data centers are secure. They update their systems on a regular basis to make their systems immune to external threats.

Once you have purchased Azure Cloud services, you will gain access to all elements related to the security of your information. All their customers have access to the latest threat detection and prevention technologies.

There is a range of security solutions and architecture that come with Azure Cloud. They include Information Protection which enables organizations to classify and protect specific information, documents, and emails by applying labels to them, and Advanced Threat Protection and Threat Intelligence.

Azure Cloud threat monitoring agents generate real-time security alerts and threat intelligence reports which provide insights into the attackers, their motives, and their tactics.

Improved business sustainability and scalability
Many businesses will be expanding their operations in the future. If they want to do so without major hiccups, they should adopt Microsoft Azure Cloud technology which provides scalability to businesses.

Under normal circumstances, it is quite costly and time-consuming to expand a business and provide it with new infrastructure. Azure Cloud guarantees the availability of services throughout the year, thanks to its many data centers the world over. As if that is not enough, Microsoft is planning to add even more servers.

Azure Cloud boasts of having a physical infrastructure in every continent. This allows for replication in the case where an organization would like to expand beyond the borders of one continent.

Final thoughts
There is no doubt that Azure Cloud is a game-changer in cloud computing. The public cloud service, IaaS, for instance, is making cloud computing very easy while providing a simple way of managing physical servers and data center infrastructure. If you picture the future of cloud computing, you are probably thinking serverless cloud, enhanced data security, improved business analytics, and business scalability. Azure Cloud provides it all. It holds the future of cloud computing in its hands – or in the cloud – so to speak!

This article is based on guidance originally published at techspective.net.

1. Make Data Security a Cultural Priority

Every method of storing and managing data carries inherent risk — cloud computing is not uniquely dangerous, but it does require deliberate security practices. For a small business, the consequences of a breach can be far more severe than for a large enterprise with dedicated security teams and deep pockets for incident response.

Practical steps to strengthen cloud security include:

• Embed security in company policy. Data security values should be part of your company’s operating principles, not an afterthought. Make explicit expectations clear in onboarding materials and employee handbooks.
• Train employees on secure cloud usage. Human error is one of the leading causes of cloud data incidents. Regular, practical training on phishing awareness, password hygiene, and proper use of cloud platforms reduces this risk meaningfully.
• Use Virtual Private Networks (VPNs). When employees access cloud resources over public or home networks, a VPN encrypts the connection and limits exposure to eavesdropping or man-in-the-middle attacks. Standardising VPN use across all remote access is a low-cost, high-impact control.
• Enable multi-factor authentication (MFA). MFA is one of the most effective controls available for cloud accounts and should be enforced for every user, not just administrators.
• Review access controls regularly. Apply the principle of least privilege — employees should only have access to the cloud resources they need for their specific role.

2. Use Cloud Tools to Encourage Cross-Team Collaboration

One of the most immediate, tangible benefits of moving to the cloud is the ability to collaborate in real time regardless of location. In a small business, where individual employees often wear several hats and depend heavily on each other, this capability can have a direct impact on productivity and morale.

Cloud-based collaboration tools allow team members to:

• Co-edit documents and spreadsheets simultaneously without version-control headaches.
• Share project updates, feedback, and approvals through integrated communication platforms rather than fragmented email threads.
• Access shared resources from any location, enabling flexible or hybrid working arrangements without sacrificing visibility.

Choosing platforms that integrate well with one another — for example, a cloud storage solution that connects natively to your team’s messaging and project management tools — reduces friction and encourages adoption. Tools that are clunky or disconnected will be avoided, undermining the collaboration benefits you are trying to achieve.

3. Think Carefully Before Allowing Personal Devices

Bring Your Own Device (BYOD) policies are attractive to small businesses trying to control costs, but they introduce security complexity that is easy to underestimate. When an employee’s personal laptop or smartphone connects to your cloud environment, you have limited control over the security posture of that device — whether its operating system is patched, whether it is also connecting to risky networks, or whether other users in the household have access to it.

If your business cannot yet provide company-owned devices to all employees, a minimum acceptable BYOD policy should require:

• Installation of your organisation’s endpoint security or Mobile Device Management (MDM) software before cloud access is granted.
• Operating system and application updates kept current.
• Mandatory VPN use when accessing corporate cloud resources remotely.
• A clear acceptable-use policy that employees acknowledge in writing.

Where budget allows, issuing company-managed devices for cloud-related work gives the business control over the full security stack — from the endpoint through to the cloud platform — and avoids the risk of sensitive data residing on personal devices outside the business’s control.

4. Scale Your Cloud Usage Deliberately

Cloud platforms make it technically easy to spin up new services, add users, and expand storage — sometimes too easy. Ungoverned cloud growth leads to sprawl: unused resources accumulating costs, inconsistent security configurations across services, and a growing attack surface that no one is actively monitoring.

Scaling your cloud environment mindfully means:

• Reviewing your cloud footprint regularly. Audit which services and storage buckets are active, who has access, and what they cost. Decommission what you are not using.
• Applying security baselines before expanding. Before adding a new cloud service or onboarding a new team, confirm that your security policies extend to cover the new surface area. Do not bolt security on after the fact.
• Planning for growth in advance. Understand your cloud provider’s pricing tiers and data egress costs so that scaling decisions are made with a full picture of the financial implications, not just the technical ones.
• Documenting your architecture. Even in a small business, a simple diagram of which cloud services you use, how they connect, and who is responsible for each one is invaluable when onboarding new staff or responding to an incident.

Key Takeaways

Cloud computing can deliver genuine agility, productivity, and collaboration benefits to small businesses — but these gains are only realised when the foundation is sound. Before expanding your cloud usage, ensure that your security posture, access controls, and device management policies are in place. Treat cloud adoption as an ongoing operational discipline rather than a one-time implementation project, and your investment will continue to pay dividends as your business grows.

Why the PCI Council Needed a New Framework

The PCI Security Standards Council released the PCI Software Security Framework, a new software security standard designed to validate payment ecosystem security in the context of modern software architectures and development practices. The framework is intended to ultimately replace the existing PCI Payment Application Data Security Standard (PA-DSS).

The motivation is straightforward: payment software is no longer built or delivered the way it was when PA-DSS was written. Organizations now ship software multiple times per day, build on microservices, deploy to hybrid clouds, and rely heavily on containerization and third-party libraries. The old standard was not designed with any of that in mind.

“Software development practices have evolved over time, and the new standards address these changes with an alternative approach for assessing software security.”

Troy Leach, CTO, PCI Security Standards Council

The Problem with PA-DSS in a DevOps World

Jeff Williams, co-founder and CTO of Contrast Security and a participant in the expert council that contributed to the new standard, described PA-DSS as “very brittle.” The standard mandated specific controls in a prescriptive way — “you had to do A, B, and C” — which left little room for the reality of modern development pipelines.

“When you’re looking at DevOps projects that are releasing seven times a day and moving super fast and using tons of libraries, and building APIs, and deploying in the cloud, that old standard just didn’t work well.”

Jeff Williams, CTO, Contrast Security

PA-DSS was designed to guide traditional payment software developers in securing the software development lifecycle (SDLC). It was not built to account for the pace, tooling diversity, or architectural patterns that characterize mature DevOps organisations.

What the PCI Software Security Framework Introduces

Rather than a rigid checklist of controls, the new framework takes an objective-focused approach. This gives organisations greater flexibility in how they demonstrate that their software meets security goals, while still providing a consistent validation methodology for payment application security.

Key changes include:

• Broader security testing options: In addition to static analysis (SAST), dynamic analysis (DAST), and manual penetration testing, the framework explicitly recognises Interactive Application Security Testing (IAST) as a valid testing methodology.
• Support for continuous testing: IAST is particularly relevant for DevOps environments because it can monitor applications during runtime as part of automated pipelines, rather than requiring a discrete testing phase that interrupts delivery.
• Secure Software Lifecycle qualification: The framework introduces a separate qualification path for vendors that can demonstrate mature, security-focused design and development practices — going beyond individual application validation.
• Expanded scope: Where PA-DSS focused on the SDLC for traditional payment software, the new framework addresses overall software security resilience, covering a broader range of modern payment platforms.

IAST and Continuous Security Validation

The inclusion of IAST as an accepted testing method is significant for DevOps teams. Unlike SAST (which analyses source code without executing it) or DAST (which probes a running application from the outside), IAST instruments the application itself during execution. This allows security testing to happen continuously as part of integration or staging pipelines, fitting naturally into CI/CD workflows without requiring separate test windows.

For teams releasing software frequently, this removes a longstanding friction point: the need to pause the pipeline for a dedicated security testing phase in order to remain compliant.

Transition Timeline: PA-DSS Is Not Going Away Immediately

The PCI Council was clear that the transition from PA-DSS to the new framework would be gradual. Organisations that have already invested in PA-DSS validation will not be immediately disrupted. According to Troy Leach at the time of the announcement:

• PA-DSS validated applications would continue to be governed under that programme until 2022.
• A formal validation programme for the new Software Security Framework was expected to be released during 2019.
• The two standards are described as “not mutually exclusive” — organisations can leverage either or both during the transition period, with the new framework offering a progressive path.

“There will be a gradual transition period to allow organizations with current investments in PA-DSS to continue to leverage those investments.”

Troy Leach, CTO, PCI Security Standards Council

What This Means for DevOps and Security Teams

For engineering and security teams building or maintaining payment-related software, the PCI Software Security Framework represents a meaningful shift in how compliance intersects with modern delivery practices. The key takeaways are:

• Flexibility over prescription: The objective-based model gives teams room to choose security tooling and processes that fit their pipeline, rather than mapping to a fixed list of required controls.
• DevOps-friendly testing: Recognition of IAST removes a significant barrier for teams using continuous delivery.
• Vendor maturity rewarded: The Secure Software Lifecycle qualification offers a pathway for vendors with strong security practices to demonstrate that at an organisational level, not just per-application.
• Plan for the PA-DSS sunset: Teams still operating under PA-DSS validation should understand the transition timeline and begin evaluating alignment with the new framework ahead of the eventual retirement of PA-DSS.

The framework was developed with input from payment technology and security industry experts, reflecting a broader effort by the PCI Council to keep its standards relevant as the technology underpinning payment systems continues to evolve rapidly.

Why the Public Sector Needs Its Own Cloud Approach

Governments are, in many respects, the largest organisations in their countries. They employ more people, process more sensitive data, and serve more stakeholders than almost any private enterprise. At the same time, they operate under strict legal frameworks, procurement rules, and accountability standards that do not apply to commercial organisations. These constraints drive demand for cloud solutions that are purpose-built — not simply adapted — for government use.

Major cloud providers including Microsoft and Google have developed government-specific offerings to address this market. Simultaneously, specialist providers have emerged to serve specific national public sectors. The result is a distinct segment of the cloud market with its own architecture patterns, compliance requirements, and operational norms.

Emphasis on Data Security and Sovereignty

Data protection is the defining characteristic that separates public sector cloud from standard commercial cloud. Government agencies hold some of the most sensitive data that exists: citizen identity records, tax information, healthcare data, law enforcement intelligence, and defence-related material. A breach or unauthorised disclosure in any of these areas carries consequences that go well beyond financial loss — they can compromise national security or erode public trust.

This drives several specific requirements:

• Accreditation and certification: In the UK, for example, government bodies are required to work with accredited cloud providers. UKCloud, a specialist provider focused on the UK public sector, has received Pan Government Accreditation from the National Cyber Security Centre (NCSC). Similar accreditation frameworks exist in other jurisdictions, such as FedRAMP in the United States.
• Data residency: Many governments require that citizen data be stored and processed within their own national borders, creating hard constraints on where cloud infrastructure can be physically located.
• Sovereignty: Beyond residency, governments often require that cloud providers operating in the public sector be legally and operationally independent — unable to be compelled by foreign governments to disclose data.
• Separation of environments: Public sector workloads are typically run in logically or physically isolated environments, separate from commercial tenants, to reduce the risk of cross-contamination or lateral movement in the event of a security incident.

These requirements push providers toward architectures — such as dedicated government cloud regions — that would be unnecessary and cost-prohibitive for most private sector deployments.

Enhanced Flexibility Within Complex Procurement Constraints

One of the more counterintuitive aspects of public sector cloud is the demand for flexibility in an environment defined by rigid processes. Government procurement cycles are long, approval chains are deep, and policy frameworks can change with elections or budget cycles. Cloud platforms serving the public sector must accommodate these realities rather than assume the rapid, on-demand decision-making typical of agile enterprises.

In practice, this means:

• Configuration-driven deployments: Rather than requiring code changes or re-procurement, public sector cloud solutions are typically designed so that agencies can reconfigure services within pre-approved parameters without triggering a new procurement process.
• SaaS partnerships: Providers like UKCloud partner with SaaS vendors that have already been security-assessed and approved, allowing government departments to adopt purpose-built software without each department having to conduct its own lengthy evaluation.
• Multi-tenancy with hard boundaries: Different government departments may share underlying infrastructure but with strict logical separation, enabling shared cost models while maintaining the isolation that policy and security require.
• Support for diverse use cases: A national health service, a local council, a defence procurement body, and a social services department all have different workflows and data models. Public sector cloud platforms must support this diversity without requiring bespoke infrastructure for every use case.

Flexibility here is not the same as agility in the DevOps sense. It means designing systems that can be adapted by non-technical policy owners and administrators within guardrails set by security architects — a meaningful architectural constraint.

Enhanced Reliability and Multi-Cloud Strategies

Availability is non-negotiable in the public sector. When a government system goes down — whether it handles benefits payments, emergency services dispatch, or voter registration — the consequences are immediate and public. This makes the reliability requirements of public sector cloud more demanding than those of most commercial deployments.

Several architectural patterns are commonly used to meet these requirements:

• Multi-cloud deployments: Rather than relying on a single provider, many public sector organisations distribute workloads across multiple cloud platforms. This reduces the risk of a single provider outage affecting critical services and avoids vendor lock-in.
• Workload-appropriate placement: In a multi-cloud model, specific workloads can be placed on the platform best suited to them — for example, using one provider’s strengths in data analytics while relying on another’s high-availability compute infrastructure for transactional systems.
• Advanced cloud management layers: Orchestration and management tooling sits above individual cloud environments, providing a unified control plane that can route workloads, enforce policies, and monitor SLAs across providers.
• Defined SLAs with contractual teeth: Government contracts typically specify availability requirements with clearly defined remedies for non-compliance, creating stronger incentives for providers to maintain uptime than standard commercial SLAs may impose.

The multi-cloud reliability patterns pioneered in the public sector are increasingly being adopted by large enterprises for similar reasons — the lessons from government-scale deployments translate well to any organisation where downtime has significant downstream consequences.

Key Takeaways for DevOps and Cloud Engineers

If you are building, operating, or selling into public sector cloud environments, the following principles should guide your approach:

1. Understand the accreditation landscape in your target jurisdiction before designing an architecture. Compliance is not optional and affects every layer of the stack.
2. Data residency and sovereignty are hard constraints, not soft preferences. Multi-region or global-by-default configurations common in commercial deployments may be prohibited.
3. Design for configuration, not just code. The people making operational decisions in government may not be engineers. Platforms should be administrable by policy owners within pre-defined security boundaries.
4. Plan for multi-cloud from the start. Single-provider lock-in is a procurement and resilience risk that government buyers will actively try to avoid.
5. Reliability engineering is table stakes. SLA definitions, failover testing, and incident response procedures will be scrutinised in ways that go beyond typical commercial due diligence.

Public sector cloud is not simply commercial cloud with a few extra compliance checkboxes. It is a distinct discipline with its own architectural patterns, procurement realities, and operational requirements. Engineers who understand these differences are better positioned to build systems that actually serve the needs of government — and the citizens who depend on them.

Background: What Is Kubernetes?

Kubernetes is an open-source container orchestration platform originally created by Google and now maintained by the Cloud Native Computing Foundation (CNCF), part of the Linux Foundation. It is one of the most widely deployed pieces of infrastructure in modern cloud-native environments.

Unlike virtual machines, which include a complete guest operating system, containers package only the application code and its dependencies, sharing the host OS kernel. This makes them lightweight and portable, but it also means that a security breach in the container management layer can have wide-reaching consequences across an entire cluster.

Kubernetes groups containers into logical units called pods and provides scheduling, scaling, networking, and lifecycle management for those pods across fleets that can number in the tens or hundreds of thousands.

CVE-2018-1002105: The First Critical Kubernetes Vulnerability

In December 2018, the Kubernetes security team disclosed CVE-2018-1002105, the first vulnerability in the project to receive a critical severity rating. The flaw resided in the Kubernetes API server — the central control plane component that processes all instructions sent to a cluster via HTTP.

How Normal API Server Authentication Works

Under normal operation, the Kubernetes API server authenticates every incoming request — typically using certificate-based authentication — before forwarding it to the appropriate backend or pod. This gating mechanism is what separates authorised operators from everyone else.

How the Exploit Worked

CVE-2018-1002105 was a privilege-escalation vulnerability triggered by a flaw in the way the API server handled error responses when proxying connections to backend pods:

1. An attacker sent a specially crafted, malformed request to the Kubernetes API server — one designed to generate an error response from a backend pod.
2. Rather than closing the connection after the error, the API server left the HTTP connection to the pod open.
3. Subsequent requests sent over that same open connection were forwarded to the pod without any further authorisation checks.
4. The attacker effectively gained the same level of access as a fully privileged user, regardless of their actual permissions.

This unauthorised access extended to exec-level privileges — the ability to execute commands inside any container on the affected node, including containers with read/write access to the host filesystem.

Two Exploit Variants

The vulnerability had two distinct attack paths:

• Aggregated API servers: Exploitable by any authenticated user, including those with minimal permissions. This made it the more dangerous variant from an exposure standpoint.
• Exec/attach/port-forward API calls: Required the attacker to already hold admin or edit role-level access, making this variant slightly less severe but still significant in multi-tenant environments.

Potential Impact

Once an attacker successfully exploited CVE-2018-1002105, they could:

• Steal data — including secrets, credentials, and application data flowing between workloads.
• Execute arbitrary commands inside any running container on the compromised node.
• Read or modify the host filesystem if the targeted container had that level of access.
• Disrupt services by killing pods or corrupting workloads.
• Pivot laterally within the cluster to compromise additional nodes and workloads.

Affected Components and Distributions

Because Kubernetes is used as a foundational layer in many managed and self-hosted distributions, the blast radius of this vulnerability was significant. Red Hat confirmed that OpenShift version 3.x — which uses Kubernetes as a core component — was affected by the flaw. Other distributions and managed services built on Kubernetes were similarly impacted and issued their own advisories.

Remediation

The Kubernetes project released a patch addressing CVE-2018-1002105 promptly after disclosure. The recommended actions at the time of disclosure were:

• Apply the patch immediately. Clusters that supported automated upgrades were protected once the patch was applied.
• Manually upgrade any self-managed Kubernetes installations that do not auto-update.
• Review audit logs for unexpected API server connections or privilege escalation attempts that may indicate prior exploitation.
• Harden RBAC policies to limit the number of users and service accounts with aggregated API server access.
• Check your distribution’s advisory — OpenShift, GKE, AKS, EKS and other managed platforms each published specific guidance and patching timelines.

Lessons for Kubernetes Operators

CVE-2018-1002105 served as a wake-up call for the Kubernetes community on several fronts:

• No software is immune. Even a mature, heavily audited project like Kubernetes can harbour critical flaws. Treat Kubernetes as you would any other piece of critical infrastructure from a patch management perspective.
• Least-privilege RBAC matters. Restricting which users and service accounts can access aggregated API servers and exec endpoints limits the exploitability of vulnerabilities of this class.
• Enable audit logging. Kubernetes API server audit logs would have been the primary forensic tool for detecting exploitation of this vulnerability in production.
• Monitor for anomalous API activity. Unexpected exec calls or unusual connection patterns from low-privileged accounts are red flags worth alerting on.

Further Reading

• Original Naked Security (Sophos) report on CVE-2018-1002105
• Cloud Native Computing Foundation (CNCF)

Reduced IT Costs

Running physical servers on-site carries a layered set of costs that are easy to underestimate:

• Capital expenditure (CapEx): Server hardware, networking equipment, UPS units, and rack infrastructure all require upfront investment.
• Facilities overhead: On-premises servers need conditioned power, cooling, and physical security.
• Personnel: Dedicated sysadmin or IT staff are required to provision, monitor, patch, and replace hardware.
• Refresh cycles: Hardware depreciates and must be replaced on a predictable schedule, typically every three to five years.

Cloud providers convert most of these costs into a predictable operational expenditure (OpEx) subscription. You pay for the compute, storage, and network resources you actually consume, and the provider handles the underlying hardware lifecycle. For many small and medium-sized businesses this shift frees capital that can be reinvested in product development, hiring, or marketing.

Fast Mobile Access from Anywhere

Because cloud-hosted data lives on internet-accessible servers, authorised users can reach it from any device with a network connection—laptop, tablet, or smartphone. This matters most in scenarios such as:

• Field sales teams who need to pull up client records, enter leads, or check inventory while on-site with a customer.
• Remote or hybrid workers who require the same data access they would have in the office.
• Executives or managers who need to review dashboards or approve documents while travelling.

A well-configured cloud environment with role-based access controls (RBAC) and multi-factor authentication (MFA) ensures that this accessibility does not come at the expense of security.

Automatic Software Updates and Patching

Managing software updates on self-hosted infrastructure is labour-intensive. Patch cycles must be scheduled, tested, communicated to users, and monitored for regressions. Missed patches are one of the leading causes of security breaches.

Cloud-based Software as a Service (SaaS) and Platform as a Service (PaaS) offerings shift much of this burden to the provider. Infrastructure patches, security updates, and feature releases are applied by the vendor, often transparently and without service interruption. Your team benefits from the latest functionality and security fixes without the overhead of a manual update process. This is particularly valuable for security-critical software such as databases, authentication services, and operating system kernels.

Important caveat: Automatic updates apply primarily to managed cloud services. If you run virtual machines (IaaS) in the cloud and self-manage the operating system and application stack, patching remains your responsibility.

Simplified Team Collaboration

Effective collaboration depends on all team members being able to access the same, current version of a document or dataset. Traditional file-share approaches—emailing attachments, shared network drives accessible only from the office network—create version conflicts and bottlenecks.

Cloud-based collaboration tools solve these problems at the architecture level:

• Single source of truth: Documents stored in cloud storage (for example, Google Drive, SharePoint, or an S3-backed content management system) have one canonical version that all collaborators edit simultaneously.
• Real-time co-editing: Multiple team members can work on the same file concurrently, with changes visible to all in near real time.
• Audit trails: Version history is maintained automatically, making it straightforward to review changes or roll back to a previous state.
• Location independence: Distributed teams across time zones can hand off work seamlessly without transferring files manually.

Additional Considerations

The four benefits above are among the most commonly cited, but cloud computing also offers advantages in disaster recovery (data is replicated across geographically distributed data centres), scalability (resources can be scaled up or down on demand), and compliance (many providers maintain certifications such as SOC 2, ISO 27001, and HIPAA BAAs that can support your own compliance posture).

No solution is without trade-offs. Cloud costs can escalate if resource consumption is not monitored, vendor lock-in can limit flexibility, and a reliable internet connection becomes a hard dependency. A thorough cost-benefit analysis that accounts for your actual workload, data transfer patterns, and compliance requirements is essential before committing to a provider or architecture.

Original concept sourced from myasbn.com. This article has been independently rewritten and expanded for a technical audience.

The right approach lets a business modernise its mainframe estate and benefit from cloud economics—elasticity, pay-as-you-go pricing, and modern tooling—while retaining the intellectual property embedded in decades of proven application code.

Core Requirements for Any Mainframe-to-Cloud Solution

Before evaluating specific migration paths, organisations should establish non-negotiable baseline requirements. A viable cloud solution for mainframe applications must be:

• Cost-effective — ideally shifting capital expenditure (CapEx) to predictable operational expenditure (OpEx).
• Secure — meeting or exceeding the security posture of the existing mainframe environment.
• Reliable — preserving the high availability and resilience that mainframe users typically expect.
• Modernisation-ready — enabling the business to adopt current and future development practices, tools, and languages.

The Three Mainframe Migration Strategies

When planning a mainframe-to-cloud migration, there are three primary options. Each carries a different risk profile, cost, and level of disruption to existing business logic.

1. Rewrite

The existing application functionality is recreated from scratch in a language more typical of cloud-native development (Java, Python, Go, etc.). This addresses concerns around the shrinking pool of COBOL and assembler developers, but it comes at a significant cost. A rewrite requires substantial investment in engineering time without necessarily adding new features or functionality. The risk of regression—failing to replicate complex, battle-tested business logic faithfully—is high. Rewrites frequently overrun budgets and timelines.

2. Replace

Organisations can implement a Software-as-a-Service (SaaS) solution to replace the current system entirely. This creates an opportunity to review and modernise business processes simultaneously. However, for all but the most commodity workloads, off-the-shelf SaaS products rarely replicate the depth of functionality built up over years in a custom mainframe application. Replacing a highly customised system often means accepting reduced capability or surrendering the competitive differentiation that the original application provided.

3. Rehost (Lift and Shift)

Rehosting moves mainframe applications to a cloud platform with minimal or no code changes. Specialised platforms can emulate the mainframe runtime environment—including COBOL execution, JCL job scheduling, VSAM data access, and CICS transaction processing—on standard cloud infrastructure. This approach preserves existing competitive advantage and lets the business continue to run proven code while incrementally modernising over time. The main challenge is organisational and political: mainframe decisions often involve entrenched stakeholders, vendor relationships, and legacy procurement contracts.

Combining Strategies: Replace Plus Rehost

For most organisations, a hybrid approach—replacing commodity components with SaaS where appropriate, and rehosting the core business-logic applications unchanged—delivers the greatest practical benefit. This allows the business to deploy modernised application components to the cloud or an alternative platform based on business and operational requirements, without a high-risk big-bang rewrite.

Benefits of Moving Mainframe Workloads to the Cloud

When executed correctly, migrating mainframe applications to a cloud environment can unlock a broad set of operational and financial benefits:

• Reduced risk — Retaining existing intellectual property and competitive advantage keeps change incremental and risk manageable. There is no need to recreate decades of proven business logic.
• Flexibility — Cloud platforms offer a wide range of virtual environments, and resources can be provisioned or decommissioned on demand rather than according to hardware procurement cycles.
• Faster time to market — Moving test and development environments to the cloud allows teams to provision mainframe-compatible instances in minutes rather than days. This enables genuine mainframe DevOps: faster iteration between development, test, and production teams.
• Cost optimisation — Shifting development, test, and production workloads to the cloud reduces dependency on proprietary mainframe hardware and software licensing. Costs become OpEx rather than CapEx, improving budget predictability.
• Reliability — Major cloud providers offer highly resilient infrastructure with automated failover, geographic redundancy, and strong SLA commitments.
• Scalability — Cloud environments support automatic horizontal and vertical scaling and load balancing, enabling workloads to grow without manual infrastructure intervention or performance degradation.
• High performance — Cloud platforms offer multiple instance types optimised for different compute-to-I/O ratios, allowing organisations to match infrastructure to workload characteristics.
• Security — Rehosted applications can be integrated into a standard cloud security stack, including identity and access management, encryption at rest and in transit, audit logging, and multi-factor authentication—in some cases improving on the security controls available in legacy mainframe environments.

Practical Considerations: Mapping Mainframe Services to the Cloud

A successful migration requires a solution that maps core mainframe services—transaction monitors, job schedulers, data access methods, and security subsystems—to their cloud equivalents, while allowing the application code itself to run unchanged. Getting this mapping right is what makes rehosting viable and what distinguishes it from a full rewrite. Organisations that achieve this can unlock significant savings through reduced operating costs, improved developer productivity, and better system performance.

Moving to a platform such as Microsoft Azure or a comparable cloud provider also frees businesses from the constraints of proprietary mainframe hardware refresh cycles, giving them faster access to infrastructure, greater scalability, higher availability, and improved business continuity—all hosted in highly secure, certified data centres.

Case Study: Migrating COBOL Payroll Processing to the Cloud

A German public-sector IT services organisation provides payroll processing to its clients, handling salary payments for approximately 420,000 individuals every month. The payroll system relied heavily on COBOL for core business logic, running on mainframes. Constantly changing salary rules, tax rates, and regulatory requirements meant the team needed to update software frequently, but the mainframe environment slowed development cycles.

To respond more quickly to client requests and adopt modern development tooling, the organisation chose to migrate its COBOL applications from the mainframe to a cloud environment using a rehosting approach. By moving the COBOL code without rewriting it, the team could migrate the most critical business logic incrementally and with low risk—one component at a time.

The migration was completed quickly, giving the business a stable, future-proof platform. Development teams could now iterate faster using modern IDEs and CI/CD pipelines while the proven COBOL logic continued to execute correctly. As the organisation’s public-sector clients continue to adopt digital workflows, the modernised platform allows the team to deliver high-quality, reliable services while protecting the integrity of critical HR and payroll processes.

Summary: Choosing the Right Path

There is no single correct answer for mainframe modernisation. Rewriting is high-risk and expensive. Replacing with SaaS may sacrifice competitive differentiation. Rehosting preserves proven code but requires organisational buy-in and a platform that genuinely supports mainframe runtime semantics. For most enterprises, a pragmatic combination—rehosting core applications and replacing commodity functions—provides the best balance of speed, risk, and long-term flexibility. The goal is not to abandon what works, but to give proven applications a modern, scalable foundation from which the business can continue to evolve.

Why Data Masking Performance Matters for DevOps

As organizations capture more customer data, the volume that needs to be masked before it reaches non-production environments grows proportionally. At the same time, cloud adoption, database migration projects, big-data platforms, and shorter release cycles all increase the frequency with which masked copies must be generated. A masking job that takes 18 hours may have been acceptable when releases shipped quarterly; it becomes a blocker when teams deploy daily.

The goal is to provision masked datasets that are:

• Contextually accurate — data types, referential integrity, and value ranges must remain realistic so that tests are meaningful.
• Provably de-identified — original sensitive values must not be recoverable, satisfying compliance requirements.
• Available quickly — masked copies must be ready within whatever SLA window the development pipeline requires.

How Data Masking Performance Is Measured

The most common unit cited by practitioners is rows per second. It is intuitive, but it can be misleading because row width varies significantly across tables. A table with four narrow columns processes very differently from one with 60 wide columns, even if both have the same row count. When comparing solutions or planning capacity, always qualify throughput figures with the average row size and column count of the tables being measured.

Because data masking is a batch process that reads, transforms, and writes large volumes of data, total elapsed time—not just rows per second—is the metric that determines whether a masking run fits inside an overnight or weekend maintenance window. Both metrics should be tracked.

Three Variables That Drive Masking Runtime

Regardless of the tool chosen, three categories of variables dominate masking performance: database characteristics, hardware configuration, and masking configuration. Optimizing across all three is more effective than tuning any single one in isolation.

1. Database Characteristics

The shape of the database tables being masked has the most direct impact on runtime:

• Tall tables (high row counts) — more data elements to process; runtime scales roughly linearly with row count when other variables are held constant.
• Wide tables (many columns) — even if only a small number of columns require masking, wide rows introduce I/O overhead because the full row must be read and rewritten during transformation.
• Referential integrity and dependencies — foreign-key relationships require masking to be applied in a specific order or with coordinated lookups, which can serialise otherwise parallel operations.
• Data types — large object (LOB) columns, JSON blobs, and encrypted fields each impose their own I/O and CPU costs.

Involving a subject-matter expert who understands the production data model early in the masking project helps scope realistic performance expectations and identify problem tables before the first full run.

2. Hardware Configuration

Data masking is an I/O-heavy batch workload. Most production database servers are tuned for online transactional processing (OLTP)—many small, random reads and writes. Masking is the opposite: large sequential reads followed by large sequential writes. This mismatch means that running masking directly against a production-tuned server without adjustment often yields poor throughput.

Key hardware considerations for a dedicated masking or staging server:

• Storage speed and layout — faster storage (NVMe or SSD RAID arrays with reads/writes spread across multiple disks) delivers measurably better throughput than spinning disks. Co-locating the staging server in the same data centre as the source database eliminates network latency from the critical path.
• RAM — sufficient memory allows the masking engine to buffer data in-process and reduces disk I/O round-trips. Allocating RAM to the masking engine rather than relying on OS-level disk caching gives the tool more predictable performance.
• CPU cores — parallelisation strategies (see below) are only useful if enough cores are available to run multiple threads simultaneously.

When SLA windows are tight, provisioning appropriately sized hardware for the staging tier is the highest-leverage, lowest-complexity change available. Most organisations already have access to the infrastructure needed; the key is to size it for a batch workload rather than an OLTP workload.

3. Masking Configuration

The scope and complexity of the masking rules applied directly affect runtime:

• Volume of masked columns — in a typical sensitive-data profile, masking rules apply to roughly 15–20% of tables. Stricter compliance requirements or broader data classification can push this toward 30–40% of tables, proportionally increasing runtime.
• Transformer type — different transformation algorithms have different CPU and I/O profiles:
  • Data Generators synthesise entirely new fictional values (e.g., valid-format credit card numbers, phone numbers). These are CPU-bound but require no external lookup.
  • Data Loaders / Shufflers substitute values from a predefined reference set (e.g., replacing real names with names drawn from a lookup table). These introduce additional I/O for the reference data reads.
• Transformer selection — where the business requirement can be satisfied by more than one transformer type, selecting the lighter-weight option can reduce runtime without compromising the compliance outcome.

Techniques for Improving Masking Throughput

Once the three core variables are understood and appropriately configured, there are additional architectural and tool-level techniques that can push throughput further:

Multi-Threading and Parallelisation

Running masking operations on multiple database columns or tables concurrently is the most direct way to scale throughput. Modern masking tools support configurable thread counts so that independent tables are processed in parallel, subject to referential-integrity ordering constraints. On a well-provisioned staging server, increasing thread count can reduce total elapsed time significantly, though the gains flatten once CPU or I/O is saturated.

Bulk SQL Operations

Row-by-row UPDATE statements generate substantial transaction-log overhead. Replacing individual DML statements with bulk SQL operations—set-based UPDATEs, bulk inserts, or direct-path writes depending on the database engine—reduces logging overhead and improves throughput. Some masking tools apply bulk SQL automatically based on database type and operation; others require explicit configuration.

Executing Transformations on the Database Tier

Where the masking engine can push transformation logic directly to the database server rather than pulling data across the network, transforming it on the application server, and writing it back, network round-trips are eliminated. Keeping data movement local to the database tier can be a substantial performance gain for large tables, especially over slow or high-latency network links.

Storage Architecture Alignment

As noted above, masking is a batch workload. If the staging database is configured with OLTP storage settings (small block sizes, heavy logging, synchronous commits), reconfiguring it for bulk-friendly I/O patterns—larger block sizes, minimised logging where the database engine permits—can improve throughput. This is a database-administrator-level change that requires careful review to avoid affecting production systems, but it is low-risk on a dedicated staging instance.

Slowing Down to Speed Up: The Implementation Discipline

The counterintuitive lesson practitioners learn repeatedly is that rushing the planning phase leads to slow masking jobs that are difficult to tune retroactively. Investing time upfront in the following activities pays dividends in predictable, faster runtimes:

1. Profile the data model — identify the tallest and widest tables, locate LOB columns, and map referential-integrity chains before writing a single masking rule.
2. Define the compliance scope precisely — work with legal, compliance, and data-owner stakeholders to agree on exactly which columns require masking. Avoid masking everything out of caution; every additional column has a runtime cost.
3. Right-size the staging server — specify hardware based on the actual data volume and target SLA window, not on a generic template. Document the specification so it can be reproduced or scaled.
4. Select transformers intentionally — for each column, choose the transformer that meets the compliance requirement with the lowest performance overhead. Document the rationale so future changes are deliberate.
5. Benchmark iteratively — run masking jobs on a representative subset, measure throughput, identify bottlenecks, tune one variable at a time, and re-measure. This produces a calibrated model that can be used to predict runtime for full production volumes.

Key Takeaways

• Masking throughput is affected by database shape, hardware configuration, and masking rule complexity—optimise all three.
• Right-size staging server hardware for a batch I/O workload, not an OLTP workload. Co-locate it with the source database.
• Use multi-threading, bulk SQL, and on-database-tier execution to push throughput higher once the fundamentals are in place.
• Faster storage—spread across multiple disks, ideally SSD or NVMe—has a disproportionate positive impact on masking runtime.
• Scope masking rules precisely; every additional column or table has a measurable runtime cost.
• Benchmark on representative data subsets early, tune iteratively, and document the configuration so results are reproducible.

This article is based on a post originally published on Security Boulevard (2018). Product-specific details relate to Imperva Camouflage as described in the original source; verify current feature availability with the vendor.

The latest solution from Redgate Software, known for its industry leading database development tools, promises to resolve much of the headache the General Data Protection Regulation (GDPR) is already causing. SQL Provision launches today and allows users to create copies of SQL Server databases in seconds which use a fraction of disk space, and mask any sensitive data to address data privacy and protection issues.

The provisioning of database copies to improve the efficiency and quality of database development and testing is becoming increasingly common as companies and organizations seek to introduce DevOps practices. Working with a copy of the production database enables developers to find errors faster and prevents problems when changes are deployed.

It does, however, take a long time and huge amounts of disk space to provision copies, so old copies are often used, which become increasingly unreliable over time and are difficult to keep track of, especially where multiple developers are involved. Coupled with this, the implementation of the GDPR in May 2018 is making the protection of sensitive data a priority for almost every company and organization.

SQL Provision has been created to support a database DevOps approach while keeping GDPR compliance central to the process. Using Data Masker, sensitive data can be anonymized or replaced with realistic data, helping to satisfy the requirements of regulations like GDPR, HIPAA and SOX. The virtual cloning technology of SQL Clone can then create full database copies in seconds using just a few MB of local disk storage, giving developers a truly realistic copy, while keeping confidential data protected.

The masking and provisioning of database copies is also managed from one place, so organizations can control their distribution and know when copies were provided, to which developers or teams of developers, and the precise data that was masked.

“The closer we get to GDPR, the bigger the issue of data privacy becomes,” says Richard Macaskill, Redgate Product Manager. “Companies need solutions that free them to take advantage of DevOps in their database development process, yet enable them to be truly compliant.”

This is particularly important because Redgate’s recent SQL Server Database Provisioning Report found that 83% of respondents agreed or strongly agreed that it is desirable to use production data in development and testing. With SQL Provision, they can continue to do so, knowing that data security, regulatory compliance and storage concerns have been accommodated.

Redgate has also deliberately priced SQL Provision so that small and medium sized enterprises can take advantage of it alongside large corporations. The tiered pricing model is based on the total size of the databases organizations need to provision, rather than the number of users.

As Richard Macaskill concludes: “Provisioning solutions have typically been restricted to big companies in the past. Every company and organization of whatever size will need to comply with the GDPR, so it makes sense to give them the opportunity to do so.”

The development teams behind Data Masker and SQL Clone which comprise the SQL Provision solution are continuing to refine the tools to streamline data masking and database provisioning yet further.

The Security Blind Spot Inside Your DevOps Pipeline

The shift toward continuous delivery and DevOps has delivered genuine speed and agility to software organizations. But it has also quietly created one of the most underappreciated attack surfaces in the modern enterprise: the sprawl of machine identities, privileged credentials, and automation secrets that power CI/CD pipelines, cloud environments, and containerized workloads.

Unlike a compromised human account, a compromised automation credential can be replicated and exploited at machine speed — across every system the automation touches. Traditional security programs, designed around human-operated systems, have simply not kept pace.

What Are DevOps “Secrets” — and Why Do They Matter?

In DevOps contexts, a secret is any credential or token that grants programmatic access to a system. Common examples include:

• Privileged account passwords used by automation scripts to access servers or databases
• SSH keys that authenticate machine-to-machine connections
• API keys and tokens used by microservices, CI/CD tools, and third-party integrations
• Service account credentials embedded in configuration management tools like Ansible, Chef, or Puppet

As organizations adopt cloud environments and DevOps pipelines, these secrets multiply rapidly. Automation tools enable machine-to-machine interactions without human intervention, relying on machine identities rather than human ones. Every new service, container, or microservice potentially introduces new credentials — and new attack vectors.

While this further helps accelerate the pace and agility of compute environments, it also increases and dynamically creates credentials, secrets and access points – expanding the attack surface, with increased numbers of privileges and credentials, while also enabling vulnerabilities to potentially be more rapidly exploited.

CyberArk, Threat Landscape Report 2018

The Scale of the Problem: What the Data Shows

CyberArk’s survey data paints a stark picture of how far security practice lags behind DevOps adoption:

• 38% of respondents did not identify cloud environments, containers, CI/CD tools, microservices, or source code repositories as locations where secrets exist in their organization — despite nearly every component of a DevOps ecosystem relying on them.
• 75% of security respondents reported that their organization had not implemented a privileged account security solution for DevOps.
• 60% of DevOps respondents admitted to storing privileged account or administrative passwords in a document on a company PC or laptop — unmanaged, unsecured, and high-value targets.
• Fewer than half (46%) of DevOps respondents said their security and DevOps teams were well integrated throughout the development process.
• 43% said the security team is brought in only at the end of each development cycle.

These figures highlight a systemic gap: organizations are automating faster than they are securing.

Why Automation Amplifies Security Risk

The core danger is a force multiplier effect. Elizabeth Lawler, then VP of DevOps Security at CyberArk, framed it plainly:

The fundamental shift that we recognised early on was that we saw the role of the privileged or super user change. That is the super-powerful system administrator of yore is now a set of coded scripts, and you have very little insight or ability to control it. This often leads to things like embedded passwords in these scripts, or privileges and access tokens… If they become compromised, then you have a very large attack surface for a malicious hacker. That is fundamentally the risk that people are taking on now when they’re doing DevOps.

Elizabeth Lawler, VP of DevOps Security, CyberArk

Three structural factors make the problem especially difficult to manage:

1. Workloads are moving to the cloud. Critical and regulated workloads increasingly run in environments where the traditional network perimeter no longer applies, adding complexity to access control.
2. Containers and microservices scale faster than humans can monitor. Ephemeral workloads spin up and down so quickly that manual oversight is impractical — yet each instance may carry its own credentials.
3. Automation fills gaps humans used to occupy. Configuration management tools, deployment scripts, and orchestration platforms perform tasks previously done by people — and if those tools are compromised, attackers inherit everything they could do.

Industry Variation: Who Is Most at Risk?

The CyberArk report found significant variation in how well DevOps and security teams collaborate across industries — with some counterintuitive results:

• Consumer services, technology, and telecommunications showed the closest partnerships between DevOps and security teams.
• Financial services — arguably among the most security-sensitive sectors — reported slightly below-average collaboration.
• Healthcare was the most concerning: only 16% of healthcare respondents believed their security and DevOps teams were well integrated, despite operating under strict regulatory requirements around data protection.

The implication is clear: heavy regulation alone does not produce good security integration. Culture, tooling, and process changes are also required.

What Good DevOps Security Actually Looks Like

Lawler argues that the answer is not to bolt security on at the end of the pipeline, nor to embed it invisibly within other tools. Instead, organizations need a dedicated, purpose-built approach to secrets management that is explicitly separate from the automation it governs:

There needs to be a bridge between this desire for velocity – for high-velocity software development and scaling – and security compliance, knowledge, and transparency. And those two don’t have to be a mismatch if you can get the foundational technologies right. With the administrator, which could be code, and the security that watches the administrator, which can also be code, there need to be checks and balances against each other, and therefore they need to be separated via privileged account and secrets management.

Elizabeth Lawler, VP of DevOps Security, CyberArk

In practical terms, this means:

• Adopt a dedicated secrets management solution (such as HashiCorp Vault, CyberArk Conjur, or AWS Secrets Manager) rather than relying on embedded credentials in scripts or configuration files.
• Rotate credentials automatically so that even if a secret is exposed, its useful lifetime is short.
• Audit and inventory all secrets across cloud environments, containers, CI/CD tools, microservices, and source code repositories — the 38% of organizations that have not done this are flying blind.
• Integrate security into the pipeline from day one, not as a final gate before deployment. This is the foundational principle of DevSecOps.
• Ensure security and DevOps teams collaborate continuously throughout design, development, testing, and deployment — not just at the end of a release cycle.

The Bottom Line

DevOps automation is not inherently insecure — but it demands a fundamentally different security model than traditional IT environments. The combination of machine-speed propagation, ephemeral infrastructure, and widespread secrets sprawl creates an attack surface that grows faster than most security teams recognize.

The organizations that will navigate this best are those that treat secrets management as a first-class engineering concern from the start, integrate security deeply into their DevOps culture and toolchain, and resist the temptation to treat a password in a text file as an acceptable solution just because it is convenient.

As Lawler put it, velocity and security do not have to be in conflict — but only if you get the foundational technologies right.

Cloud Computing Is Everywhere—and So Are Its Risks

If you use Facebook, Gmail, Dropbox, Slack, Google Drive, Spotify, or Snapchat, you are already a cloud computing user. Cloud computing provides on-demand access to shared resources—networks, servers, storage, applications, and services—allowing individuals and organizations to offload data storage and application hosting at relatively low cost. For businesses, this means reduced development and maintenance overhead. For individuals, it means virtually unlimited storage and always-available services.

But the convenience of cloud computing comes with a significant trade-off: security and privacy. As more sensitive data migrates to the cloud, the attack surface grows—and the consequences of a breach can be enormous.

Data Breaches Are Not Exceptional—They Are Routine

Common protective measures—firewalls, virtualization, and regulatory policies—provide layers of defense, but they are far from airtight. A fundamental problem is that users are frequently required to provide data to cloud service providers in plaintext, meaning without encryption at the point of processing. Because cloud software and hardware contain bugs and misconfigurations, sensitive information can be exposed to unauthorized users, third-party applications, or malicious actors.

The Yahoo breaches illustrate how severe the consequences can be. In September 2016, Yahoo disclosed that a 2014 breach had exposed data belonging to approximately 500 million accounts. That estimate was revised in December 2016 to 1 billion accounts from a separate 2013 incident. By October 2017, Yahoo revised the figure again—this time to 3 billion accounts, making it one of the largest data breaches ever recorded. Compromised data included names, email addresses, passwords, and security questions and answers.

The Yahoo case is extreme in scale, but breaches of cloud-stored data occur regularly across organizations of all sizes. The pattern underscores a systemic problem: trusting the cloud provider to keep data confidential is an assumption that the modern threat landscape can no longer support.

The Problem With the Traditional Security Model

Traditional enterprise IT security operates on the assumption that internal servers can be trusted. In a closed, perimeter-defended network, this made sense. The cloud environment is fundamentally different: it is open, heterogeneous, and distributed. Data may be stored and processed across multiple jurisdictions, on hardware shared with unknown tenants, managed by staff you have no visibility into.

Applying the old trust model to this environment creates a dangerous mismatch. If a cloud server is compromised—whether by an external attacker, a rogue insider, or a software vulnerability—all data held in plaintext on that server is immediately exposed. Perimeter security cannot prevent this.

A Data-Centric Security Model: Protect the Data Itself

A more resilient approach shifts the trust boundary. Rather than assuming the cloud infrastructure is safe, this model embeds protection mechanisms directly into the data—primarily through encryption and cryptographic authentication. Under this model, even if the cloud server is fully compromised, the data remains protected because it was never decryptable by the server in the first place.

This approach requires solving a difficult technical problem: how do you allow authorized users to search, share, and process encrypted data without ever decrypting it on the server? Research in this area focuses on techniques such as:

• Homomorphic encryption – computations performed on ciphertext that produce encrypted results, decryptable only by authorized parties.
• Searchable encryption – allows a server to search encrypted data without learning its contents.
• Attribute-based encryption (ABE) – access to decrypted data is controlled by cryptographic policies tied to user attributes, not by server-side access control lists.

Attribute-Based Encryption: How It Works

One concrete implementation of this data-centric model is an attribute-based secure messaging system. The system is designed to deliver end-to-end confidentiality for enterprise users, operating under the explicit assumption that the cloud messaging server itself cannot be trusted to keep messages private.

Here is how the core concept works:

1. Every user is assigned a set of attributes. For example, Alice’s attributes might be student, school-of-business and Bob’s might be student, school-of-information-systems.
2. A trusted keymaster issues each user a unique decryption key derived from their attributes. The keymaster does not need to be co-located with the cloud server.
3. When a user sends a message, they encrypt it and attach an access policy. For instance, a message might carry the policy all-students or business-school-students. The resulting encrypted message is called a ciphertext.
4. Only users whose attributes satisfy the policy can decrypt the message. If the policy is all-students, both Alice and Bob can decrypt it. If the policy is business-school-students, only Alice can.
5. The cloud server never holds the decryption keys. It distributes ciphertexts without ever being able to read them.

A useful analogy: imagine depositing valuables in houses distributed across a city. Each house key is unique to its owner, and each owner’s key only opens houses their access level permits. A keymaster elsewhere manages who gets which key. Even if a house is burgled, a thief without the right key cannot access the valuables inside.

A key efficiency advantage of this approach is that only one ciphertext needs to be generated and delivered, regardless of how many recipients match the access policy. This avoids the overhead of encrypting a separate copy for each recipient—making it practical at scale.

What This Means for Cloud Security in Practice

The attribute-based model addresses several real-world cloud security challenges simultaneously:

• Server compromise does not equal data compromise. Even a fully breached server exposes only ciphertext that is computationally infeasible to decrypt without the correct keys.
• Access control is cryptographically enforced. Unlike server-side ACLs that can be misconfigured or bypassed, ABE policies are enforced by mathematics.
• Scalable access management. Adding or revoking access does not require re-encrypting data for every user—only policy and key management at the keymaster level changes.
• Works over untrusted networks. Confidentiality is preserved even if the communication network itself is open or monitored.

The Road Ahead

Cloud security is not a problem that can be solved by adding another firewall rule or compliance checkbox. As cloud environments become more distributed—spanning multi-cloud architectures, edge computing, and increasingly complex supply chains—the attack surface will continue to expand. The assumption that cloud infrastructure can be trusted must be replaced with architectures that are secure even when it cannot.

Data-centric approaches like attribute-based encryption represent a meaningful step in that direction. They shift the security model from “protect the perimeter” to “protect the data”—a fundamental rethink that the scale and openness of modern cloud computing demands.

For DevOps and security engineers, the practical takeaway is straightforward: evaluate your cloud architecture against the assumption that your provider could be compromised. If that scenario results in plaintext data exposure, your current model has a structural gap that perimeter controls alone cannot close.

Two aspects in particular deserve careful evaluation before you commit to a cloud provider or deployment model: shared access (multi-tenancy) and virtualization exploits. Understanding these factors will help you choose the right cloud model and negotiate appropriate security controls with your provider.

Why Cloud Security Deserves Upfront Scrutiny

The growth of big data means every organisation—regardless of sector—needs a scalable, cost-effective way to store and process large volumes of data. Cloud storage satisfies that need, but the cost and scalability advantages come with a different security posture compared to on-premises infrastructure. Before selecting a deployment model (public, private, hybrid, or multi-cloud), you should map your own compliance requirements, data sensitivity classifications, and risk tolerance against what each model actually provides.

1. Shared Access and Multi-Tenancy

Multi-tenancy is a foundational characteristic of public cloud environments. Multiple customers—often from entirely different industries—share the same underlying physical compute, storage, and networking resources. The cloud provider uses logical isolation (hypervisors, software-defined networking, access control policies) to keep each tenant’s data and workloads separate.

This model is efficient and economical, but it introduces risks that a dedicated private infrastructure does not:

• Noisy neighbour effects: A neighbouring tenant’s resource-intensive workload can affect your application’s performance, even when data isolation is maintained.
• Misconfiguration risk: Shared environments are complex. A misconfigured access policy on your side—or, historically, on the provider’s side—can expose data to other tenants.
• Compliance complications: Regulated industries (healthcare, finance, legal) may have data-residency or isolation requirements that a standard public cloud multi-tenant tier does not satisfy without additional configuration or a dedicated offering.

Practical Mitigations for Shared Access Risk

• Encrypt data at rest and in transit. Even if logical isolation fails, encrypted data is significantly harder to exploit. Manage your own encryption keys where possible (customer-managed keys / BYOK).
• Use VPN tunnels or private connectivity options (such as AWS Direct Connect, Azure ExpressRoute, or Google Cloud Interconnect) to keep traffic off the public internet.
• Apply the principle of least privilege to IAM roles, storage bucket policies, and network security groups so that a misconfiguration limits rather than fully exposes your blast radius.
• Review your provider’s compliance certifications (SOC 2 Type II, ISO 27001, PCI DSS, HIPAA BAA, etc.) and understand which controls are the provider’s responsibility versus yours under the shared responsibility model.

2. Virtualization Exploits

All major cloud providers depend heavily on virtualization. Physical servers run hypervisors that host many virtual machine (VM) instances simultaneously. This architecture is efficient, but it adds an attack surface that does not exist on bare-metal infrastructure.

Virtualization exploits generally fall into four categories based on where the attack originates and where it lands:

Many of these vulnerabilities are not publicly known at the time they are exploited (zero-days), which means cloud providers may not have modelled them in their published risk frameworks. High-profile CPU-level vulnerabilities such as Spectre and Meltdown demonstrated that even hardware-level isolation assumptions can be violated in shared virtualized environments.

Practical Mitigations for Virtualization Risk

• Ask your provider about hypervisor patch cadence and how quickly critical virtualization CVEs are remediated across the fleet.
• Consider dedicated host or bare-metal options if your workload is particularly sensitive; these eliminate same-host neighbour risk at higher cost.
• Enable memory encryption where available (e.g., AMD SEV or Intel TDX on supported instance types) to protect in-use data from host-level inspection.
• Monitor VM behaviour with host-based intrusion detection and cloud-native security services (AWS GuardDuty, Azure Defender, GCP Security Command Center) to detect anomalous patterns that could indicate an exploit attempt.
• Keep guest OS and application stacks patched—many guest-to-host escapes require a vulnerable guest OS component as the initial vector.

Choosing the Right Cloud Model for Your Risk Profile

Once you understand these two risk categories, you can make a more informed choice about deployment model:

• Public cloud — lowest cost, highest multi-tenancy risk; suitable when data sensitivity is low or when strong encryption and access controls are applied consistently.
• Private cloud — dedicated infrastructure, eliminates most multi-tenant risk; higher cost and operational overhead.
• Hybrid cloud — keeps the most sensitive workloads on-premises or private infrastructure while leveraging public cloud for less sensitive or elastic workloads.
• Multi-cloud — distributes risk across providers but increases operational complexity and may complicate security monitoring.

Key Takeaways

Cloud computing offers compelling cost and scalability benefits, but informed adoption requires understanding the platform’s security assumptions rather than simply accepting the provider’s marketing claims. Multi-tenancy and virtualization are not flaws—they are intentional design choices that enable cloud economics—but they carry inherent risks that every business owner and IT decision-maker should evaluate against their own threat model. Doing that homework before you migrate is far cheaper than responding to a breach after the fact.

The Core Challenge: Surrendering Control to a Third Party

Security is the primary mandate of any government agency, which makes the question of cloud adoption particularly fraught. The most fundamental concern is straightforward: moving highly sensitive data to the cloud means surrendering direct control to a third-party provider. Compounding this, data routinely flows between cloud providers and across national boundaries — running directly counter to the physical security measures government agencies are required to maintain.

The attack surface exposed by cloud adoption is substantial. Mobile device complexity, shared infrastructure, multi-tenant environments, and cross-border data flows all create vectors that do not exist in fully air-gapped, on-premises systems. For government agencies handling classified or operationally sensitive data, each of these factors must be addressed systematically before any migration can be considered.

Executive and IT Concerns: What the Data Shows

A Cloud Security Alliance (CSA) survey found widespread anxiety among executives and IT managers about cloud data security. According to the report:

• 73% of respondents said security concerns were holding them back from adopting cloud computing.
• 38% cited regulatory compliance as a major barrier to cloud adoption.
• 38% expressed concern about losing control over IT services.

These figures reflect the reality that government IT environments operate under constraints that commercial organizations do not — legal mandates, classification regimes, and accountability structures that make the standard commercial cloud model a poor fit without significant customization and oversight.

Securing Government Data in the Cloud: The DoD Framework

To provide a structured approach to these concerns, the U.S. Department of Defense (DoD) released the Cloud Computing Security Requirements Guide (SRG), an unclassified document outlining the essential components for secure cloud computing in a government context. The SRG is designed to simplify security requirements for both the DoD and cloud providers, who must attest to, control, monitor, and provide evidence of data separation.

The framework uses a tiered impact level model that categorizes data records according to their sensitivity:

• Low impact (public data): Non-sensitive, unclassified information — such as data available through the Freedom of Information Act or hosted on public-facing websites — can be stored in commercial clouds that meet the baseline standards of the Federal Risk and Authorization Management Program (FedRAMP).
• Higher impact (sensitive but unclassified): As sensitivity increases, physical security requirements intensify. Data must be housed in environments with stricter access controls, personnel vetting, and hardware separation.
• Secret classification and above: A public cloud is not appropriate. Data at this level must reside on-premises or in private, government-owned cloud infrastructure — not commercial cloud services.

Enclaves that handle sensitive data must be architecturally isolated. Personnel with access must hold appropriate government clearances, and physical access controls must be strictly enforced. These enclaves are physically separated within a data center and do not share hardware, applications, or other resources with other tenants — a direct departure from the shared-resource model that makes commercial cloud economically attractive.

Physical Cloud Security Controls

Air-Gapped Systems

Most government agencies take a risk-averse posture toward cloud security. A common physical control is the air gap — physically separating a secured network from an unsecured one so there is no direct electronic path between them. Air-gapped systems are also found in major financial institutions, stock exchanges, and industrial control systems in environments such as nuclear power plants.

While air gaps are highly effective at preventing remote intrusion, they introduce significant operational complexity. Transferring data between an unsecured and a secured network requires human intervention — manual processes that are inherently prone to error and can themselves become security vulnerabilities (for example, through the use of removable media).

Data Diodes

Data diodes are a widely used solution in high-security environments. They provide a secure, unidirectional channel through which data can only flow in one direction, ensuring that sensitive data on a secured network cannot be leaked back to an unsecured one.

Technically, data diodes work by converting Transmission Control Protocol (TCP) connections to User Datagram Protocol (UDP) at the hardware level, then converting them back on the receiving side. This tricks applications using the File Transfer Protocol (FTP) into believing a bidirectional connection has been established, while in practice data can only travel from the unsecured network to the secured one — never in reverse.

The trade-off is that data diodes are specialized hardware components that add cost and complexity to network architecture. However, for environments handling classified data, that cost is typically justified by the security guarantee they provide.

The Road Ahead: Evolving Threats and Compliance Gaps

Government security regulations establish a baseline — but baselines, by definition, lag behind the threat landscape. Cyberthreats evolve continuously, and regulatory frameworks must be flexible enough to adapt without requiring full legislative or policy cycles each time a new attack vector emerges. This structural inertia is one of the most significant long-term risks in government cloud security.

A specific gap worth noting is Identity and Access Management (IAM). In a government cloud context, IAM must be able to authenticate users from a single authoritative location and pass authentication credentials seamlessly between providers. Many major commercial cloud providers do not fully support this model out of the box, creating friction that can either block adoption or lead agencies to accept weaker authentication controls.

The volume and sophistication of attacks targeting government and military networks continues to grow. While highly classified data will not move to commercial cloud environments, there is a genuine opportunity for cloud technology to serve as a platform for intelligence sharing between allied nations — using private, government-owned cloud infrastructure that maintains physical and logical separation from commercial services.

Key Takeaways for DevOps and Security Engineers

• Not all data belongs in the same cloud tier. Government impact-level frameworks provide a useful model for any organization handling sensitive data — tiering storage and access controls by data sensitivity is sound practice regardless of sector.
• FedRAMP compliance is the minimum bar for government cloud workloads, not a comprehensive security strategy. Agencies must layer additional controls on top of baseline compliance.
• Physical controls (air gaps, data diodes) remain relevant for the highest-sensitivity workloads. Software-defined security cannot fully replace hardware-enforced unidirectionality in classified environments.
• IAM federation across cloud providers is an unsolved problem at scale for government environments. Engineering teams should evaluate identity brokering solutions carefully before committing to multi-cloud architectures in regulated contexts.
• Regulatory baselines age quickly. Build continuous compliance monitoring and threat modeling into your pipeline rather than treating a compliance audit as a one-time gate.