1. From Digital-first to AI-first: Organizations approaching AI implementation only as software deployment are facing challenges in extracting meaningful value. AI requires a shift in mindset from digital first to AI first, so that systems are not just AI-enabled but become AI-native. To unlock its true value, AI must be wired deeply into the core of the organization, into the workflows and the decision-making process so that systems are learning continuously and adapting by using its intelligence.

For instance, while working with a large banking customer we re-designed workflows to optimize them. One of the objectives was to decouple different channels to facilitate agentic AI to perform jobs such as updating a customer profile across channels and have a seamless view of customer engagement.

2. From bottom line to top line growth: It is time to reframe the AI value proposition as a core lever to accelerate business growth by empowering teams and processes. Focusing on AI as a tool for cost-savings and productivity limits the scope to create value. AI discussions focused on the bottom line also create uncertainty as it leads to the inevitable question of reducing employees. Often there are unrealistic expectations from cost-cutting gains which are hard to realize at scale, while growth-focused use cases tend to deliver more tangible impact.

Our experience find that when AI conversations are about empowering teams and creating possibilities such as hyper-personalized marketing and dynamic pricing mechanisms, AI experiences faster uptake. Initiatives tied to top-line growth—such as customer engagement, marketing automation, or contact center scalability—face fewer barriers as these use cases do not threaten existing jobs. Instead, it expands capacity and reach. For example, AI-powered agents may have lower conversion rates than humans, but they can operate at scale with no limit on outreach volume. Even marginal gains across a larger funnel contribute meaningfully to revenue.

3. From Large Language Models to Small Language Models: Organizations that have better navigated the AI curve understand the AI models must be tailored to the needs of the organization, and the accuracy of the outcomes is closely linked to the data it is fed. While it is tempting to rely on off-the-shelf LLMs, it is difficult to create differentiated value through these models. Small language models on the other hand are customized, vertical-specific, and tailored to the needs of the organization deliver impactful outcomes.

Working with a large B2B marketplace, we understood that our client needed a retrieval augmented generated (RAG) model that is built and trained on its platform-specific requirements. This is where the data strategy comes into play, and we helped the customer re-architect to meet AI-specific goals.

In another case, we have helped a banking customer to prepare the data for AI by starting with data classification. We embedded intelligence into the process to classify compliant-bound data and less sensitive data and built a solid foundation for the next steps. Once data was categorized, we could strategize how to harness the data and explore use cases for automation, analytics and AI in a structured and compliant manner. We empowered the customer to harness even non-critical data to identify opportunities for automation and enhance efficiency.

Thriving with an AI DNA

Implementing AI goes beyond layering intelligence on top of existing systems. Organizations must embrace an AI-native mindset and implant AI into the DNA of the organization—to include efforts towards re-designing the architecture, re-engineering workflows, and embedding intelligence into decision making.

Infusing AI into the way organizations think and act creates new possibilities. Forward-looking companies are rolling out AI deployments as a strategic initiative with a long-term vision of how it will shape business outcomes, while building robust data foundations with thoughtful implementations. By empowering employees to work alongside AI, organizations are crafting winning strategies by amplifying human ingenuity with AI.

BRIDGING GAPS IN DATA ASSURANCE – SIX CHALLENGES AND HOW TO SOLVE THEM

1 Poor Quality Data undermines trust and decision-making

When data is flawed, the best laid plans go haywire, however meticulous the execution may be. This can have far reaching consequences in different industries—in audit and tax, poor data quality can lead to non-compliance and misreported filings while poor quality data in insurance hampers risk assessment.

How NewVision Fixed: Implement quality gates across the data lifecycle for continuous data validation to catch errors early, build confidence and prevent downstream impact.

2. Lack of sufficient test data limits coverage and risk detection

Siloed data sources and regulatory restrictions such as HIPPA and GDPR create data paucity. It is difficult to get sufficient data such as frauds, anomalies in claims or production defects. Test coverage cannot simulate real-world complexities and becomes incomplete without large and diverse data sets.

How NewVision Fixed: Use AI-generated synthetic data to overcome lack of data diversity. AI uses advanced technologies such as LLMs and Generative Adversarial Networks to mimic the structure and property of real-world data without compromising privacy or data confidentiality.

3. Tool-specific automation leads to siloed validation, redundant effort, and fragmented visibility

While tool-specific point solutions accelerate tasks in isolated cases, it does not support holistic data validation. For example, ETL, BI and application teams will test data separately resulting in redundant  efforts, gaps in testing standards, delayed error detection, root cause analysis and impact assessment.

How NewVision Fixed: Implement unified testing automation framework across UI, API and data layers for reliable and scalable testing framework that cuts across tools, environments and workflows. It provides end to end visibility ensuring traceability, robust governance and higher re-usability of testing assets.

4. Manual report validation is slow, error-prone, and unscalable

Manual data checking across dashboards, applications and spreadsheet is time consuming and unscalable. As applications grow, complexities increase giving rise to inconsistent validation and absence of traceability.

How NewVision Fixed: Implement automated reporting with custom tooling and strategies with tailored QA frameworks mapped with unique business logic, thresholds and data relationships.

5. Overdependence on UI testing leads to slower feedback and fragile tests

UI testing cannot validate underlying data in the pipelines, ETL, API and databases, or test the business logic. UI test validates only what is visible on the screen.

How NewVision Fixed: Embrace a testing pyramid optimized for data-heavy applications with 70% data layer automation, 20% API, and 10% UI. Most data-heavy applications embed business logic at the ETL, aggregation and transformation stages, so focus testing on data ingestion, schema validation, integrity, consistency across sources, data drift.

6. Misaligned team skill sets result in poor automation adoption and inconsistent quality

Businesses often falter in acquiring the right automation and testing skills, leading to inconsistent test coverage and unreliable automation. For example, QA professionals with expertise in UI testing lack exposure in API testing and data validation.

How NewVision Fixed: Deploy a test skill pyramid including UI, API and data layers, supported by a structured organizational model to facilitate collaboration, ownership and continuous learning. A Center of Excellence provides training; organizes re-usable libraries and best practices; and maintains testing frameworks and tool alignment.

Strong Data Test Strategy is Key to Building Business Trust

Business decisions are only as good as the insights that power it. Today, trustworthy data is not just essential, but the cornerstone of a high-performance organization. If data is the new oil, trust is the new gold.

A digital strategy sans data assurance strategy will wobble as it scales and send ripples across the organization. As organizations scale AI-powered digitalization from pilot to production, the time to get started with data  assurance is now, sooner than later.

If you want to know more about data assurance or how NewVision can transform the data testing strategy, write to us at contact@newvision-software.com.

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Use Cases Where Agentic Automation is Creating Impact

From customer care, supply chain and cyber security, Agentic AI is the most watched application in AI development. According to Deloitte’s State of Generative AI report, 26% respondents have stated that their organizations are actively exploring Agentic AI applications and use cases.

NewVision’s team of experts have helped clients build autonomous multi-agent systems by adding role-specific agents such as customer-care specialists or financial analysts and by integrating with external APIs such as financial market data. Below we share our perspectives on new possibilities with Agentic AI in different applications.

Customer Support: A new level of efficiency with NLP capabilities, context-aware information retrieval and intelligent escalation with Agentic AI will allow customer service organizations to focus on higher value tasks such as empathy, escalations and relationship building.

Financial Services: Real-time monitoring of transactions to flag anomalies, security breaches and compliance, while faster data reconciliation of financial transactions will empower human analysts to focus on better strategic planning, risk mitigation and investment opportunities.

Supply Chain Management: High level of operational efficiency, cost savings and better preparedness with demand forecasting, dynamic route optimization and close co-ordination across the supply chain and logistics and transportation sector to navigate disruptions due to geo-political risks, natural calamities and volatility.

Cyber Security: With increased cyber security attacks, Agentic AI elevates the defense system with continuous monitoring and remedial action such as shutting down a vulnerable port, a compromised system, or denying access to intrusion. At the same time, it helps to fortify IT systems with predictive analysis and recommendations.

Scripting a Successful Autonomous Strategy  

Agentic AI is the most interesting amongst GenAI applications enabling AI to access multi-modal types of information and the ‘agency’ to act independently by orchestrating complex workflow, coordinating with other agents, while learning continuously based on feedback loop.

Agentic AI is a shift from a reactive mode to proactive mode and so organizations are seeking to create competitive differentiation by focusing on areas that are uniquely critical to the success of business. A strategic approach wherein automation is viewed as a continuum is necessary to systematically harvest the benefits of RPA, while experimenting with new capabilities of Agentic AI.

The move from automation to autonomy is inevitable and forward-thinking organizations are already exploring how AI agents can augment their teams. However, Agentic AI faces the same set of challenges as GenAI such as regulatory uncertainty, building trust and transparency, data deficiency and talent issues and must design a comprehensive strategy that carefully considers the challenges while navigating the journey from automation to autonomy.

Digitalization is entering into a new phase of evolution—one in which AI is continuously learning and achieving new levels of autonomy across the organization. Even though Robotic process automation (RPA) has dramatically increased efficiency and enabled businesses to automate high-volume repetitive tasks, there are inherent limitations when multiple processes are involved. Agentic AI is turning these limitations on its head injecting flexibility, adaptability and enhanced capability to deal with complex scenarios, which often entails judgement, and to take actions independently based on intelligent analysis and continuous learning.

Agentic AI will have massive ramifications in traditional systems and processes, specifically in service interactions. Within the next four years, i.e. by 2029, 80% of customer service issues will be resolved independently without human intervention, according to Gartner.

The Shift from Rule-Based Bots to Intelligent Agents

The contrast between RPA and Agentic AI could not be more stark—while RPA works in the background Agentic AI will bring autonomy to the front and center of organizations. RPA deployment is defined via coding, and it interacts with the software through a user interface—similar to how humans interact. While the core of Agentic AI is designed with a human-like architecture which enable it to process unstructured data, apply reasoning and execute tasks in complex workflows with precision.

To put it simply, Agentic AI extends the capabilities of RPA by embedding intelligence into the automation process. It mimics human cognitive capabilities and operates in complex environments to take independent decisions, much like a human brain. The autonomy of AI systems marks a profound shift in enterprise technology empowering organizations to be more intention driven.

Cloud Migration Challenges

Migrating workloads to the Cloud is fraught with challenges, and organizations that understand these challenges are better equipped to deal with the migration. Despite the obvious, often the absence of a comprehensive Cloud migration strategy leads to sub-optimal outcomes. A comprehensive strategy should include defining the objectives, selecting the right public Cloud provider and the identifying the right workloads to move to the Cloud.

Organizations often falter in making an accurate estimate of Cloud migration. Given that there are multiple pricing strategies and different types of services, organizations make mistakes in making the right choices and experience escalating costs. There are also hidden costs in data migration, such as, the need to upgrade connectivity to accommodate higher bandwidth requirements.

Designing a Cloud architecture that aligns with the existing IT systems while meeting with the goals of migration is challenging, as it must take into account legacy systems and dependencies that were not designed for Cloud integration.

Cloud Migration Roadmap for Businesses

Below we share some practical tips and actionable insights, based on our expertise and experience, on how to better prepare the organization and thus, smoothen the journey to the Cloud.

• Take the plunge: While planning is crucial, getting started is the key. Too often, organizations get mired in execution details which bogs down the process and stalls the migration program. NewVision experts find that savvy adopters start with a small team of engineers opening an account with Microsoft Azure or on AWS Cloud, and trying out services from the console. This sets the pace within the organization as engineers become familiar with the environment to spin up instances, try out different regions and get an opportunity to understand the different tiers of services. Most public Cloud providers offer a free tier which is good to get started, and many organizations actually start production workloads from the free tier before upgrading to different instances and services.
• Executive Sponsorship for Cloud:Needless to say, strong executive sponsorship bolsters the program, providing the much-required momentum and fostering collaboration across stakeholders to galvanize the strategic move to the Cloud. Usually, the Cloud initiative is headed by the CIO or the CTO with the backing of the CEO and the CFO. Sometimes in large-scale migrations it is the business head such as the COO, and sometimes even the CEO leads from the front to facilitate the alignment of technical and business objectives; managing the short-term and long-term goals; and stakeholder expectations. Given that Cloud migration has many complexities, the executive office of the Cloud Center of Excellence comprises representatives from legal, procurement, risk management, CISO, heads of infrastructure and delivery along with Cloud evangelists.
• Set Migration Goals: Organizations must have clear goals and expectations which will help benchmark efforts and take corrective action to steer the course of migration successfully. The objectives could be business reasons such as cost savings, enhanced customer experiences, improved agility or it could be technical benefits such as improved scalability or enhanced security. At the same time, it is important to ensure that there is alignment of business and technical goals. For instance, the need for enhanced customer experience and high performance may clash with the objective of cost-efficiency, as meeting the former objective will require auto-scaling and fail-over strategies which will be more costly. Managing stakeholder expectations is a key requirement in setting migration goals.
• Reach out to Cloud Migration Experts: Set up a dedicated session with an experienced Cloud migration services provider to get clarity on many critical aspects which will affect the decision making. It is important to address all the doubts, so take a systematic approach in collecting the queries of different stakeholders and have a detailed discussion with an experienced provider. These sessions will provide clarity on the big picture, guidance in navigating licensing and pricing issues, security and compliances, architectural best practices, all of which will help you make informed decisions.
• Assessment of IT Estate: Conduct a comprehensive evaluation of your organization’s IT landscape. Start by creating a detailed inventory of all systems and applications. Document key details such as version numbers, usage patterns, interdependencies, and business criticality. Identify strategic applications by mapping their criticality to business operations and examining interdependencies, performance, and security implications. This thorough analysis will reveal which applications should be prioritized for migration. For instance, workloads experiencing variable peak loads or sudden traffic bursts are ideal candidates for the Cloud due to its inherent scalability. Additionally, applications nearing end-of-life can be modernized by moving them to the Cloud, reducing legacy maintenance burdens.
• Establish a Governance Framework: A Cloud governance framework is essential to ensure that migration goals are met. Apart from aligning security and compliance objectives, the governance framework must establish policies and guidelines for financial management, operations, data management, performance and asset management. Typically, the framework will define the outcomes in the desired areas without specifying the procedures, as technologies and approaches change rapidly. For instance, data governance policy can mandate encryption to store sensitive data without specifying which tool to use. The governance framework is also essential to sort out overlapping objectives, such as, security and data management; operations management and cost control.
• Communication Strategy: Put in place a strong communication strategy as Cloud migration will affect all stakeholders in the organization, and therefore a solid communication plan will help smoothen the process. First, identify all internal stakeholders and communicate about the need, objectives and migration roadmap. It is also important to keep external stakeholders such as migration partner and collaborators on the same page. Next, identify what needs to be communicated to each stakeholder. For example, CXOs will be interested in knowing about the legalities, possible business disruptions and ROIs while HR and operations will be keen to know more about change management, employee onboarding, business continuity in terms of access to resources, etc. At the same time, educating stakeholders about the nuances of the new model is an important communication objective. For instance, procurement must become adept with managing an operating expenditure, while legal will need to understand the implications of a borderless organization.
• Talent Management and Skill Mapping:Prepare the workforce with skillsets for migration using an integrated approach that includes skill evaluation, mapping of existing skill sets, upgrading with training, strategic hiring and leaning on an external partner for skills that are difficult to bridge. An experienced partner brings technical expertise and helps to make informed choices to meet technical and business goals.
• Establish a Cloud Center of Excellence: The Cloud Center of Excellence is a cross-functional team of experts. It is the hub of activity and will oversee practically everything from defining the Cloud strategy, selecting the right public Cloud platform, assessment of IT estate, defining the migration roadmap, managing the execution including developing a governance framework, and providing training and support.

Cloud Migration Consulting Services

As an experienced consulting and solutions provider, NewVision Software has enabled complex migrations, based on which we have developed institutional assets and knowledge repository to provide a strong operational foundation for successful migrations. This includes a migration methodology for executing legacy migrations in a methodical way with a robust set of tools to automate and accelerate common migration scenarios.

Customers have successfully migrated workloads to Microsoft Azure and are harvesting the inherent benefits of the platform, including agility, scalability and quick access to the advanced technology resources. These capabilities are empowering customers to experiment often, innovate at speed and take up competitive positions in the market. Working with an expert like NewVision to define a Cloud migration strategy has facilitated customers to prepare the organization well, and look beyond the nitty gritty of migration and focus on growth and differentiation.

Lake-Centric Approach: Fabric is built around OneLake—Microsoft’s Data lake solution—akin to how Microsoft’s 365 applications are automatically tied with OneDrive. Given that it is a single fabric that spans multiple Cloud, it eliminates data silos and empowers users with a unified data storage capability.

Designed for AI-experiences: Native integration with Azure’s Co-pilot at every level of the architecture empowers users to draw insights in conversational language without doing complex coding. This empowers employees with different personals to utilize GenAI capabilities to summarize reports and insights, build machine learning models, develop dataflows and pipelines, generate code and visualize results in custom conversational language experiences.

Empowering Data Personas: Aimed at facilitating data-based decision making, Fabric is integrated with Microsoft 365, providing a single copy of data to empower users—from business users, analysts, data scientist, machine learning specialist to developers—to draw insights.

Cost reduction: The unified data experience from resource provisioning, integration, and tool utilization using a single pool of compute for the entire data workload results in reduced cost and effort, as the single compute eliminates costs of idle resources provisioned for different resources.

Employing Microsoft Fabric to Benefit NewVision Customers 

As an early proponent of Microsoft Fabric, NewVision Software has exploited these capabilities to benefit customers in myriad ways. One example is how NewVision has boosted post-merger integration capabilities using Microsoft Fabric to integrate disparate data systems and drive operational efficiencies to unlock the synergies of mergers.

Specifically, NewVision worked with a leading US-based orthodontist support platform to pursue its growth strategy via acquisitions. With more than 2.5 million patient visits spread across 72 locations, success was crucially linked to quick integration of acquired practices.

NewVision designed a robust data platform to cater to the current and future requirements by implementing a data agnostic layer on top of a data warehouse hosted in Microsoft OneLake, using Microsoft Fabric data pipelines to ingest and transform data quickly. This has empowered users to leverage Power BI to draw insights and pursue its aggressive acquisition-led growth strategy.

If you want to know more about Microsoft Fabric or how we empowered the orthodontist platform’s with data integration

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Service Portfolio

Evaluate the MSP’s service offerings to ensure they align with your organization’s requirements.

Look for comprehensive services covering Cloud infrastructure design and implementation; Cloud migration services; application management; data architecture and analytics; user assistance; governance and compliance; and Identity and Access Management (IAM).

Expertise and Experience

Assess the MSP’s track record, expertise, certifications, and client references. Ask for referenceable customers within your industry. An MSP with vertical experience brings deep understanding of business and insights to help create differentiated value. MSPs coalesce best practices and help organizations reduce the learning curve in Cloud adoption approaches.

SLAs (Service Level Agreements)

Review SLAs carefully to ensure they align with your expectations regarding response times, issue resolution, and uptime guarantees. Define the scope of service and specify what is included and excluded in the contract. A small company may not require the entire bouquet of managed services. Ensure metrics for measurement, reporting methods, and frequency, conflict resolution process, and provision for updating SLAs.

Scalability

Scalability is imperative to remain responsive and the MSP must have the ability to scale services according to the organization’s evolving needs. Whether you are expanding operations or experiencing seasonal fluctuations, the MSP should offer options to add or modify services, optimize support staff, provide skilled technical expertise, and adapt to changing requirements without compromising performance.

Integration

Evaluate the MSP’s capabilities to integrate Cloud systems with your existing systems and infrastructure. Seamless integration is crucial to maintain workflow efficiency and data consistency across disparate platforms, particularly when multi-Cloud deployments are involved. Look for a provider with proven expertise in integrating complex IT environments and facilitating smooth transitions.

Delivery Model

Understand the MSP’s service delivery options, including on-premises, cloud-based, or hybrid solutions. Whether you prefer a fully managed service or a co-managed approach, choose a delivery model that best suits your IT strategy and resource allocation.

Technology Partnerships

The MSP’s partnerships with leading technology vendors are indicative of the capabilities and expertise. Strategic partnerships empower the MSP to access specialized expertise, cutting-edge solutions, and exclusive resources to deliver enhanced services. Make sure the MSPs partnerships are aligned with your requirements, such as, need for advanced automation or AI models.

Cost and Value

Make an assessment of the total cost of ownership associated with the MSP’s services, including upfront fees, ongoing maintenance costs, and potential savings from improved service quality and ROI. While cost is an important factor, prioritize value-driven decisions that facilitates organization’s long-term goals.

Thriving with MSP Partnership

To thrive in unpredictable market conditions, businesses must adeptly identify trends, seize opportunities, and prioritize differentiation and value creation. Best-in-class Cloud MSP are digital natives with deep understanding of how the digital world operates. Having them on your side empowers to create customer delight on a continuous basis. A strategic partner fosters growth as a trusted advisor, employing a consultative approach to drive outcomes, while also adopting a proactive, preventive approach to service delivery.

If you want to know more about how NewVision MSP services can support you, write to us at contact@newvision-software.com

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AI in Audit and Tax

As AI reshapes the landscape and becomes a fixture at the workplace, talent and skill management are undergoing transformation. The industry is aware that AI is not a replacement, instead staff augmentation with AI empowers them to do more with less—junior auditors will better assist experienced auditors allowing them to focus on more value-creation and sector-specific insights.

Natural Language Processing capabilities are reducing the barriers, enabling employees across levels contribute meaningfully—employees across levels can easily use NLP technology to extract information and insights from unstructured data such as emails, contracts, invoices, meeting minutes, etc.

GenAI is playing a transformative role in empowering auditors to expedite the creation of reports by summarization vast datasets and highlighting crucial findings. It is also enabling auditors to augment existing datasets, test various scenarios and validate models with synthetic data without exposing sensitive information. By simulating different scenarios, AI helps to detect anomalies and irregularities in financial data and strengthen risk assessment and decision making.

However, harnessing AI for financial reporting effectively remains a challenge as it requires a significant upgrade in infrastructure; training employees to use AI systems; data privacy and security safeguards; ensuring AI models are consistent and adhere with ethical practices.

NewVision has been helping audit and tax firm to implement AI in multiple ways—from streamlining audit operations with intelligent automation; implementing comprehensive data strategy to ensure high-quality data; ensuring high performance applications and systems with AI-driven QA processes; designing, building and deploying AI-driven digital products.

Purpose-Driven Data Management in Audit and Tax  

High-quality data in terms of data extraction, data refinement and making it easily accessible to auditors is the foundation of its professional services. A global tax and audit consulting firm with operations in 150 countries had developed a proprietary data management platform to facilitate its team of auditors around the world to gather, analyze and report data, while safeguarding sensitive data and critical business processes. The firm supports organizations with complex data management, risk advisory, compliance, and audit services to optimize business processes and ensure compliance.

As the platform was being built, they needed a modern QA strategy to align with stringent timelines. This required a rapid testing approach that enabled continuous testing and automation throughout the development lifecycle, so the platform could go live on schedule without compromising quality or compliance.

The platform faced multiple challenges that impacted its ability to deliver value—it required complex data integration from multiple sources with near-real-time processing in a seamless manner. At the same time, it had to ensure financial data consistency and integrity which was a challenge for efficient testing.

However, the organization struggled to achieve high-quality data due to absence of standardized test data generation processes across systems, and effective quality indicators for cleansing, validation and consistency. This was because balancing comprehensive test coverage with rapid iterations was a challenge. This was forcing the omission of several test scenarios, posing a risk to data integrity. Further, data was processed in large batches which delayed testing and slowed the ability to identify defects.

As data volumes grew—up to 100 million records weekly—scalability became a major concern as the existing QA strategy and tools could not handle the increasing complexity. The platform had a multi-year release roadmap with advanced features, and the need was to speed up testing with automation.

Due to these inherent complexities, the execution was fragmented and the test environment was not reliable which led to frequent test failures and effort wastage by the testing team.

Intelligent Automation in Auditing – A Strategic Approach to QA Transformation    

NewVision brought in a strategic approach to transforming the QA systems and designed and a comprehensive framework aligned with the needs of the organization. The aim was to standardize testing practices and ensure  a consistent and intelligent automation in auditing across all stages by integrating testing at various levels—UI, API, and data—to ensure comprehensive coverage of functional and non-functional requirements.

The team of experts implemented a quality gate-driven strategy that validated checkpoints at critical stages—data acquisition, transformation, and reporting—to ensure data integrity and minimize risk throughout the testing lifecycle. The team then implemented a unified tool automation framework covering UI and data validation to ensure efficiency across diverse testing requirements.

The team of experts implemented best-in-class regression automation suite using Tricent’s Tosca to automate the entire lifecycle of software development and seamless integration with Azure DevOps.

We further strengthened the framework by integrating it with a custom tool to generate test data that accurately simulated real-world scenarios and improved test accuracy and consistency. It also helped reduce dependency on data from the source systems. The team embedded efficiency by designing a comprehensive regression test suite that prioritized business-critical workflows and ensured that tests reflected real audit operations in global markets.

Finally, our experts introduced build verification tests and leveraged continuous monitoring for early detection and resolution of issues related to the test environment. NewVision’s transformation initiatives increased automation coverage by 64% and reduced execution time from six weeks to two weeks.

The outcome was that the customer achieved dramatic efficiency gains wherein data preparation time was cut down to just four hours for one million records. This along with early defect detection capabilities kicked in cost efficiencies in QA processes to the tune of 40%.

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Cloud Security is Different by Design  

Given the seamless nature of the Cloud, security is inherently different. There are no physical barriers as the organization has become boundary-less, and so, traditional approaches relying on perimeter-based security have become redundant.

Cloud offers higher security with in-built automation and advanced AI capabilities for monitoring and remedial measures. But the key is to understand the capabilities and employ security practices aligned with organizational needs—like any other tool, security practices must be finely balanced with the need for speed and agility.

A fundamental difference between Cloud and traditional IT security is that security is a shared responsibility in the Cloud. This means that public Cloud provider is responsible for the physical infrastructure such as the datacenter, and the underlying infrastructure, while it is the users, the business or organization that must ensure the security of the resources they use with secure configuration and restricting access to the resources and data.

Studies find that security misconfiguration is the top concern amongst 67% of the organizations using Cloud, followed by lack of visibility at 64% and IAM misconfiguration at 61%.

It is important to understand that while modern Cloud approaches such as DevOps automation, distributed serverless architectures and ephemeral assets such as containers offer greater agility and flexibility, the security implications of leveraging such resources are completely different.

Thanks to advanced automation, public Cloud is inherently more secure and easier to manage than traditional on-premise deployments. For example, automated patching ensures rapid updates, while continuous monitoring and auto-remediation capabilities ensure workloads are resilient and protected. Features such as Active Directory, Azure Management Groups, Azure Blueprints allow businesses to deploy, manage and monitor security setting from a central dashboard to meet security and compliance requirements on a continuous basis.

Cloud providers continuously invest in security technologies aligned with the evolving needs of customers, while also supporting with security best practices and architectures. Designing Cloud architectures with Well-architected principles, embedding Security by Design principles, and embracing a zero-trust approach make Cloud deployments robust and highly secure. Further, the flexibility that Cloud architectures accord allow organizations to tweak deployments to enhance security aligned with the evolving landscape.

Enhancing Security with Cloud Security Assessment Services

To enhance the security posture, organizations must take stock of existing systems and practices and embrace a process of rigorous implementation to prevent, detect and respond to incidents. Based on our experience of working with a wide range of customers, NewVision finds the following steps crucial in assessing the Cloud security posture of workloads.

Detailed security assessment of existing deployment to identify misconfigurations, architectural risks and blind spots.

Define a security roadmap for a robust foundation with Well-Architected framework design principles, security tools and forensics.

Implement Security by Design principles wherein security is embedded in the fabric of Cloud deployment by leveraging the most appropriate security services, and third-party tools for comprehensive view of the environment.

Embrace a zero-trust approach with a layered approach to access and data encryption at rest and in transit.

Security automation for continuous compliance, auto-remediation and incident management.

Enable 24×7 monitoring with advanced analytics, threat remediation and ongoing threat management.

NewVision provides comprehensive penetration testing solutions including Static Application Security Testing (SAST) and Dynamic application security testing (DAST) services which covers security vulnerabilities from all angles. Our SAST services examines source code and binaries to identify security gaps early in the development cycle, and remediate applications before going into production.

By integrating SAST into the CICD pipeline, we facilitate customers to build high-quality software with continuous scanning and code review. At the same time, automated report generation and mechanisms track remediation efforts and empower development teams to prevent costly mistakes easily.

NewVision DAST services analyzes production workloads to discover risks in a runtime environment by simulating attacks such as SQL injections and breaching authentication. Our solutions are aimed at helping customers to identify potential gaps and strengthen the overall security posture.

Best Practices for Cloud Security Posture Management

Working with a large number of customers across organizations, NewVision Software understands the threat landscape and recommends the following best practices to enhance the overall Cloud security posture on Azure.

Implement Access Control: Granular access control using Azure RABC that restricts access to workloads and Cloud resources, backed by regular review and update permissions.

Multi-Factor Authentication: Implement MFA to get an added layer of access control. MFA capabilities are integrated with Microsoft Entra which is a comprehensive IAM suite, and authenticates based on access policy and risk assessment.

Centralized Web Application Firewall: A WAF solution simplifies monitoring and managing applications against threats and intrusions from a central location instead of managing individual applications. WAF is natively integrated with many Azure services, such as Azure Application Gateway and Azure CDN.

DDoS Protection: While the basic tier of Azure DDoS Protection is available for all workloads, we recommend to subscribe a paid version for enhanced capabilities like advanced analytics, adaptive tuning and integrated reporting.

Use a Bastion Host: Protect applications by using a bastion to prevent direct public access to your workloads. Using Azure Bastion enables to access workloads without exposing to the public internet, while providing seamless connectivity through the Azure portal.

Enforce Port Management: Restrict unnecessary inbound and outbound ports into the public Internet to minimize exposure and unauthorized access. Permit only essential inbound ports and fortify with firewalls and network security access; permit outbound traffic to those necessary for business, and restrict protocols such as SSH and RDP to prevent data exfiltration, lateral movement, and network scans during an event.

Automated log monitoring: Collect and anlayze logs from infrastructure and applications on a continuous basis using tools like Azure Monitor and Log Analytics for real-time visibility into the environment and incident management.  

How to Improve Cloud Security Posture

Best-in-class organizations are stepping up Cloud security measures seriously, and working with experienced partners to enhance the security posture. This includes review of the existing systems by Cloud security consulting providers and implement tools, polices, and frameworks that continuously manage Cloud infrastructure and platforms through prevention, detection, and responses.

Importantly, there is an emphasis to embrace a proactive approach to assess and identify risks in cloud configurations and put in place mechanisms that remediate with an automated and human-driven approach.

As more workloads move to the cloud, enhanced security has become a key concern, and Microsoft Azure offers advanced capabilities with role-based access management, end-point management, and enhanced protection with the Microsoft Defender line of products. Organizations are hardening security practices using best practices, including zero trust and security by design.

Cloud Security Services

Azure provides a host of services to harden security at every layer. Savvy users are choosing the best offerings to simplify management. For instance, combining a platform such as Azure Service Fabric allows us to deploy and manage containers in a distributed environment while monitoring and managing it centrally via the Azure Security Center.

Another example how organizations are available the best of Azure capabilities is by combing end point management with Microsoft Intune family of Cloud products, along with Microsoft Tunnel, a mobile app management tool to achieve a secure VPN solution for mobile devices and securely connect apps hosted in Azure.

The Cloud juggernaut is on, and security measures must keep pace. NewVision’s customers are beefing up security via periodic reviews, advanced tools, and testing capabilities. Are you lagging behind?

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In a software-driven world, businesses are trying to distinguish by innovating at speed while ensuring quality customer experiences. These efforts are continuous using agile and iterative methodologies to elevate the software development lifecycle and get the most out of the value stream.

Yet despite the hype and value-proposition of DevOps, many organizations have not been able to scale because of faulty implementations. Gartner’s prediction that 70% of DevOps initiatives will fail due to people-related challenges require careful consideration.

DevOps is not about tools and technology, but more about organizational culture, collaboration, and workflow. There is a reason why it is called DevOps practices and DevOps champions must play a crucial role in reducing the friction between organizational silos by uniting disparate teams.

Silos Impede Speed 

Organizations are structured into teams based on specialization, which is a stumbling block to the speed and velocity required for software development. The essence of DevOps is to facilitate continuous improvement via collaboration and communication amongst teams and working in silos is obstructing these principles.

From a DevOps perspective, silos are isolated working environments within an organization that do not effectively communicate or collaborate with each other. These silos can exist across departments as well as within a specific unit.

For instance, developers may be focused on coding and meeting project deadlines without sufficient interaction with other teams, just as operations who are focused on infrastructure stability and reliability might resist changes to stem outages. Such lack of collaboration with development can result in slow deployment processes.

On the other hand, if testing is treated as a separate phase and not integrated with development, teams experience delayed feedback, longer development cycles and higher possibility of leaking defects into production. Similarly, if security measures are not considered early in the development lifecycle, there are bound to be serious challenges in managing vulnerabilities and compliance requirements.

A thoughtful approach can successfully transition the organization to DevOps practices. NewVision has navigated the DevOps journey in many organizations and here are the four pillars of our methodology.

Ushering the right culture: This requires an evaluation of the organization’s sentiment towards adopting DevOps processes by examining things like decision-making for application releases, collaboration and sharing practices amongst teams, governance structure, awareness regarding automation, infrastructure configuration, and management. The DevOps workflow is aimed at fostering visibility amongst teams to promote transparency and encourage knowledge sharing.

Automation: Automation ensures tasks are performed consistently, minimizes human errors, and allows teams to focus on higher-value activities. It requires focus on several areas including identifying automation opportunities, adopting CICD pipelines, using Infrastructure as Code, and integrating automated testing and continuous monitoring. It examines practices such as code review, code sharing among teams, security testing, version control, rollback processes, and application monitoring tools and procedures.

Structures and Processes: Structure and processes provide the necessary framework for efficient collaboration with well-defined roles and responsibilities; standardized workflows and best practices to guide teams in daily operations and ensure consistency and reliability in software development and delivery. Towards this, we recommend establishing clear goals aligned with business needs; KPIs to measure success; standardized workflows, such as CICD pipelines; automated monitoring and collaboration. Fostering transparency encourages knowledge sharing, while regular training combined with continuous process refinement keeps the organization agile and responsive.

Measurement: Successful DevOps implementation relies on measurable metrics to ensure DevOps practices are creating impact. Specifically, deployment frequency measures how often software updates are released, indicating agility and responsiveness to user needs. Change failure rate assesses the reliability of updates while mean time to recovery evaluates the speed and effectiveness of issue resolution. Lead time tracks the duration from concept to implementation; change volume ensures updates are meaningful and defect escape rate measures pre-production error detection and customer tickets indicate overall user experience.

DevOps for Speed, Quality and Innovation 

IT operations must adapt to the complexity of multi-cloud, and traditional environments by adopting a DevOps culture that integrates quality and security into everything to ensure software delivery meets the core requirements of easier, faster, and transformative.

Embracing enterprise-scale DevOps necessitates a significant transformation in how organizations plan, build, test, release, and manage applications. More importantly, scaling DevOps in large, globally dispersed IT organizations not only enhances application delivery but it is an opportunity to drive better business outcomes by optimizing value streams and facilitating continuous innovation.

The Transformative Shift in Software Engineering with GenAI

As digital penetrates, winning companies are striving to differentiate with AI-driven software to deliver new and better experiences. The race to outpace competition with quality software has put tremendous pressure on software organizations and embrace new technologies and software development approaches such as Agile, DevOps, automation of code integration, testing and deployment practices; cloud native and composable architectures and low-code-no-code. Yet software organizations are still struggling to reduce technical debt and deliver quality output at speed.

GenAI has shown early promise in empowering coders with better, faster code development. Key areas in which GenAI can have a transformative impact on software engineering practices include the following.

Assisting Business Analysis: GenAI is helping business analysts, product owners and business users identify needs, understand it accurately and translate that need into actionable user stories. It helps to resolve inconsistencies and remove ambiguities by prioritizing needs, defining an implementation roadmap and ensuring consistent communication amongst stakeholders. Most importantly, NLP features ensure that user stories of all stakeholders are effectively captured.

Accelerating design and coding: GenAI is facilitating software developers in designing and coding including UI, entity models, microservices and API to create faster and more accurate software architectures. Research finds adoption of GenAI is at an early stage with 11% using it for software development purposes with a slated penetration of 25% by 2026 for software design, development and testing. This is triggering a profound shift in development practices where there is a clear shift from coding to prompt engineering. Software engineers can simply describe the functionality required, and the GenAI program will generate an output. Just as a developer can initiate a code and the GenAI program will autocomplete to accelerate the development process. It assists coders through intelligent code refactoring suggestions, and automated review.

Optimizing test design and QA: Incomplete or deficient testing is a major cause of software bugs and failure. GenAI enhances the quality assurance process and bolsters productivity by generating comprehensive test cases and test scripts by evaluating acceptance criteria and known uncertainties to streamline acceptance testing and fine-tune the overall QA process.

Managing test data is a critical challenge, from ensuring coverage to maintaining data integrity and security. GenAI streamlines this process by efficiently managing test data, generating high-quality synthetic datasets, and minimizing risks—enhancing both test coverage and application security. By 2025, Gartner predicts 20% of all test data will be synthetically generated. However, adoption remains low, with test case generation at 26% and coding assistance being the most widely used application at 39%, indicating that most organizations are still in the early stages of leveraging AI for software testing.

Benefits of GenAI in Software Development

As compared to traditional approaches in software development, GenAI based development platforms are reducing the time by as much as 50% according to some research.

One of the most disruptive capabilities of GenAI is NLP or conversational capabilities which enables coders and even business users to simply describe the requirement, without learning any coding language. The system can listen and understand the intent and context to create applications or suggest workflows, personas, and data models based on industry best practices. This process lowers the barriers in software development enabling business users to participate in the development process. Specifically, the benefits of GenAI in software development include the following.

• Conversation-based approach in software generation which allows users to interact with the system in natural language. The system returns a solution or suggest an application design based on the description of a requirement or functionality.
• AI-driven optimization of workflows, data models, and application components wherein humans interact with GenAI models to design intelligent automation, optimize workflows, refine data models, and enhance application components through iterations, monitoring and seamless decision-making.
• GenAI driven software development accelerates time-to-market by enabling prototyping in real-time, rapid deployment, and continuous improvement by leveraging GenAI powered automation through the SDLC. Real-time prototyping enables developers to generate code, UI mock-ups, and system architectures instantly while rapid deployment is facilitated through AI-assisted CI/CD pipelines with continuous improvement driven AI-driven analytics based on user feedback and performance monitoring.
• Enhanced collaboration between technical and business teams by translating business requirements into technical specifications and vice versa with an overwhelming 78% developers citing it as a benefit of GenAI. It bridges the gap between teams by automatically generating documentation, user stories, and code snippets from business inputs, ensuring stakeholders can clearly articulate requirements while developers get a more precise understanding.

NewVision as a Software Development Partner using Advanced GenAI Capabilities

NewVision Software has been leveraging cutting-edge technologies in the SDLC to service customers with high-quality software products. From requirement gathering through testing and delivering continuous improvements, we employ GenAI to enhance productivity and faster time to market.

NewVision employs AI-driven Co-Pilots powered by LLMs and deep contextual understanding wherein co-pilots work along-side software engineers to accelerate development cycles. By integrating AI-driven automation in requirement gathering and testing along with intelligent code generation, we empower businesses with smarter, personalized, and high- quality software products.

NewVision has also established Vision Lab which drives GenAI innovation within the company. The Labs has completed POCs in key applications including customer service, AI copilots, content generation, and document intelligence. By integrating leading LLMs in software development capabilities we help businesses stay ahead in an increasingly AI-driven world and remain a trusted partner in driving the next wave of AI adoption.

Harnessing GenAI Smartly

Even as GenAI empowers developers with disruptive capabilities, it is important to understand that it is a tool and must be used to complement developer capabilities. It excels in laborious tasks such as documentation, code review, keeping track of change requirements, requirement validations, etc.

Employing GenAI in development without a well-defined software architecture will create future challenges such as code repeatability and scalability. A well-defined structure will ensure the software is future-proof and meets with compliance requirements, while embedding  standardized best practices for enhanced software quality.

Software organizations will do well to have a structured approach in adopting GenAI. This must start with a culture of learning wherein developers are sensitized about the nuances of the tool and trained to utilize it effectively. For example, prompt engineering is a key skill that determines the outcome of software quality and invest in imparting new skills.

Over time, GenAI assisted software development will replace traditional approaches, wherein team members will be assisted at various stages allowing them to focus on creative and specialized tasks. GenAI is redefining software development and will fundamentally change industry practices. A strategic approach to adoption is imperative as early adopters will reap the advantage for competitive advantage while those slow to adapt risk falling behind in an increasing AI-powered landscape.

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