From Data Islands to Defensible Intelligence: Modernizing Public Sector Transportation Infrastructure

Posted on by Travis Escoffery

Across the United States, transportation agencies are operating in a moment of historic opportunity, and equally significant pressure. With more than $200 billion in capital funds required to be obligated before the 2026 deadline, agencies are tasked not only with delivering projects at scale but also with doing so with a level of transparency, accountability and precision that withstands public and regulatory scrutiny.

Yet while funding has accelerated, many of the systems used to manage transportation programs have not kept pace with the complexity of the initiatives themselves. The result is a growing disconnect between project activity in the field and decision-making at the program level.

Closing that gap requires more than new tools. It requires a shift from fragmented data to defensible intelligence.

The New Reality: High Stakes, Limited Visibility

Transportation leaders today are navigating a complex operating environment shaped by three converging pressures:

Federal funding deadlines and obligation requirements that leave little room for delay
Technical complexity, where construction teams must not only lead traditional construction effort, but also the tech associated with those projects
Increased audit and compliance scrutiny, requiring agencies to demonstrate clear, traceable use of public funds

Individually, these challenges are manageable. Together, they expose two systemic issues: limited visibility across the capital program lifecycle and unnecessary complexity.

Without a unified view of project information, cost, field activity and performance, agencies are

often forced to rely on lagging indicators, manual reporting and disconnected systems, making it difficult to act with confidence.

The Persistence of Data Silos

Despite advances in digital tools, many Public Sector transportation programs still operate across fragmented environments:

Field data is captured inconsistently or stored locally
Financial tracking exists separately from project execution
Compliance documentation is often assembled in an ad hoc manner
Key intelligence gathering during the build phase is often not handed off to operational teams

This creates what can be described as data islands, pockets of information that are not easily connected, validated, or scaled across the portfolio.

The implications are significant:

Delayed decision-making due to incomplete or outdated information
Inconsistent reporting across projects and stakeholders
Limited ability to identify risks early
Increased exposure during audits and compliance reviews

In this environment, even well-managed projects can appear fragmented at the program level, making it difficult to demonstrate accountability with confidence.

A Shift Toward Defensible Intelligence

To address these challenges, transportation agencies are beginning to rethink how data is structured, governed and used across the lifecycle of capital programs.

This shift can be understood as a move from data collection to defensible intelligence.

A defensible approach ensures that:

Data is captured consistently from the field
Information is standardized across projects
Data is not only collected, but analyzed to proactively mitigate risk
Documentation is audit-ready at every stage, not just at project closeout

At its core, this is about establishing a system of record that allows teams to shift from looking at projects in the rearview window after the fact, to having clear project visibility through the entire asset lifecycle.

Building the Foundation: Governance & Clarity

The first step in this transformation is strengthening governance.

Adoption as a Prerequisite for Insight

Even the most advanced systems fall short if they are not consistently used. In transportation programs, where multiple stakeholders, contractors and teams are involved, adoption is critical to ensuring that data is both accurate and timely.

An adoption-first approach helps ensure:

Consistent data capture across the field
Standardized workflows across projects
Greater confidence in reporting and analytics

Establishing Secure, Traceable Oversight

Given the scale of public investment, transportation agencies must demonstrate fiduciary responsibility at every stage of a project.

This requires:

A clear audit trail of decisions, approvals and changes
Centralized access to financial and project data
Alignment with Federal security and compliance standards

Advancing the Model: Connected Control

With a strong governance foundation in place, agencies can begin to unlock the next level of capability: connected control over project delivery.

Improving Responsiveness Through Visibility

Access to timely, integrated data allows program leaders to:

Identify schedule variances as they emerge
Understand cost impacts in context
Drive corrective actions, whether on site, at the office or on the Hill
Use historical data to make informed forecasting decisions today

This represents a shift from retrospective reporting to proactive program management.

Bridging Construction and Operations

One of the most persistent challenges in transportation infrastructure is the transition from construction to operational readiness.

When systems are disconnected:

Critical asset data may be lost or duplicated
Operations teams lack visibility into construction decisions
Time to project delivery is delayed

By maintaining continuity of information across the lifecycle, agencies can:

Enable smoother transitions into active service
Reduce rework and data re-entry
Support long-term asset management from day one

Looking Ahead: A More Connected Future for Transportation Programs

The modernization of transportation infrastructure is not solely a matter of funding or scale. It is increasingly a matter of data maturity.

Agencies that continue to rely on fragmented systems may find it difficult to keep pace with evolving requirements around compliance, reporting and delivery speed.

Those that invest in connected, well-governed data environments will be better positioned to:

Navigate funding deadlines with confidence
Respond to issues in real time
Demonstrate accountability across the full lifecycle of their programs

As transportation programs grow in complexity and visibility, the need for clarity, consistency and control becomes more critical.

Moving from data islands to defensible intelligence is not just a technology shift; it is an operational one. It reflects a broader evolution in how agencies plan, deliver and oversee infrastructure in a high-stakes environment.

By strengthening governance and enabling connected control, Public Sector transportation leaders can build not only infrastructure, but also predictability, transparency, accountability and efficiency.

Ready to improve visibility and control across your transportation projects? Connect with us.

Keep More, Store Less: The Case for Advanced Compression in Federal EDR

Posted on by Richard Verwers

How agencies can retain full-fidelity data without overspending on storage

Endpoint detection and response (EDR) depends on data. The more telemetry you collect, the more context you have to detect threats, investigate incidents and meet Federal compliance requirements.

But data volume is also the problem. Federal agencies generate massive amounts of endpoint telemetry every day. Process activity. File changes. Network connections. User behavior. Multiply that across thousands of devices and storage requirements quickly grow beyond what many teams can sustain.

Security teams often face a difficult tradeoff: retain full-fidelity data and absorb higher storage costs, or limit retention and risk losing critical visibility.

That tradeoff is no longer necessary. Advanced data compression changes the economics of endpoint visibility. Agencies can retain unfiltered telemetry for extended periods without expanding storage budgets or adding operational complexity.

The Visibility–Storage Tradeoff is No Longer Sustainable

Federal cybersecurity requirements continue to raise the bar for telemetry collection and retention. Agencies must support Zero Trust initiatives, continuous monitoring programs and audit readiness. Modernization efforts increase the number of connected endpoints, including cloud workloads, remote systems and contractor-managed devices. Each new endpoint expands the telemetry footprint.

At the same time, budgets remain under scrutiny. Storage infrastructure must compete with other mission priorities and security leaders must justify every dollar. When storage costs climb, teams often respond in predictable ways:

Reduce retention windows
Sample or filter telemetry
Drop lower-priority event types
Offload data to external archives that are difficult to query

Each of these approaches creates blind spots. Shorter retention windows limit historical investigations and filtered data weakens threat hunting while fragmented storage slows response times.

In a threat context where adversaries can dwell quietly for months, incomplete data is a liability. Agencies need a way to collect and retain comprehensive telemetry without creating unsustainable storage growth.

Compression-First Architectures Improve Data Retention

Traditional security platforms treat compression as an afterthought. Data is collected at scale, stored in raw or lightly optimized formats and compressed later in the pipeline. By then, infrastructure costs are already locked in.

A compression-first architecture takes a different approach. Advanced compression techniques reduce data size at ingest. Telemetry is optimized as it enters the platform, not after it has consumed storage resources. The result is a significantly smaller storage footprint without sacrificing fidelity. For Federal security operations centers (SOCs), this shift has meaningful impact:

Longer retention without higher cost – Agencies can retain 180 days or more of full-fidelity telemetry while remaining within budget constraints.
Unfiltered visibility – Teams do not need to decide in advance which data might matter later. They can keep it all.
Faster investigations – Optimized storage enables efficient querying across large datasets, supporting threat hunting and incident response.
Simplified architecture – Native compression reduces the need for external storage tiers or complex archival systems.

Instead of managing tradeoffs, security teams regain flexibility.

Full-Fidelity Data Supports Compliance and Zero Trust

Federal mandates increasingly require measurable security maturity. Continuous monitoring, device-level visibility and documented audit trails are central to that effort, and retention depth matters.

When agencies can access complete endpoint histories, they strengthen their ability to:

Validate Zero Trust controls within the device pillar
Reconstruct events during forensic investigations
Demonstrate compliance with evolving Federal security requirements
Support reporting obligations tied to vulnerability and risk management

Short retention windows make it harder to answer fundamental questions: When did this behavior begin? Was lateral movement attempted? Did similar activity occur on other systems?

With compressed full-fidelity data, those questions become easier to answer and teams can look back months, not days. This level of historical visibility supports stronger analytics, more informed risk decisions and more defensible reporting.

Cost Efficiency Matters Under Federal Scrutiny

Every Federal technology investment must demonstrate operational value. Advanced compression directly addresses cost concerns in several ways:

Reduces total storage consumption
Delays or eliminates additional infrastructure purchases
Lowers operational overhead tied to managing multiple storage systems
Minimizes data movement between tiers

At the same time, it strengthens the overall security posture by preserving data that might otherwise be discarded. This combination of efficiency and depth is particularly important for agencies balancing modernization initiatives with budget discipline.

Security cannot become a cost center that expands without limit. It must scale responsibly. Compression-first EDR architecture supports that balance.

The Federal security community no longer needs to accept a compromise between cost and visibility. Advanced data compression enables agencies to:

Collect unfiltered endpoint telemetry
Retain data for extended periods
Support Zero Trust maturity
Strengthen investigative capabilities
Maintain fiscal discipline

As agencies define the next standard for Federal EDR, data strategy must be part of the conversation. Retention, accessibility and efficiency determine whether telemetry delivers long-term value.

Carbon Black and Carahsoft help Federal agencies adopt a compression-first approach to endpoint detection and response, so teams can keep more data, store less and operate with confidence.

Contact us to learn how your agency can adopt a compression-first approach to endpoint visibility while staying within budget.

Carahsoft Technology Corp. is The Trusted Government IT Solutions Provider, supporting Public Sector organizations across Federal, State and Local Government agencies and Education and Healthcare markets. As the Master Government Aggregator for our vendor partners, including Broadcom, we deliver solutions for Geospatial, Cybersecurity, MultiCloud, DevSecOps, Artificial Intelligence, Customer Experience and Engagement, Open Source and more. Working with resellers, systems integrators and consultants, our sales and marketing teams provide industry leading IT products, services and training through hundreds of contract vehicles. Explore the Carahsoft Blog to learn more about the latest trends in Government technology markets and solutions, as well as Carahsoft’s ecosystem of partner thought-leaders.

The Role of AI Infrastructure in Government

Posted on by Mike Adams

To maintain its place as a leader in AI advancements, and to comply with the latest White House guidance, Government agencies must harness AI capabilities, such as secure cloud computing platforms, high-performance data processing systems and scalable machine learning frameworks, for critical functions such as cybersecurity, predictive analytics and economic competitiveness. As with any new technology, AI requires updated infrastructure to power these advanced capabilities.

The Capabilities of AI Infrastructure

AI infrastructure refers to the hardware and software needed to create and deploy AI-powered applications and solutions. It enables both AI, the technology that simulates the way people think, and machine learning (ML), a focus area of AI that utilizes data and algorithms to imitate the way humans learn, increasing the accuracy of its results the more data you input. AI infrastructure enables users to create and deploy AI and ML apps, such as chatbots, facial and speech recognition and computer vision.

Building the infrastructure for AI requires data storage and processing, compute resources, ML frameworks and MLOps platforms to acquire the processing capabilities needed for AI, and also to train ML models.

AI Infrastructure Deep Dive

Below are the six pillars that define a strong AI foundation, each continuously evolving to keep pace with the next generation of AI capabilities.

Specialized Compute
In 2025, AI solutions rely on more than GPUs, they use a mix of processors designed for different types of AI tasks. This makes it faster and more cost-effective to train, update and run today’s complex models. As AI systems are becoming more advanced, many models are becoming larger and require HPC solutions. On the other hand, smaller models can run on cloud-based architecture for lower compute needs.

Data Preparation

The success of an AI solution can tie back to how well the data is prepared before it’s used. Modern AI infrastructure now includes built-in tools to clean, label and organize data at scale, sometimes using AI itself to automate the work. This ensures models are trained on accurate, relevant information, while also tagging and tracking data to meet security, compliance and transparency requirements.

Data Storage
Because today’s AI solutions are becoming more and more advanced, additional data is required to train the models. AI now depends on lightning-fast data storage that can easily grow alongside datasets. New tools also make it possible to keep sensitive data in specific locations or environments, meeting strict privacy and Government requirements without slowing down AI workflows.

Networking
As AI models get bigger, the speed of moving information between systems is critical. New high-speed networks reduce delays so AI can process and deliver results in near real-time, even across large environments.

Software & Orchestration
Managing AI today requires controlling the entire process from development to deployment. Modern platforms help teams easily update models, track their history and ensure they run efficiently whether in the cloud, on-premises, or in secure Government networks.

Security & Governance
AI infrastructure in 2025 is built with security at its core. It goes through rigorous testing to ensure it meets Government compliance standards and protects sensitive information. It is important to choose solutions from providers that continuously monitor their models, ensuring they’re safe, reliable and ready to be audited at any time.

All these AI Infrastructure features will be utilized by Government agencies to enable AI solutions that improve workflows and maintain global competitiveness.

AI Infrastructure: A National Priority

Executive Order 14141 names AI infrastructure, including data centers and compute clusters that are powered by clean energy, as a national priority to upholding U.S. leadership, national security and competition.

The order encourages Government agencies to secure supply chains, integrate clean energy and collaborate with the private sector. It also directs Federal agencies to make Federal lands and sites available for clean power generation and gigawatt-scale AI data centers

In alignment with the Executive Order, the Department of Energy (DOE) has released a Request for Information (RFI) to use its territories to build AI infrastructure datacenters, citing that they would enable AI training and inference, scientific research and other essential services.

Most recently, the AI Action Plan outlines recommended policy actions regarding building AI infrastructure such as data centers, semiconductor manufacturing facilities and energy infrastructure. The goal of the AI Action Plan is to streamline AI adoption and, in turn, speed up and scale the development of AI infrastructure on the federal level. National Security, AI incident response, cybersecurity and secure-by-design systems are highlighted as vital pillars of the AI Action Plan’s infrastructure guidance. By sharing specific steps to achieve safe and secure AI infrastructure, such as identifying available federal land, training our workforce, building data centers and keeping security at the backbone, the AI Action Plan outlines clear next steps that agencies need to take in order to push AI adoption forward.

In an increasingly technology-driven landscape, AI infrastructure allows Government agencies to modernize their operations and deliver more efficient, responsive services. Strategic investment in AI infrastructure enables agencies to enhance decision-making processes, reduce operational costs, protect national security interest and fulfill their core mandate of serving citizens. Once this foundation is in place, agencies can begin to build and deploy solutions that directly support their missions. The next blog in our series will explore how this infrastructure enables Generative AI and its potential for transforming Government workflows.

Carahsoft’s ecosystem of hardware and software vendors are equipped to connect agencies with the latest technology for AI, including the infrastructure needed to run it. To learn more about AI infrastructure solutions that are tailored for the Public Sector, visit Carahsoft’s Page on AI Solutions.

Accelerating The Healthcare AI Revolution: Reasoning Models and Data

Posted on by Debojyoti (Debo) Dutta

The healthcare industry stands at the precipice of transformation. While artificial intelligence (AI) has been utilized in healthcare for decades, analyzing OMICS and supporting drug discovery, recent advancements in generative AI (GenAI) and reasoning models are redefining what’s possible, especially when connected to private data. This evolution represents not just incremental improvement but a fundamental shift in how technology can augment healthcare delivery.

The Accelerating Pace of AI Evolution

The GenAI movement that emerged around 2017 added a new dimension, enabling AI to create content. However, it was the 2022 release of ChatGPT that democratized access to these capabilities, creating a “Wright Brothers moment,” springboarding the industry of AI. Suddenly, everyone from children to healthcare professionals began experimenting with these systems, often finding productivity gains despite the limitations of early versions of the technology.

Just as organizations were adapting to this new reality, reasoning models emerged in late 2024. These systems do not simply generate content, but think through problems step by step, mirroring human cognitive processes. Within months, more efficient, open-source reasoning models followed, making this technology accessible even for regulated industries like healthcare (e.g. Med-R1 8B).

GenAI Reasoning Models in Healthcare

GenAI enables healthcare professionals to work more efficiently, freeing time to engage with patients. Unlike earlier models, recent GenAI reasoning models provide transparency into their decision-making process. These models can now power advanced AI agents using healthcare-specific models like Google AIM, Med-PaLM 2 or Med-R1. This auditability is crucial in healthcare, where understanding why a recommendation was made is often as important as the recommendation itself.

Before implementing AI agents and reasoning, agencies should define clear outcomes and goals. Here are several factors to consider when integrating GenAI into your agency:

Data Strategy: The effectiveness of AI models depends significantly on the quality and privacy of your data. Organizations need clear protocols for creating evaluation datasets and managing sensitive patient information that can be kept sovereign.
Infrastructure Decisions: Healthcare organizations must decide whether to deploy models in the cloud or on-premises, considering regulatory requirements and data sensitivity. A hybrid approach often provides the flexibility needed to address various use cases.
Model Selection: Open-source models now trail proprietary options by only about six months in capabilities while offering cost advantages and greater control. Many organizations are adopting hybrid strategies, using proprietary models for cutting-edge applications and open-source alternatives for routine tasks.
Scale Considerations: Small, specialized language models can be more efficient for specific healthcare tasks, while larger models may be necessary for complex reasoning about treatment options or research questions.

Agencies should prepare robust data governance frameworks and flexible infrastructure that spans cloud and on-premise environments to enable healthcare personnel to use GenAI effectively. Overall, GenAI enables healthcare professionals to work more efficiently, enabling them to connect more with patients.

Your Journey to an AI Future Starts Now

The future of healthcare will be augmented by reasoning models, making healthcare more affordable and accessible for all.

Some new, AI-driven areas to watch for include:

Data Interaction: LLMs will navigate complex healthcare data ecosystems, from electronic health records to genomic data, answering nuanced clinical questions without requiring complex programming.
Planning and Research: By functioning as collaborative partners in research, the models look to help design clinical trials, analyze research literature and develop treatment protocols.
Actionable Workflows: Reasoning models will help automate clinical and administrative processes while incorporating human feedback in a continuous improvement cycle.

AI agents will begin to help address the acute staffing shortages plaguing healthcare systems worldwide. These digital assistants can handle routine documentation, answer common patient questions, and provide decision support, allowing clinicians to focus on direct patient care. As AI systems become more affordable and consumption increases, we’re likely to see a revolution in healthcare accessibility, particularly for underserved populations, with AI agents augmenting healthcare workers’ efforts.

The journey toward AI-augmented healthcare is accelerating faster than most experts predicted. For healthcare leaders, the question is no longer whether to embrace these technologies, but how to implement them to improve care while maintaining the human connection that defines healthcare.

The content of this blog was pulled from the Healthcare Information and Management Systems Society (HIMSS) panel, “Accelerating Enterprise GenAI.” To learn more about Nutanix GenAI, visit Nutanix’s AI Solution page.

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