What often appears to be “overnight success” is usually the result of years of strategic investment, learning from failure, and relentless perseverance. In the workplace, innovation doesn’t arise by chance—it’s cultivated through intentional effort, structured initiatives, and a culture that inspires people to imagine what’s possible.

Award-winning organizations don’t leave innovation to luck. They create environments where creativity flourishes and bold ideas are transformed into meaningful results. Here are some of the foundational strategies that drive innovation behind the scenes:

1. Encouraging Psychological Safety

One of the most powerful predictors of a high-performing team is psychological safety—the belief that it’s safe to take risks, speak up, and share unconventional ideas without fear of embarrassment or retaliation.

When employees feel secure, they’re more likely to voice new concepts, challenge the status quo, and support each other through trial and error. Innovation requires experimentation, and that means sometimes failing forward. A psychologically safe environment turns failure into feedback and fuels the kind of creative thinking that leads to breakthroughs.

2. Fostering Cross-Functional Collaboration

When people from different departments, roles, and backgrounds collaborate, they bring fresh perspectives and uncover unexpected solutions.

Organizations that encourage this kind of cross-functional synergy consistently outperform those that operate in silos. Collaboration fosters empathy, promotes knowledge-sharing, and sparks the kind of energy that drives change. Highlighted in Karsun’s previous blog post on collaboration and communication, which explores how a connected culture leads to higher-quality outcomes—demonstrating that the best ideas often emerge at the intersection of diverse experiences.

3. Investing in the Right Technology and Processes

True innovation requires the right infrastructure. That means not just having access to cutting-edge tools, but embedding smart processes that empower employees to do their best work.

Forward-thinking companies are constantly evolving their tech stacks and workflows to remove friction, boost efficiency, and open doors to new ways of thinking. These investments aren’t just about staying competitive, they signal to employees that the organization believes in their potential and is committed to enabling their success.

Building a Culture That Sustains Innovation

A thriving culture of innovation is never accidental. It’s a reflection of leadership choices, employee empowerment, and a clear vision of what’s possible. By prioritizing the employee experience—through trust, creativity, collaboration, and capability—organizations don’t just achieve recognition. They build workplaces where people are inspired to grow, experiment, and shape the future every single day.

Behind every award, milestone, or major leap forward lies a story of intention. Success, it turns out, is built in the quiet moments: brainstorming sessions, courageous conversations, and the unwavering belief that tomorrow can always be better than today.

This blog was written by Karsun expert Angela Brooks. Angela is Karsun’s Director of Talent. Connect with her on LinkedIn.

Learn how innovators and experimenters grow at Karsun then dive into the latest from our Karsun Innovation Center.

Lisa Hoover, Head of Experience and Design at Karsun Solutions recently joined John Gilroy and the Federal Tech Podcast. In this insightful discussion, they delve into the evolving Customer Experience (CX) landscape in the midst of rapid technological change.

Shifting from UX to CX

In their discussion, Lisa describes the evolution of focus from user experience (UX), which centers on digital product solutions, to CX, an all-encompassing approach to technology products. CX considers entire interactions a person has with an organization’s brand, throughout their entire relationship with that brand including solutions, services, and of course, the UX on an organization’s website. 

In the federal sector, there’s a growing awareness of CX. This was driven by previous executive orders on federal customer experience and service delivery. However, Lisa notes that initially, the focus was often on meeting requirements, simply “checking a box” for CX rather than truly embracing human-centered design principles.

The Legacy System Challenge 

A significant hurdle in improving federal CX is the prevalence of legacy IT systems. These monolithic systems with complex, entangled codebases make modernization difficult. Customers often get stuck with inefficient processes due to these outdated applications, and manual patches only exacerbate the problem. Lisa observes this is why a “checking-the-box” approach to requirements is not enough, organizations need to implement a holistic, product mindset.

Unlike commercial companies that can independently implement user-friendly changes, the federal government faces limitations due to these legacy systems and strict compliance requirements, including those surrounding data privacy and security. That’s why, Lisa notes, only 23% of Americans believe federal services are easy to navigate, indicating a significant need for improvement

Measuring CX Effectiveness 

Measuring the impact of CX improvements is more complex than measuring UX, where metrics like bounce rate and time to task completion are readily available from web analytics and other resources. In the past, insights into customer challenges were often buried in support tickets, making it difficult to establish a baseline for improvement. However, with the rise of artificial intelligence (AI), organizations can tease apart the complex relationships between online and offline activities, paint a clearer picture, and improve customer experience.

The Dawn of Approachable AI 

Shifting the conversation to the role of AI in addressing these challenges, Lisa emphasizes that AI has become increasingly “approachable.”  Modern AI has moved from an abstract concept to a mainstream technology, largely due to advancements like OpenAI. This increased familiarity makes federal customers more open to accepting AI-powered solutions.

AI for Efficiency at Scale

AI presents a remarkable opportunity to achieve efficiency at scale within federal agencies. Instead of replacing humans, AI can help individuals become more efficient by automating manual tasks and allowing them to focus on higher-value work. For instance, developers currently spend a significant amount of time troubleshooting legacy systems; AI can help reduce this well-known “time suck.”

A Blueprint for Modernization

Karsun Solutions offers a potential solution with its ReDuX AI-powered modernization platform. ReDuX creates a blueprint of a legacy system, breaking down the architecture and the behavior of the system’s users. This provides situational awareness and context awareness, allowing various stakeholders to understand the system as a whole. This comprehensive view helps teams devise strategic modernization plans beyond simply “patching holes.” By offering this overarching blueprint, ReDux aims to take the guesswork out of legacy system challenges.

Streamlining Decision-Making with AI

AI, when integrated with platforms like ReDuX, can significantly streamline decision-making and accelerate modernization efforts. By providing a holistic view of complex systems, teams can identify areas for improvement, implement standardized solutions, and measure the impact. AI-powered tools like chatbots and augmented reality can further guide users and help interpret data for UI improvements at scale.

The Future of Efficiency and Enhanced CX 

Lisa concludes by expressing excitement about the future. She envisions increased adaptation of AI-enhanced solutions like ReDuX to modernize legacy systems more efficiently. The goal is to create federal services that are more usable, faster, and ultimately lead to satisfied and joyful customers. The key takeaway is that efficiency, enabled by understanding the big picture and leveraging AI responsibly, will be crucial in transforming federal IT and customer experience.

To learn more about Karsun’s ReDuX modernization platform, visit GoReDuX.AI. To listen to the full podcast, tune in at https://www.theoakmontgroupllc.com/ep-220-how-customer-experience-can-make-or-break-federal-technology-initiatives/

Government agencies facing an increasing mandate to move from legacy mainframe systems. The solution, generative AI. Now, agencies no longer need to fear the tradeoff between migration risk and the opportunity available in the cloud. In this case study featured on the AWS Public Sector Blog, our Karsun experts share their experience accelerating modernization with AI.

This case study features Karsun Solutions’ ReDuX AI powered migration and modernization platform. It offers a compelling approach for enterprises considering mainframe migration. Sharing their experience modernizing legacy applications for one of our government customers, this case study demonstrates what happens when modern AI tools meet modern software development practices. 

Deep Insights Using Amazon Bedrock

ReDuX is designed to tackle typical obstacles in mainframe modernization, such as undocumented business rules, technical complexities, and regulatory compliance. Its approach is inspired by Karsun’s decade and a half modernizing complex enterprise systems for the federal government. ReDuX utilizes generative artificial intelligence (GenAI) powered by Amazon Bedrock to analyze and understand legacy systems. 

Using Bedrock the Karsun team accessed high-performing foundation models from leading AI companies via a single API. Building their ReDuX platform on top of the resources provided by Bedrock, our team created an AI powered toolkit for identifying and mapping the complex business rules and processes embedded within mainframe applications. This facilitates a smoother transition to modern architectures. Further, by providing a structured approach, ReDuX reduces risks associated with legacy system modernization.

Matching AI with Modern Microservices Architecture

In the case study, we share how using our AI powered platform, we enabled our government customer to migrate from monolithic mainframe systems to microservices-based architectures on AWS. This shift enhances scalability, flexibility, and maintainability of applications, aligning with modern IT strategies. 

It also allowed our team to focus on security and privacy. Recognizing the importance of enterprise-grade security and privacy, especially in government and regulated industries, the platform incorporates robust measures to protect sensitive data during and after the migration process. ​

Furthermore, migration to the cloud enables secure-by-design architecture. Using AI powered tools, like those available on the ReDuX AI platform, lets our agency customers access the benefits of the cloud while reducing migration risks. Written in a modern language, it alleviates workforce concerns and eliminates long-term maintenance issues arising from the aging, dwindling population familiar with COBOL and other outdated languages.

By demonstrating a successful implementation of generative advanced AI technologies and cloud services to migrate and modernize legacy systems, this case study from our team offers valuable insights. It presents a potential roadmap for organizations seeking to undertake similar modernization initiatives. Head over to the AWS Public Sector blog for deeper insights on AI-driven modernization and a technical breakdown of our solution.

We recently announced that we were appraised at CMMI Level 5 DEV for a third time. This maturity appraisal comes from ISACA, a global professional association focused on governance of enterprise IT, and represents the highest maturity level in that organization’s CMMI (Capability Maturity Model Integration) framework.

Few companies meet the requirements for CMMI Level 5 Dev and we’re honored to appraise at this level three times. We’re proud to deliver quality consistently at the highest levels. Artificial intelligence, AI, powers the statistical and quantitative techniques used to meet our performance objectives.

In fact, in 2024 experts from our Karsun Innovation Center spoke at the ISACA CMMI Conference. At this conference, they presented a case study from one of our teams supporting a government agency customer. This team used AI tools included in our ReDuX AI platform to enhance efficiency while creating production ready code at a rate faster than if human teams produced that code on their own. 

To better understand how AI can drive these process improvements, we share some insights from their talk below. For deeper insights on AI, development and modernization, Discover How ReDuX AI Works. 

The Case for AI Powered Process Improvement

We presented this real-world case study at the annual CMMI conference in Phoenix, Arizona in 2024. CMMI appraisals were originally designed to help the Department of Defense understand its process. Today organizations around the world now seek out CMMI assessments. Karsun’s software development methodology is appraised at the highest tier for software development appraisals indicating we use a data driven process to analyze, assess and continually improve our practices.

The story begins in the Karsun Innovation Center. This center houses the Karsun research and development unit responsible for prototyping and integrating emerging technology and connecting our team members with industry technology leaders and experts. Part of the work of the Innovation Center includes building toolkits, playbooks and other resources. 

That includes an agile platform we built called GoLean that’s used by our software development teams. Using this platform, we tracked performance data, made adjustments, and observed the impact of our process improvements using dashboards and other tools to gain insights. Using the tools in this platform our teams compared incremental improvement to past performances, identified practices that were outliers from our average performance, and made adjustments to reduce the impact and risk of those outlier events. This approach to agile development resulted in our first two CMMI Level 5 DEV appraisals.

Meanwhile, artificial intelligence opened up the door to truly optimize our practice. We began building AI tools that could handle very high dimensionality. In other words, the model used by the AI needed to handle data with many related factors. 

This allowed us to understand the relationship between processes and the impact of changes to those processes. We could now better assess moving certain processes to the left, testing new automation practices or adding new DevOps or CI/CD tools. We could now measure holistic improvement to more rapidly scale and drive improvements among our development teams enhancing our overall efficiency.

The Results: Elevating Efficiency with AI

This, when combined with other tools in our AI platform, boosted our team’s efficiency as they modernized a 30 year old mainframe system. With the use of AI, our team saw a 22.5% increase in productivity! What’s more, using AI, they developed reusable resources to further optimize our processes.

In addition to new and improved methods for measuring process improvement, our ability to deliver high quality code quickly was further improved by AI agents. From code generation to scalability templates, agentic AI introduced yet another opportunity to further enhance our efficiency. To uncover this part of our toolkit visit us at GoReDuX.AI.

Agile development is in our DNA. Now using AI to boost efficiency in government, we’re leading the pack with high quality software and modern enterprise systems to meet agency missions. 

Ready to partner with proven performers? Contact our team here to get started!

As a software developer/solutions architect, navigating the complexities of modernizing legacy applications requires more than just adopting new technologies. It demands a deep understanding of software design patterns that ensure scalable, resilient, and maintainable solutions. Unfortunately, in the rush to modernize, crucial design patterns are often overlooked, leading to technical debt, performance bottlenecks, and security vulnerabilities. This article explores key design patterns frequently ignored by modernization teams, the reasons they are neglected, and the consequences of bypassing them. By integrating these patterns into modernization strategies, developers and architects can build robust, future-proof applications that stand the test of evolving technological landscapes.

Strangler Fig Pattern

The Strangler Fig Pattern is a gradual migration strategy where new functionality is built around the existing legacy system, slowly replacing it until the old system is entirely phased out.

• Why it’s overlooked: Modernization teams often opt for a full rewrite rather than incremental refactoring, assuming that starting from scratch will be faster and more efficient. However, this can introduce significant risks and delays.
• Real-world example: A financial institution migrating from a monolithic COBOL-based mainframe to a microservices-based architecture used the Strangler Fig Pattern. They introduced an API layer that progressively handled more transactions while legacy components were retired incrementally.
• Consequence of ignoring it: A complete system rewrite without this pattern can lead to prolonged development times, business disruptions, and increased failure risks due to untested new implementations.
• When to use: Use this pattern when modernizing large, complex legacy applications that cannot afford extended downtime or complete overhauls at once.

Saga Pattern

The Saga Pattern manages distributed transactions by breaking them into a series of smaller, compensating transactions.

• Why it’s overlooked: Modernization teams often assume eventual consistency is automatically handled by microservices frameworks, neglecting explicit transactional workflows. Implementing sagas can be challenging due to the need for handling failures and maintaining consistency. Teams may opt for simpler orchestration mechanisms without considering the potential for distributed transaction failures.
• A real-world example: An online travel booking system had issues where partial failures left customers with incomplete reservations (e.g., flights booked but hotels not confirmed). Implementing the Saga Pattern ensured rollback mechanisms were in place, maintaining data consistency across services. 
• Consequence of ignoring it: Without the Saga Pattern, distributed systems suffer from data inconsistencies, orphaned transactions, and poor user experience. Distributed transactions can lead to inconsistencies if not handled properly. Failures in one service can impact the entire transaction.
• When to use: Use this pattern when dealing with distributed transactions involving multiple services that must maintain consistency and systems with complex business transactions that span multiple services.

Sidecar Pattern

The Sidecar Pattern runs auxiliary services in separate containers alongside main application services, enabling functionalities like logging, monitoring, and security without modifying the core application.

• Why It’s Overlooked: Modernization teams may prioritize core service development and neglect auxiliary concerns, leading to bloated application code.
• Real-World Example: A fintech company used the Sidecar Pattern to deploy a separate logging and monitoring service alongside each microservice, simplifying debugging and performance tracking.
• Consequence of Ignoring It: Neglecting this pattern leads to tightly coupled services, making maintenance difficult and increasing the complexity of scaling and updating auxiliary functions.
• When to Use: Use this pattern when microservices require independent functionalities like logging, monitoring, or security without modifying the core service logic.

Circuit Breaker Pattern

The Circuit Breaker Pattern prevents a system from continuously making requests to a failing service, reducing unnecessary load and enabling faster recovery.

• Why it’s overlooked: Modernization teams often assume cloud-native platforms handle failure gracefully, ignoring the need for explicit fault tolerance mechanisms.
• Real-world example: Netflix employs the Circuit Breaker Pattern to maintain high availability in its microservices architecture. If a particular service fails repeatedly, the circuit breaker trips and prevents further calls until recovery.
• Consequence of ignoring it: Without circuit breakers, cascading failures can occur, where a single failing microservice can bring down an entire system due to unhandled retries and excessive load.
• When to use: Use this pattern in distributed systems where service failures must be isolated to prevent widespread outages.

Bulkhead Pattern

The Bulkhead Pattern isolates different components or services so that failures in one do not impact the others.

• Why it’s overlooked: Many teams focus on horizontal scaling but neglect to compartmentalize workloads, making services susceptible to systemic failures.
• Real-world example: In e-commerce platforms, checkout, inventory, and recommendation services can be isolated using bulkheads to ensure that failure in one does not affect the others.
• Consequence of ignoring it: Ignoring this pattern can lead to entire systems going down due to a single point of failure, significantly impacting user experience and revenue.
• When to use: Use this pattern in microservices architectures where services must operate independently to ensure resilience.

Event Sourcing Pattern

The Event Sourcing Pattern stores changes to an application’s state as a sequence of immutable events rather than modifying records directly.

• Why it’s overlooked: Teams often prioritize relational database models and transactional consistency, overlooking event-driven architectures that enhance auditability and scalability.
• Real-world example: Uber uses event sourcing to track rides, payments, and user interactions, ensuring that every action is recorded as an event for consistency and debugging.
• Consequence of ignoring it: Not using event sourcing can lead to data inconsistencies, loss of historical data, and difficulties in troubleshooting and replaying past transactions.
• When to use: Use this pattern in applications requiring strong audit trails, historical tracking, and event-driven state management.

CQRS (Command Query Responsibility Segregation) Pattern

CQRS pattern separates read and write operations into different models, optimizing for performance and scalability.

• Why it’s overlooked: Many teams’ default to CRUD-based architectures without considering read-heavy or write-heavy optimizations.
• Real-world example: E-commerce platforms like Amazon use CQRS to manage inventory updates separately from customer queries, ensuring high performance under heavy loads.
• Consequence of ignoring it: Ignoring CQRS can lead to database contention, performance bottlenecks, and inefficient scaling strategies.
• When to use: Use CQRS in high-performance applications where read and write workloads differ significantly.

Repository Pattern

The Repository Pattern separates the business logic from data access, providing a clean abstraction layer between application logic and database queries.

• Why It’s Overlooked: Modern ORM (Object-Relational Mapping) frameworks promise simplified data management, leading teams to believe explicit repository layers are unnecessary.
• Real-World Example: A healthcare software provider initially used direct ORM calls within service classes. As the system scaled, database logic became scattered, leading to maintenance challenges. Refactoring to use the Repository Pattern improved code organization and maintainability.
• Consequence of Ignoring It: Ignoring this pattern leads to tightly coupled code, making it harder to switch databases, optimize queries, or maintain separation of concerns.
• When to Use: Use this pattern when dealing with complex domain logic that requires a clean separation between business rules and data access.

API Gateway Pattern

The API Gateway Pattern acts as a single entry point for all client requests, routing them to appropriate backend services while handling cross-cutting concerns like authentication, logging, and rate limiting.

• Why It’s Overlooked: Teams may assume direct client-to-microservice communication is sufficient, leading to complex client logic and inefficient network calls.
• Real-World Example: A streaming service adopted an API Gateway to handle authentication, request aggregation, and traffic management across various backend services, improving performance and security.
• Consequence of Ignoring It: Without an API Gateway, microservices architectures can become fragmented, increasing security risks, inconsistent data access, and complex client-side logic.
• When to Use: Use this pattern when managing multiple microservices and requiring centralized handling of security, authentication, and request routing.

Modernization efforts should not only focus on adopting new technologies but also on leveraging proven design patterns to ensure scalability, resilience, and maintainability. Patterns like Strangler Fig, Saga, Circuit Breaker, Bulkhead, Event Sourcing, Repository, Sidecar, and CQRS provide essential strategies to ensure a scalable, resilient, and maintainable architecture. By integrating these patterns into modernization efforts, teams can avoid common pitfalls, minimize risks, improve system reliability, and create robust solutions for the future.

A version of this blog was first posted by Karsun expert Lakshman Maruri. Lakshman is an expert in our aviation portfolio. Connect with him on LinkedIn. Our Karsun Cloud Solutions experts use tools like those available in our Cloud Runways and Microservices Toolkits to accelerate transformation and build resilient, scalable architecture. Learn more at https://karsun-llc.com/innovation-center/modernization-and-transformation-toolkits/cloud-runways/

Whether a state or local government, or a U.S. government agency, a major problem facing government and industry alike is their aging mainframe systems. A shrinking workforce with experience in older languages such as COBOL, limits the ability of organizations to maintain these legacy systems and prepare for the future. This was apparent as states like New Jersey, facing strain on their systems during the early days battling COVID, called for assistance with their COBOL systems.

Now as federal agencies seek new opportunities to introduce efficiencies via artificial intelligence (AI), these systems provide an easy target for modernization. AI with its ability to tackle complex, repetitive tasks accurately provides one solution to the government mainframe modernization challenge. 

To this end, our Karsun Innovation Center experts devoted their time to producing a complete suite of AI solutions dedicated to tackling the mainframe modernization challenge.

The COBOL Modernization Challenge

With their decades-old interconnected applications and systems, these legacy mainframe systems are complex, expensive to maintain, difficult to change, and vulnerable to attacks. At the same time, the cloud presents the opportunity to build secure, resilient systems that adapt to the changing needs of its users.

Past modernization and migration attempts appeared out of reach for these systems. Stymied by both the cost and time required to transform these systems. These high-value assets handling mission-critical workloads present a high stakes challenge for modernization teams. Often core to the business with large customer bases, they have vast complex functionality that must be broken down into smaller parts before proceeding. 

Karsun applied AI, speeding up this tedious task while ensuring modernization teams had a complete map of the legacy system. Using its ReDuX platform, Karsun limited the impact of poorly documented systems and unanticipated relationships between components. When integrated with the other tools available in the ReDuX platform, AI-assisted teams generated production ready code two times faster than when they used humans alone.

Further given this complexity, some organizations may decide to simply replatform the system. But AI gives us the opportunity to do more than lifting and shifting the application to the cloud. It empowers us to optimize with a product mindset. With this in mind we can build human centered systems that scale and adapt to a changing environment.

The Solution: AI for Mainframe Modernization

Enter ReDuX, Karsun’s AI-powered digital transformation platform. We built our platform using AI to accelerate modernization while reducing risk. Using Amazon Bedrock, a platform that provides access to AI tools via an API, Karsun built a comprehensive digital transformation platform. Karsun’s ReDuX provides AI-enhanced tools for modernization team members from designers to developers in a single platform.

Using generative AI, the platform creates a complete blueprint of a legacy system, reducing risk, and generates behavioral insights. Using this information business analysts, designers, and others do more than rewrite these legacy systems, they reimagine an optimized system designed to match the needs of its users.

This product oriented process is enhanced throughout the modernization effort via the platform’s digital transformation agents. Using agentic AI, the modernization team can chat with their code for deeper understanding and apply reusable templates using automations to rapidly scale their work while reducing risk.

ReDux is already in use by the Karsun teams delivering large-scaled complex modernization solutions to our federal agency customers. We recently shared our experience on the AWS Public Sector Blog.  To learn more about AI Solutions for government modernization visit us here: https://karsun-llc.com/solutions/artificial-intelligence-ai-solutions/

Stuck with a difficult modernization challenge? Check out GoReDuX.AI and get unstuck today!

Happy Valentine’s Day from the Enterprise Modernization Experts! Today, we’re sending our love to the designers, developers, and data engineers who deliver excellence to our government customers and the American people. Driving this performance are the dedicated teams diving into the latest technology solutions. Among those cutting edge solutions are these digital transformation trends we love here at Karsun Solutions.

Designing with a Product Mindset

The Product Mindset empowers teams as they design applications in a way that adapts to the ever-changing digital landscape. As we enter a period requiring adaptability and a forward-thinking approach, the Product Mindset asks teams to consider how the system will be used now and in the future and how they can introduce efficiency now while building to scale. 

This mindset emphasizes moving from a “building” to a “dwelling” perspective. This focuses the process on continuous design as a form of continuous improvement. That includes modeling user experiences, using modern platforms, and implementing layered designs. Learn how Karsun does this with our Digital Transformation Toolkit in this white paper from the Karsun Innovation Center.

https://karsun-llc.com/resource/design-for-every-next-2/

Generative AI for Human-Centered Design

Another key digital transformation component is human-centered design (HCD.) This approach centers design using practices that identify whether the product matches the expectations of its human users. This is achieved by building in continuous feedback and using practices that assist product teams as they identify whether design supports or hinders users.

While human-centered design brings humanity back to software development, it also helps teams ensure the product is used as intended. It uses tools, like those built with machine learning (ML) and artificial intelligence (AI), to process user data and generate new insights. From these behavioral insights business analysts and others can more quickly generate requirements to ensure functionality carries over to the new systems built by the product team. 

As a final process improvement and efficiency-boosting feature, systems using Generative AI learn from the best practices of the development teams using these tools. When well integrated into platforms like Karsun’s ReDuX AI, the team may use AI to identify the practices that best help achieve their product goals. Based on these insights, the team can add guard rails, further engineer their model, or use agentic AI to generate code, templates, and other resources in line with those recommendations.

Zero Trust Architecture Boosted by Security-Led Practices

When using zero trust architecture (ZTA), systems continuously validate every interaction with the system. Using this approach, teams build systems that limit the ability of people and devices to access it, assuming that by default, they are not to be trusted. Zero trust best practices are essential when building secure architecture designed to adapt to changing needs and evolving threats. The ZTA approach includes well-architected practices. It also includes integrating emerging technologies. 

When teams use security-led practices, such as zero trust architecture and well-architected principles, they can now use AI to enhance their capabilities. That includes using predictive AI to identify risks and generative AI (GenAI) to address those hazards. Teams using our ReDuX AI-powered digital transformation engine use GenAI to proactively recommend best practices, generate guard rails based on established policies, and build templates so they can scale their practices more effectively. 

At Karsun, we design large-scale government IT solutions that enhance the capabilities of our agency partners and drive performance. Our commitment to solid digital transformation and design practices is part of our enduring commitment to technology solutions for every next. Our experts deliver these robust enterprise solutions at agencies across the federal government. At the same time, our innovators explore, prototype, and implement the latest technologies as part of delivering truly transformative solutions. If any of these trends pique your interest, we invite you to connect with our Karsun Innovation Center team and discover how you can begin your technology journey.

With National Data Privacy Week upon us, we reflect on the shifting data environment. From concerns surrounding artificial intelligence (AI) to an evolving threat environment, there is as great a need as ever to be mindful in our approach to protecting data. Our Karsun Innovation Center experts have met this challenge producing new solutions to address future requirements.

Our teams have a long history of incorporating emerging technologies into our data solutions. We build data platforms that produce meaningful insight while protecting sensitive data. Whether that occurs through machine learning (ML) for business intelligence or incorporating well-architected practices into our data-led migration. Moreover, many of these novel solutions incorporate machine learning and AI to further enhance data privacy. 

Utilizing Synthetic Data

Synthetic data enables enterprise data teams to innovate securely. This data is produced through machine learning using models that learn the patterns, structures, and relationships within the real dataset. Next, artificial, or synthetic, data is produced with similar statistical properties to the original data set. Thus, this process masks sensitive data, such as personally identifiable information (PII).

By providing high-quality datasets that mirror real-world data without exposing this data, synthetic data minimizes privacy risks, supports compliance with regulations like HIPAA, and reduces the impact of data breaches. Meanwhile, by allowing safe data sharing and model training, synthetic data accelerates AI and analytics development while ensuring ethical data practices, making it a powerful tool for balancing privacy and innovation. Introducing strong synthetic data practices can be one of the steps organizations can take to prepare their data for an AI-enhanced future. 

Security Automation

We can apply both predictive AI and generative AI (GenAI) and enhance organizations’ security posture by strengthening their security automation. While predictive AI identifies threats, GenAI creates new pathways to addressing security concerns. GenAI is particularly well suited to automating security. When incorporated into an AI-powered platform, it enhances safety culture by applying guardrails, best practice policies, templates, and automations that proactively address security concerns. GenAI further enhances security through its self-healing mechanisms that assess threats and then incorporate those assessments into its policy recommendations.   

GenAI to Meet Regulatory Requirements

GenAI also offers significant potential for meeting regulatory requirements as modernization teams migrate legacy systems. We have written about this extensively on the ReDuX website. ReDuX is our AI-powered platform that accelerates mainframe modernization. One component of that platform is a mapping feature that builds a blueprint of the legacy system. With an enhanced understanding of the legacy system, the team avoids security pitfalls, identifying functional code while removing dangerous dead code and reducing the risks and errors. Moreover, using a platform with built resources allows teams to introduce guardrails like those used to improve security automation.

Bringing It Together

Consider then a data project where each of these methodologies is included as part of a robust data practice. First, synthetic data is produced using machine learning. When combined with AI-assisted development, like that used by ReDuX, the security automation guardrails enforced by the AI-powered platform ensure proper security tools and practices, including those applied to synthetic data, are used properly every time. Then, as AI practices evolve, they are refined further.  

AI/ML helps technology teams navigate complex regulatory landscapes, including compliance with standards like HIPAA, FISMA, and GDPR, to ensure data privacy and system security. By adopting an AI-enhanced approach, agencies can protect privacy, overcome regulatory challenges, and maintain secure and resilient applications to align with their data goals. To learn more about emerging technology from the Innovation Center, visit our Projects Page or take the first step on your data modernization journey and connect with us on our Data Solutions Page. 

At a time when the tech industry is rapidly evolving and competition for top talent is fierce, Karsun Solutions is making significant strides in creating a workplace that not only attracts top professionals but also nurtures their growth and innovation. Its growing accolades for its workplace, growth, and innovation reflect the company’s unwavering commitment to fostering a people-first culture that prioritizes employee satisfaction and development.

Celebrating A Regional, National and Industry-Wide Culture Leader

Karsun picked up new Top Workplaces awards for culture over the course of this year. Rankings on these regional, national, and Culture Excellence lists are determined based on anonymous surveys filled out by employees, conducted by third-party research firm Energage. In total, Karsun received 12 Top Workplace awards in 2024. These include awards for teams in the Washington, D.C., and Oklahoma City regions, for its remote teams, and for cultures that promote Innovation, Purpose and Values, Work-Life Flexibility, Employee Appreciation, and more.

As Karsun now appears on some lists for the third year in a row, these Top Workplaces awards are indicative of genuine sentiments within the organization. Energage, which administers these surveys, focuses on key aspects like alignment, execution, and connection. Karsun’s inclusion in this esteemed list underscores the company’s success in these areas, demonstrating a strong alignment between company goals and employee roles, efficient execution of tasks, and a deep sense of connection among team members.

Technology Industry Leadership

Among its awards for employer culture, Karsun received repeated recognition as a Technology Industry Top Workplace. It was also recognized locally for its leadership within the public sector information technology industry. Ongoing investment in innovation drives this growth. Here too, Karsun received recognition. This year, it was named a finalist for the 2024 Moxie Award, which honors boldness in business across various sectors, including government contracting and technology. Karsun was specifically recognized in the GovCon 300+ Employees category, reflecting its growing impact and scale.

Another significant accolade came from the Northern Virginia Technology Council (NVTC), which named Karsun to its 2024 Tech 100 list. This recognition celebrates visionary companies and leaders in the technology industry. Karsun’s inclusion speaks to its ongoing leadership in enterprise modernization and digital transformation.

The Karsun Innovation Center (KIC) plays a pivotal role in maintaining this leadership position. The center spearheads research and development initiatives, such as the ReDuX AI toolkit, which leverages generative AI to streamline the migration and modernization of legacy mainframe systems. By integrating emerging technologies, Karsun ensures that its solutions remain relevant and effective, helping government agencies meet their missions.

A Call for Experts and Innovators

As Karsun Solutions embarks on its fifteenth year of transforming government IT solutions, it continues to seek passionate individuals ready to accelerate their careers. The company’s mission to modernize enterprise solutions for federal agencies opens numerous avenues for professionals in software development, cloud computing, AI, and data analytics.

Are you ready to explore endless possibilities and turn bold ideas into reality? With Karsun’s commitment to innovation, excellence, integrity, and collaboration, there has never been a better time to join this forward-thinking organization. Join a company that empowers you to expand your potential and make a lasting impact. Visit KarsunCareers.com/jobs and take the first step as you Find Your Next, transforming your career with Karsun Solutions.