Federal missions operate across complex, distributed environments, from secure data centers to cloud enclaves and tactical platforms in disconnected conditions. Artificial intelligence (AI) must now match this operational agility.
Hybrid AI integrates cloud, on-premises and edge compute, enabling intelligence where and when it is needed. Whether inside a SCIF, within a FedRAMP-moderate enclave or in contested environments, hybrid architectures ensure trusted intelligence is continuously available to support mission outcomes.
Why Hybrid AI is Mission-Critical for Federal Agencies
As mission data becomes more dynamic and dispersed, centralized compute models alone cannot meet operational demands. Agencies must process, generate and act on information securely, whether in the field, across partner networks or in highly regulated environments.
Hybrid AI brings compute to the data, respecting governance and sovereignty while maintaining flexibility. AI capabilities must function reliably in environments where connectivity is degraded or unavailable, and where data cannot move freely due to classification or jurisdictional constraints.
This ensures real-time inference and decision support at the point of need while safeguarding CUI, PII and FOUO data under FISMA, EO 14110 and Zero Trust principles. AI-powered insights remain accessible even when the network does not.
The Technology Foundations of Mission-Ready Hybrid AI
Data sovereignty is essential Agencies must process, train and infer within regulatory boundaries, maintaining full control of sensitive data across its lifecycle, from edge ISR streams to classified model development. Containerized and optimized AI software must run flexibly across accelerated environments, from enterprise cloud to air-gapped data centers.
Infrastructure must scale seamlessly Hybrid environments enable compute to move across core, cloud and field deployments, keeping AI aligned with changing mission needs.
Accelerated computing powers mission AI Advanced generative and deep learning models demand high-efficiency, accelerated compute platforms. Hybrid AI leverages this capability to deliver high-throughput, low-latency insights not only in data centers but also at the tactical edge—essential for mission-aligned generative AI and emerging agentic applications.
Interoperability drives flexibility Containerized AI microservices and API-driven architectures ensure seamless integration with mission platforms like health and geospatial, while enabling secure, policy-compliant operations across hybrid environments. Architectures should also support flexible integration of retrieval pipelines and evolving data governance models, ensuring mission intelligence is grounded in trusted, up-to-date sources.
Real-World Applications: Hybrid AI in Action
Agencies are applying hybrid AI today to extend mission capabilities beyond what centralized architectures allow.
In public health, sovereign data platforms combined with edge analytics support real-time outbreak modeling and informed containment planning. Disaster response teams ingest and analyze aerial imagery and IoT data locally, providing actionable insights even when disconnected from central networks.
Generative AI is transforming document-centric workflows. It accelerates the summarization of complex reports and regulatory analysis while maintaining strict control over sensitive content.
Sovereign AI innovation is advancing rapidly. National AI clusters allow agencies to train and refine models domestically, ensuring compliance with governance mandates while enhancing operational independence. Many of these efforts begin under SBIR, OTA or BPA contracts and evolve into modular architectures that scale with mission requirements.
Key Considerations for Building Hybrid AI
Hybrid AI success requires intentional architecture, policy fluency and alignment with mission realities.
Architectures must enable agility, supporting rapid adaptation to evolving mission needs, data sources and model advancements. Flexibility ensures AI remains relevant as both operational risks and opportunities evolve. Hybrid environments should also be designed to support emerging model types, including multi-modal, agentic and retrieval-augmented AI, and to accommodate evolving policy mandates.
Interoperability is essential. Open, standards-based pipelines and containerized services enable integration with evolving toolchains, partner ecosystems and commercial innovation while maintaining governance.
Federal leaders are using hybrid architectures to operationalize responsible AI principles outlined in EO 14110. Early alignment with procurement vehicles—OTAs, GWACs and BPAs—ensures scalable, policy-ready architectures. High-impact use cases, such as edge-deployed generative AI assistants and sovereign model training pipelines, continue to demonstrate the value of this approach.
Next Steps for Federal AI Leaders
Hybrid AI represents an inflection point for Federal missions. Leaders who invest in scalable, policy-aligned AI infrastructure today will be positioned to harness tomorrow’s AI innovations at mission speed.
By supporting secure, accelerated AI capabilities across edge, cloud and on-premises environments, hybrid architectures help agencies maintain operational advantage in any scenario. The focus is not just on deploying AI models, but on building adaptive infrastructure that delivers intelligence wherever the mission requires it.
Hybrid AI architectures also lay the operational foundation for the emerging era of AI Factories—systems that continuously generate, adapt and deploy intelligence at scale, across mission environments.
Federal leaders who establish this foundation today will ensure that AI serves the mission with the trust, agility and resilience it demands—and with the flexibility to evolve alongside the accelerating pace of innovation.
What makes one agency able to move artificial intelligence (AI) into mission production in days, while another still navigates the same barriers months or even years later? The answer isn’t technical talent or budget alone. It’s whether infrastructure is intentionally built to support velocity, trust and scale.
As Federal leaders sharpen their focus on operational AI, speed is becoming the key differentiator. Not speed for its own sake, but speed that is purposeful, compliant and aligned with outcomes the public and the mission demand. Moving AI from pilot to production quickly now defines AI leadership in Government.
Rethinking AI Readiness for Federal Missions
Simply demonstrating isolated AI successes is no longer sufficient. Federal agencies are now expected to embed AI into core workflows, drive outcomes and uphold public trust. CAIOs are shifting focus from pilots to impact. That shift requires more than technical oversight; it demands leadership that can drive operational change and enable the workforce to prioritize higher-value work.
Scaling mission-aligned AI requires rethinking old norms. Agencies embracing this shift are achieving faster deployments, greater agility and increased transparency, while others risk getting stuck in pilot mode without the proper foundation.
Building the Foundation for Mission-Aligned AI
Reliable acceleration comes from an intentional foundation, not shortcuts. Agencies moving AI from concept to capability consistently align strategy, data, infrastructure, teams and governance from the outset.
Mission Strategy First
Successful AI efforts prioritize mission impact over technical novelty. Clear goals ensure leadership, infrastructure and resources move in sync toward measurable outcomes.
Data That Moves at Mission Speed
AI needs fast, secure access to trusted structured and unstructured data. Retrieval-based architectures anchored in vetted sources support both performance and privacy.
Scalable, AI-Optimized Infrastructure
Traditional IT can’t handle AI’s demands. Agencies moving at mission speed rely on infrastructure optimized for accelerated computing and seamless operations across domains.
Integrated, Agile Teams
Scaling AI takes more than data science. Cross-disciplinary teams aligned on outcomes and able to deliver in agile cycles are key.
Compliance as an Enabler
Built-in transparency and risk management turn compliance into an asset. Agencies that embed governance early shorten ATO timelines and boost public trust.
A Roadmap for Responsible Acceleration
Moving fast without structure is risky. Moving fast with structure enables repeatable, responsible AI delivery. A maturity roadmap helps agencies balance acceleration with alignment to Federal guidance.
1. Baseline Assessment
Clear visibility into current data maturity, infrastructure readiness, governance posture and workforce capabilities helps agencies prioritize investments. Addressing common gaps, like fragmented data pipelines and siloed teams, systematically gives AI initiatives a foundation that scales without risk.
2. Mission-Driven Objectives
Successful AI leaders define what “mission success” looks like in concrete terms. This discipline prevents overbuilding, keeps efforts tied to operational outcomes and builds clear value stories to sustain leadership support.
3. Phased Testing Environments
Test beds and controlled environments provide space to validate AI approaches before full production. These environments foster safe iteration, surface governance needs early and create reusable patterns that accelerate future deployments.
4. Continuous Model Feedback
AI systems must adapt over time, not just at launch. Embedding continuous monitoring, performance tuning and user-driven feedback ensures models remain mission-relevant and trustworthy as operational contexts evolve.
From Use Case to Outcome: What Speed Requires
Agencies moving AI into production quickly focus on the right use cases. Logistics optimization, document analysis and fraud detection are examples of areas where AI at mission speed delivers immediate benefit.
Another key enabler is avoiding unnecessary reinvention. Pre-trained, enterprise-grade models tailored to agency needs dramatically reduce development time.
Modern platforms that support containerized deployment and orchestration of AI microservices across cloud and on-prem environments accelerate this process. Agencies gain flexibility to optimize cost, performance and control based on mission needs. Modular, adaptable architectures also help avoid lock-in and support evolving policy and security requirements.
Security and compliance must be integrated from day one. Systems aligned with FedRAMP, FISMA and Executive Order 14110 requirements to avoid rework that can stall even well-intentioned efforts late in the process.
The Capabilities That Make Rapid AI Possible
To deploy AI at mission speed, infrastructure must deliver scalability, explainability, risk management and collaboration-readiness.
Systems must handle expanding data sources, dynamic mission demands and increased user load without degradation. Models must produce outputs that analysts, operators and oversight bodies can trust and interpret.
Ethical risk management must be proactive, not reactive. Bias checks, audit trails and transparency must be built in from training through ongoing monitoring. Collaboration across agencies and partners must be seamless to maximize impact and minimize duplication of effort.
These capabilities must be grounded in alignment with Federal frameworks such as the AI Risk Management Framework and GSA’s AI guidance. Infrastructure that is “policy-ready” supports faster delivery and greater trust in outcomes.
Leading with Principles That Scale
For Federal AI leaders, the challenge is scaling AI to deliver real mission outcomes while maintaining public trust. Success requires investing in scalable, policy-aligned infrastructure and fostering a culture where speed and governance go hand in hand.
Sustainable, enterprise-wide impact demands leadership that connects vision with execution. The CAIO must drive cross-agency collaboration, operational change and continuous feedback to keep AI responsive to evolving mission needs.
Fast, Mission-Driven AI is Achievable—If You Build for It
Deploying AI in days—not months—is possible when infrastructure, strategy and culture align to support it. Agencies embracing this imperative are setting the pace for responsible, impactful AI in Government.
When AI systems are grounded in mission need, accelerated by the proper infrastructure and governed with intention, they enable something bigger: a Government workforce empowered to focus less on routine tasks and more on the high-impact decisions and public outcomes that matter most.
For Federal AI leaders, the opportunity is now: to move from pilot to production with velocity, governance and trust—and to deliver mission outcomes at a speed that matches the urgency of the moment.
You’ve launched artificial intelligence (AI) pilots and proven their initial value. Now comes the harder question: how do you scale that progress without disrupting core operations or exceeding current system constraints? For Government AI leaders, the goal isn’t just AI adoption—it’s enabling AI evolution through resilient infrastructure that aligns with mission continuity and operational control.
Many agencies face the same tension. They need modernized systems to meet new expectations from Executive Order 14110 and similar mandates, without risking service downtime or fragmenting mission workflows. This requires moving beyond piecemeal integration and toward a scalable, secure and interoperable AI deployment architecture that fits within existing environments.
From Integration to Evolution
Agencies often begin with targeted AI pilots or API-based tools. But real progress means transitioning to infrastructure designed to support high-reliability, mission-aligned AI deployments at scale. AI stacks built for performance, observability and governance, not just experimentation, will allow agencies to achieve this progress.
What does this look like in practice? It means infrastructure that supports model training, inference, lifecycle management and secure data movement are all underpinned by capabilities like versioning, rollback, audit logging and support for MLOps practices. These capabilities help ensure operational readiness as agencies move from pilot to production.
This evolution doesn’t require scrapping functional systems. By using modular designs and accelerated computing, agencies can layer AI capabilities onto their existing IT backbones. Compatibility with containerized environments and orchestration tools enables phased implementation, which reduces duplication, minimizes disruption and supports operational continuity.
What to Look for in a Modern AI Infrastructure
Adaptable and Modular Design Agencies benefit from modular infrastructures, with reusable building blocks such as containerized microservices, pre-trained models and policy-controlled pipelines. Modern designs accelerate deployment while maintaining alignment with internal security and governance frameworks’ practices.
Deployment Flexibility Support for on-premises, hybrid and Government-authorized cloud environments ensures that sensitive workloads can be managed without vendor lock-in. AI capabilities should be deployable across systems with varying levels of connectivity, compliance and mission assurance requirements.
Embedded Security and Compliance Encryption, runtime integrity checks, secure boot and audit trails with access controls must be native, not bolted on later. Compliance-readiness for frameworks like FedRAMP, NIST and digital sovereignty requirements is critical in regulated environments. These controls support zero-trust principles and enable responsible AI deployment across sensitive Government workloads.
Performance and Scale AI workloads, from large-scale model training to low-latency inference, require optimized systems. Optimizations may include high-throughput, accelerated computing and GPU-based operations. Support for retrieval-augmented generation (RAG) can further extend GenAI capabilities by safely leveraging agency-specific grounded, context-aware outputs aligned with mission requirements.
Modernization Without Disruption
A step-by-step modernization plan helps agencies validate functionality, performance and alignment before scaling enterprise-wide. AI infrastructure should offer version control, rollback capabilities and seamless patching to reduce service risks in live environments.
Integration with legacy systems is equally vital. AI systems must coexist with core IT functions, avoiding the need for redundant tooling or excessive abstraction layers. Using standardized APIs and interoperable components helps limit rewrites and eases workforce adoption.
Cost containment and alignment
Managing cost also plays a central role. Modular infrastructure helps reduce unnecessary spend, avoids one-off duplications across programs and supports coordinated cross-agency deployments, especially as centralized AI procurement strategies evolve.
Building a Future-Ready AI Strategy
Lifecycle Alignment AI Infrastructure should span the entire lifecycle, from data ingestion and labeling to training, inference, deployment, monitoring and governance. Gaps between these phases introduce risk and slow down scaling.
Support for What Already Works Agencies shouldn’t be forced to abandon functioning legacy systems. Look for infrastructure that layers AI capabilities onto existing environments, enabling incremental expansion without disrupting current operations or compromising system security.
Security and Trust at the Core From day one, AI infrastructure must enforce robust controls, auditability and observability to satisfy both internal oversight and external regulatory demands. These safeguards are essential for enabling secure, compliant and trustworthy AI operations across the entire model lifecycle.
Scalable by Design From pilots to full-scale rollouts, AI infrastructure should scale efficiently, without sacrificing reliability, operational control or observability.
Governance and Workforce Enablement Mature infrastructure strategies pair AI capability with internal enablement. Documentation, integrated MLOps tooling and standardized lifecycle workflows ensure teams are ready to manage and scale AI sustainably. Support from an ecosystem of trusted technology partners can further accelerate enablement and integration, helping agencies stand up Centers of Excellence, streamline operational onboarding and drive long-term capability transfer.
The Path Forward
Government AI leaders have a clear opportunity: to advance innovation without compromising operational resilience. The right infrastructure strategy doesn’t require starting from scratch; it builds on existing investments with modular, accelerated and secure components that integrate into mission workflows. When agencies align their AI deployment architecture with mission demands by embracing capabilities like retrieval-augmented generation, hybrid deployment models and full-lifecycle support, they can scale AI with control, trust and lasting impact.
The most effective AI infrastructure is more than a technical foundation; it’s a strategic enabler. When AI is embraced as part of a bigger strategy, it ensures Government agencies are not only ready for today’s AI challenges but also equipped to lead through tomorrow’s opportunities.
As agencies increase their investment in artificial intelligence (AI), the most pressing challenge is no longer just developing advanced models. It’s ensuring those models fit seamlessly into the operational workflows that underpin essential public services. These processes are deeply embedded in systems built over decades and require reliability above all else. Abrupt changes could introduce mission risk, especially in regulatory enforcement, public benefits and defense environments.
Standardized APIs offer a proven path forward. Acting as controlled, reusable interface points, APIs allow AI-powered automation in the Public Sector to augment legacy systems without destabilizing them. They expose core logic as callable services, enabling integration without overhaul. In this way, APIs bridge the gap between technical advancement and operational continuity, enabling mission-ready integration without disrupting how teams or programs operate.
Bridging Legacy and Innovation Through API Abstraction
Legacy infrastructure remains central to many Federal operations. Replacing it entirely is often impractical, but delaying AI modernization carries operational risks. Standardized APIs provide a strategic link between modern AI capabilities and existing Public Sector systems. By abstracting backend complexity, they make it possible to integrate AI into mission workflows without extensive code changes.
Abstraction layers allow AI models to access structured and unstructured data, delivering AI-driven inferences and task automation within secure, controlled environments. Because APIs provide a consistent interface, AI capabilities can evolve independently of the systems they enhance. This decoupling supports agility without sacrificing system stability, which is critical for maintaining resilience in a fast-changing technological landscape.
Accelerating Secure AI Adoption Through Operational Consistency
Government teams need to move quickly, but without compromising trust. Standardized APIs enable faster deployment by removing common bottlenecks in system integration. They streamline the delivery of secure enterprise-grade AI by enforcing consistency across environments—cloud, on-premises and edge—delivering the performance and efficiency expected from accelerated computing platforms.
These APIs also reinforce compliance with Government AI security standards. By embedding role-based access, encryption and logging at the interface level, AI solutions for the Federal Government can be monitored and governed with confidence, forming a technical foundation for responsible AI deployment.
Supporting Mission-Ready AI Through Infrastructure Portability
Modern Government AI strategies must be infrastructure-agnostic. Agencies operate in hybrid environments, and AI services need to follow. A standardized API layer model enables portability by decoupling AI tools from underlying infrastructure, allowing them to be moved or replicated across platforms without changes to the core logic or dependency on specific hardware configurations.
Portability is especially important for mission-critical operations where performance, latency and security vary by deployment context. Whether in secure data centers, cloud environments or tactical edge scenarios, standardized APIs keep infrastructure aligned with mission needs.
Lifecycle Management for Sustainable AI Operations
Agencies must manage the entire lifecycle, from versioning and deployment to monitoring and updates. APIs simplify lifecycle management by introducing structured controls around model exposure, usage and evolution.
Versioning at the endpoint level preserves backward compatibility, allowing existing applications to continue operating while new capabilities are deployed. Monitoring and audit tools track how models are used, by whom and with what data, enabling full traceability and supporting AI compliance in the Public Sector.
Collaboration and Workforce Enablement Through Shared Interfaces
API-driven design encourages reuse and collaboration. Once an AI capability is exposed via a standardized API, it can be reused across departments, avoiding redundant development and improving consistency. A federated approach supports AI data governance in Government by making it easier to enforce policies across distributed teams and can also support interagency collaboration where appropriate governance models are in place.
Workforce readiness is equally critical. By abstracting technical complexity, APIs enable Government teams to interact with AI capabilities through standardized, well-documented interfaces, lowering the barrier to adoption and empowering teams to manage their own AI workflows using the skills they already have. Rather than requiring deep ML expertise, this approach lets staff build and deploy with confidence.
A useful mental model is to think of APIs as shared utilities: once an AI capability like summarization or classification is made available via API, it can be reused, like electricity travels across the grid. APIs can be shared across programs without rebuilding the engine each time.
Evaluating API Readiness for Long-Term Government AI Success
When evaluating API readiness as part of a Government AI strategy, leaders should consider whether the API layer truly supports integration with the agency’s operational reality. This includes the ability to ingest both structured and unstructured data, interface with current tools and extend across agency-specific workflows.
Security should be integral, not layered in later. APIs must offer native support for encryption, authentication and fine-grained access control, and provide clear audit trails that satisfy compliance frameworks central to secure and responsible AI deployment in Government. Lifecycle support is equally vital: robust APIs must facilitate controlled versioning, rollback and real-time observability, including monitoring, logging and alerting, to ensure performance and trust are never compromised.
Scalability across infrastructure is another benchmark. APIs must perform consistently across cloud, edge and on-premises environments without friction. And since no agency succeeds in isolation, a mature API ecosystem should include reference implementations, shared patterns and a strong developer community to reduce implementation time and cost.
These attributes, taken together, define whether a technology stack is suitable for the mission and whether it can scale securely, responsibly and efficiently as part of a long-term digital transformation roadmap.
API-First Integration: A Catalyst for Scalable, Trusted AI
For Government agencies modernizing AI operations, standardized APIs represent more than a technical solution – they are a strategic enabler of scalable, secure and mission-aligned innovation. By offering a flexible integration layer, APIs make it possible to accelerate adoption, reduce duplication and build trustworthy AI-powered automation in the Public Sector.
Rather than forcing a complete rebuild of legacy infrastructure, APIs allow agencies to evolve at their own pace. They provide the foundation for responsible, compliant and cost-effective AI integration while keeping Government teams in full control.
Agencies that adopt this approach can shift from isolated pilots to enterprise-scale systems where AI becomes a routine, reliable part of Public Sector operations. Standardized APIs transform secure enterprise AI from a strategic aspiration into an operational reality, enabling repeatable success across mission workflows.
In the evolving era of generative artificial intelligence (AI), pre-packaged AI often falls short in the Public Sector. Off-the-shelf models typically lack the context needed to perform at the standards required by Government use cases, and building AI models from scratch remains too resource-intensive for most agencies.
However, a middle path has emerged powered by advancements in fine-tuning, accelerated computing and security-conscious infrastructure. This new approach enables agencies to adapt robust foundation models to mission-specific needs quickly, securely and without the traditional complexity of AI customization.
What’s changing isn’t just technology; it’s the framework for how Government thinks about AI readiness. By grounding strategy in full-stack development principles and AI lifecycle management, Public Sector AI leaders can begin moving from research to real-world impact at mission speed.
Accelerated Fine-Tuning, Engineered for Agility
Traditional approaches to AI model development often fail to transition from proof-of-concept to production. They can’t keep pace with mission timelines or infrastructure constraints. This is where automated, accelerated fine-tuning plays a transformative role.
By enabling targeted optimization of foundation models, teams can iterate quickly and cost-effectively. This significantly reduces compute requirements and accelerates iteration cycles, enabling rapid experimentation using sensitive data.
These capabilities allow Federal teams to develop and refine models using their existing infrastructure, removing a major roadblock to operational AI. When fine-tuning is seamlessly integrated with the hardware and orchestration stack, model updates are no longer bottlenecks. They become core to a continuous delivery process.
Security Built In, Not Added On
For Federal leaders, security is not negotiable. It’s foundational. AI platforms must be designed from the ground up to operate securely, not simply comply with policy.
Modern development stacks address this by combining containerized workloads, Zero Trust access control and built-in compliance with frameworks like FISMA and NIST 800-53. These capabilities allow agencies to maintain control of sensitive data while leveraging state-of-the-art model development tools.
Equally important is the ability to trace every stage of a model’s lifecycle. Visibility into data lineage and model provenance is essential for building public trust, ensuring transparency and simplifying audit and ATO processes.
Unifying the AI Lifecycle Under One Stack
The journey from raw data to mission-ready application spans preprocessing, evaluation, deployment and real-time monitoring. Without a unified platform to manage this lifecycle, Government teams face silos, drift and duplication of effort.
The most effective AI solutions deliver a full-stack environment where teams collaborate on the same infrastructure. This alignment ensures that experimentation is not only fast but replicable; models don’t need to be rebuilt for deployment, they’re ready to ship by design.
Operational continuity is especially important in Federal settings, where changes in leadership or mission can disrupt priorities. A unified lifecycle platform provides the flexibility to pivot quickly while maintaining compliance and consistency and can help overstretched teams scale AI impact without proportionally scaling headcount.
Mission-Tuned AI for Complex Government Domains
Generic models often struggle to perform in specialized domains. These challenges are amplified in Government, where datasets are often sparse, highly structured or privacy-restricted.
Fine-tuning large language models using domain-specific data is the most effective way to close this gap. When paired with synthetic data generation and tools like retrieval-augmented generation (RAG), agencies can create models that operate with high accuracy without increasing exposure to outside data sources.
These models can be deployed across diverse environments thanks to the flexibility of modern accelerated computing platforms, whether in the cloud, on premises or at the tactical edge. This portability, achieved through containerized AI microservices and optimized orchestration, is critical for Government teams.
From Exploration to Execution
The case for custom AI in Government is no longer theoretical. Advances in hardware-accelerated fine-tuning, lifecycle-integrated orchestration and secure, portable inference environments have made the once-difficult possible and practical.
The goal isn’t simply to deploy AI faster but to deploy AI that is trustworthy, domain-aware and cost-efficient, with solutions that enhance mission effectiveness without compromising governance.
As Public Sector leaders navigate tight budgets, workforce reductions and mounting oversight, platforms that streamline AI delivery can provide much-needed relief. Rather than requiring new teams or expensive retraining, agencies can scale with existing staff and systems.
This moment represents a shift from experimentation to operationalization. The agencies that act now—building their capabilities on a modernized, full-stack AI architecture—will not only realize early wins but will be best positioned to adapt to the accelerating pace of AI innovation in the years ahead.
As artificial intelligence (AI) advances from exploratory pilots to mission-critical systems, Government agencies face an increasingly urgent challenge: how to modernize intelligently without destabilizing the core infrastructure that supports essential services. From public benefits to regulatory enforcement, Government operations depend on reliable systems—and yet the demand for more agile, intelligent and data-driven services is accelerating.
In this environment, Application Programming Interface (API)-driven architecture offers more than a technical advantage. It provides a framework that aligns with how Government adopts innovation: carefully, incrementally and with strong requirements for security, oversight and continuity. For AI and technology leaders shaping the future of digital Government, APIs are not just useful—they are foundational.
Modernization Without Disruption
Public Sector systems are often mission critical and decades old, built long before real-time inference or machine learning were technical considerations. Replacing these systems would be cost-prohibitive, slow and risky. However, ignoring them is not an option when they contain the data and logic upon which essential functions depend.
API-first design offers a bridge. Instead of rewriting these systems, agencies can overlay intelligent services that interact with them via stable, controlled interfaces. For example, a model trained to extract structured fields from unstructured forms can be accessed as a service. The model can be invoked as needed, without being embedded in the legacy system, decoupling innovation from infrastructure.
That modularity makes progress manageable. Teams can test AI services in narrow use cases, assess results and scale adoption in stages. It also protects staff from abrupt shifts, enabling workforce transition and training to occur alongside technical deployment. For leaders evaluating enterprise readiness, this suggests prioritizing architecture that enables incremental adoption of AI capabilities without high-risk disruption.
Embedding Security and Compliance from Day One
In the Public Sector, systems must be secure and compliant by design. Requirements for data protection, access control, identity management and auditable decision-making are foundational. AI systems must align with those standards from the outset.
An API-first approach gives agencies a way to build governance directly into the AI deployment framework. Rather than relying on one-off integrations, every interaction with an AI model can be mediated through an API that enforces strict controls. Authenticating requests, encrypting data, logging transactions and rate-limiting ensure system resilience.
Just as important is the flexibility to deploy AI capabilities in controlled environments. Whether in air-gapped systems, private cloud infrastructure or hybrid networks, API-exposed services can meet the traceability and isolation requirements essential to mission-critical operations. Decision makers should seek solutions that support environment-agnostic deployment and align with relevant security and data sovereignty frameworks.
Scaling Through Reuse, Not Redundancy
A frequent challenge in agency AI programs is the repetition of effort across teams. Without a unified strategy, different groups may develop overlapping models for classification, summarization or extraction—resulting in redundant investment and inconsistent performance.
API-driven architecture supports reuse as a foundational capability. Once a model is trained, validated, and deployed as a callable service, it can be shared securely across programs.
A federated model allows each office to maintain autonomy while benefiting from shared resources and proven capabilities. This not only accelerates adoption but also improves consistency and reduces the burden on overextended technical teams. Agencies should look for platforms that facilitate model sharing, usage tracking and consumption governance to reduce redundancy and scale effectively.
Bringing Discipline to the AI Lifecycle
AI systems evolve. Models are retrained, refined and replaced to address performance gaps, policy changes or bias mitigation. Without lifecycle controls, these changes can introduce instability or compliance risk.
Deploying models through well-governed APIs introduces discipline. New versions can be released under new endpoints, allowing dependent applications to upgrade at their own pace. Logs can track which models are in use, by whom and for what purpose, enabling structured deprecation and full auditability.
Lifecycle control in AI mirrors DevSecOps practices that have already been adopted in many Government IT environments. Evaluate solutions that support endpoint versioning, access analytics and governance-ready observability to ensure stability and trust throughout the AI lifecycle.
Keeping Options Open in a Fast-Changing Landscape
The AI technology stack is rapidly evolving. New models, deployment frameworks and cost-performance tradeoffs continue to emerge. For agencies operating on long procurement cycles, flexibility is not optional. It is essential for long-term sustainability.
API abstraction allows teams to decouple applications from specific model implementations. A chatbot or summarization service can continue operating even if the underlying model is swapped or updated, supporting continuity and reducing the risk of vendor or architecture lock-in.
Flexibility supports hybrid deployment models where mission-sensitive workloads remain on-premises, and others run in trusted cloud environments. Leaders should prioritize runtime abstraction and model backend flexibility to preserve choice and adaptability as technology evolves. When possible, platforms should also expose APIs through open standards such as Representational State Transfer (REST), OpenAPI or GraphQL to ensure interoperability across systems and vendors.
Enabling Responsible, Scalable AI in Government
Responsible AI requires more than principles—it demands a technical foundation that makes oversight and accountability operational. API-first architecture provides this foundation.
Every request can be logged, every model version tracked and every output monitored for alignment with policy and mission needs. This observability not only supports compliance audits but also enables continuous performance assessment and model improvement. Built-in telemetry from API gateways can offer insights into usage trends, model health and performance, supporting both governance and optimization efforts.
Equally important, API-based integration supports human-centered adoption. Agencies can augment existing workflows, develop AI copilots and embed decision-support tools without forcing radical system changes. Government employees benefit from AI-enhanced tools, improving efficiency, insight and mission outcomes without overwhelming the workforce or introducing operational risk.
For technology and program leaders building AI strategy and capability benchmarks, this architecture offers a durable path forward, enabling secure, scalable and auditable adoption. Agencies can modernize at their own pace while maintaining full control over how AI is introduced, used and governed.
APIs do not just connect systems, they enable strategy. They create a common language between legacy operations and next-generation intelligence. For agencies tasked with delivering modern, secure and responsive public services, API-driven architecture is not just a recommendation; it is the foundation of mission-aligned innovation.
The onion model emerged during the growth of enterprise IT when organizations responded to new threats by adding new defensive layers. Each incident or compliance requirement led to another perimeter or middleware control. While effective in the short term, this layered approach produced patchwork systems with overlapping functionality, inconsistent policies and gaps that attackers could exploit.
The Onion Model and Its Vulnerabilities
The traditional “onion model” of cybersecurity layers defenses concentrically around a central database. Each layer is intended to provide a barrier against intrusion, but the cumulative effect is often an expanded and more complex attack surface. From the inside out, the layers typically include:
Database (Data) – the core asset containing customer records, financial transactions, intellectual property, logs and other sensitive information.
Schema & Validation – enforcement of data formats, constraints and integrity checks designed to prevent malformed or malicious inputs from reaching the core.
Application Logic & APIs – business rules and access methods that determine how applications interact with the database, often exposing numerous interfaces.
Access Controls & Identity (IAM) – authentication and authorization services (passwords, tokens, SSO, MFA) that regulate who can reach protected resources.
Encryption Services – cryptographic mechanisms for protecting data at rest and in transit, including key management, TLS/SSL and disk-level encryption.
Firewalls / Perimeter Security – network boundary defenses, intrusion detection systems, packet filtering and monitoring services designed to repel external threats.
Why the Attack Surface Expands
While each layer aims to protect the core, collectively they create new opportunities for exploitation:
Integration Points – every interface or protocol boundary becomes a seam that can be misconfigured or attacked.
Configuration Complexity – with more interdependent systems, administrators must manage extensive policy sets and security rules, increasing the likelihood of mistakes.
Expanded Targets – each layer (firewalls, IAM, middleware, encryption appliances) presents its own vulnerabilities, requiring constant patching and monitoring.
Dependency Chains – the failure of a single outer system can cascade inward, leaving the core exposed despite the presence of other controls.
In practice, adding more layers often enlarges the attack surface instead of shrinking it. Attackers exploit this complexity, probing for the weakest link among numerous entry points.
Operational Cost of a Typical Attack Surface
Beyond theoretical weaknesses, a large attack surface carries real operational costs. Tool sprawl burdens administrators with dozens of systems to configure and maintain.
Overlapping monitoring layers generate alert fatigue, obscuring genuine threats. Security budgets become diluted, funding maintenance of redundant defenses rather than reinforcing the integrity of the data itself.
Modern Threat Landscape
Today’s adversaries exploit weaknesses that layered defenses cannot easily address. Lateral movement bypasses layers once attackers are inside a network. Supply chain compromises enter through trusted applications, neutralizing perimeter filters. Zero-day exploits render outer walls ineffective overnight. Core-first security, with protection embedded at the data level, ensures confidentiality and integrity even in the face of these modern tactics.
Architectural Simplicity as Security
Simpler architectures are inherently more secure. Each removed integration point reduces the trusted computing base and the probability of misconfiguration. By embedding protections directly into the data layer, Walacor collapses overlapping controls, producing a system that is easier to audit, verify and trust. This simplicity is itself a security multiplier.
The Core-First Alternative
A core-first security model inverts the paradigm by embedding protections at the data layer itself rather than relying primarily on external systems:
Record-Level Encryption and Validation – each data element carries its own cryptographic safeguards, ensuring confidentiality and authenticity.
Immutable Integrity Proofs – cryptographic hashes and proofs guarantee that tampering is detectable, independent of outer defenses.
Minimized Trust Dependencies – fewer external layers are required for assurance, reducing the number of systems that must be defended and configured.
Resilience Under Breach – even if outer controls fail, the data itself remains cryptographically protected and resistant.
This approach shrinks the attack surface by concentrating security at the point of greatest value: the data. Instead of expanding outward with additional complexity, it reduces potential vectors for compromise.
Walacor and Core-First Protection
Walacor implements the core-first philosophy by embedding immutability, cryptographic enforcement and schema validation directly into the data layer. Rather than building outward layers that expand the attack surface, Walacor collapses unnecessary perimeter complexity and anchors protection where it cannot be bypassed: the data itself.
Data-Level Cryptography – each record is encrypted and bound to proofs of authenticity, eliminating reliance on external encryption appliances.
Immutable Storage – records are tamper-evident at the core, reducing the need for overlapping monitoring systems.
Integrated Validation – schema and policy checks occur at write-time, blocking invalid or hostile data without middleware add-ons.
Shrinking the Attack Surface – because Walacor renders many outer layers redundant, there are fewer interfaces to defend, fewer seams to misconfigure and fewer targets for attackers.
Walacor demonstrates that the most effective way to minimize the attack surface is to concentrate defenses in the core, ensuring data integrity and confidentiality regardless of the state of external systems.
Agents, AI and the Attack Surface
The emergence of intelligent agents and AI-driven systems adds a new dimension to the attack surface discussion. Agents interact with data across multiple contexts—querying, transforming and making autonomous decisions. In a traditional layered model, each of these interactions multiplies the integration points and potential vulnerabilities. Malicious prompts, poisoned training data or compromised connectors can all bypass outer defenses to reach sensitive information.
A core-first model directly addresses this risk. By cryptographically securing and validating data at the record level, Walacor ensures that even AI agents cannot be tricked into handling falsified or tampered records. Every data element carries its own assurance, creating a trustworthy substrate for automated reasoning and machine learning pipelines.
In this way, AI becomes a consumer of verifiable data rather than a potential vector for hidden compromise, aligning intelligent agents with the same guarantees that protect human operators.
Forward-Looking Implications
A core-first approach lays the groundwork for enduring benefits. Immutable, verifiable data strengthens sovereignty in federated and multicloud environments. Compliance becomes easier, as audit trails and integrity proofs are inherent to the system rather than bolted on. This architecture future-proofs sensitive systems, ensuring resilience against evolving threats.
Reinforcing the Core-First Premise
The onion model reflects a reactionary philosophy that often results in excessive complexity and a sprawling attack surface. A core-first strategy simplifies the architecture by embedding protection directly into the data layer, eliminating unnecessary exposure and ensuring that sensitive information remains secure even in hostile conditions.
To learn more about a core-first approach to cybersecurity, contact Walacor.
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 Walacor, 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.
In December 2025, Carahsoft and Broadcom commissioned Forrester Consulting to survey 212 U.S. Government cybersecurity decision makers about the state of Public Sector security operations following the budget and headcount reductions of early 2025. What they found was a sector under sustained pressure, but also one actively searching for smarter, more resilient ways forward. The findings provide a candid assessment of where agencies stand today and the steps required to strengthen their cybersecurity posture in an era of constrained resources.
Budget Cybersecurity Gaps
Budget instability remains widespread, with 38% of agency budgets still classified as mostly or completely fiscally unstable. Another fifth of agencies reported no change since the initial cuts were enacted. The result is a cybersecurity landscape where teams are being asked to protect increasingly complex digital environments with fewer people, fewer tools and less financial runway than they had even a year ago. Over half of the respondents report that budget constraints have moderately or significantly impacted their ability to maintain core security operations. Perhaps most telling, just 38% of cybersecurity leaders express confidence in their agency’s security posture following headcount reductions.
The areas most exposed under current resource limitations are network security, data protection and incident response. Roughly a third of respondents also flagged concerns around endpoint security, visibility, analytics and compliance. For agencies already navigating a complex regulatory and threat environment, these vulnerabilities represent more than operational friction; they signal genuine risk to mission-critical systems and the sensitive data agencies are entrusted to protect. As leadership teams work to roadmap investments for the year ahead, two priorities have risen to the top: securing critical infrastructure against bad actors and integrating artificial intelligence (AI) and cybersecurity capabilities.
Rising Breach Risk in a Leaner Environment
Understanding the current risk landscape is an essential first step toward addressing it effectively. 86% of respondents anticipate an increase in potential compromises or breaches in the coming year due to the recent staffing and funding reductions. More than a quarter expect breach numbers to climb by 1–10%, while over 20% anticipate increases of 30% or more. For agencies responsible for protecting sensitive Government data and public-facing services, this trajectory demands immediate strategic attention. The connection between resource reduction and elevated risk is already being experienced across teams, where reduced personnel have created measurable gaps in detection, response and remediation capacity.
The operational data reinforces this concern. 61% of respondents report that security incidents overall have increased in frequency, while 65% say their mean time to remediate (MTTR) has been negatively affected. Over half indicate their ability to secure technology and architecture delivery has also suffered. These are not isolated data points; they reflect a compounding effect where each unaddressed gap creates the conditions for the next. Agencies that do not act strategically in prioritizing their highest-risk exposure areas will face growing difficulty in maintaining the compliance posture and operational resilience their missions demand.
AI and Automation as Force Multipliers for Lean Teams
Amid the challenges, a clear opportunity is emerging. Agencies are increasingly recognizing that AI and automation are essential tools for maintaining security effectiveness when human capacity is stretched thin. 72% of respondents indicated openness to automation tools as a means of enhancing cybersecurity resilience. The top priority areas for automation adoption include incident response, network security, compliance and data protection, precisely the domains where resource gaps are most acute.
Forrester’s recommendations reinforce this direction. Leveraging AI to automate network traffic analysis, policy validation and alert triage allows teams to concentrate on high-confidence threats such as data exfiltration and lateral movement, rather than being consumed by manual tasks. Applied effectively, AI can help offset staffing shortfalls, reduce analyst burnout and preserve or even improve, mean time to investigate (MTTI) or MTTR metrics. Agencies that invest in AI-driven security tools now are not just responding to a short-term resource problem; they are building a more adaptive, scalable security model that can sustain performance through continued uncertainty. This is a strategic shift as much as a technical one, and cybersecurity leaders who embrace it early will be better positioned to protect their environments long-term.
Strategic Consolidation as the Path Forward
The data points toward a clear prescription: agencies must work smarter, not just harder, with the resources available to them.
On the investment side, respondents are focusing on limited resources where they will have the greatest impact: threat detection, incident response, network infrastructure modernization and process automation. Forrester recommends that agencies rationalize their security stack to eliminate overlapping capabilities, adopt consolidated platform solutions such as Endpoint Detection and Response (EDR) or unified network security platforms and reduce one-off tool purchases that contribute to sprawl and complexity. Critically, agencies should plan for sustained lean operations rather than assume a return to pre-2025 staffing or budget levels. Redesigning operating models around automation, risk prioritization and efficiency will be the defining factor for resilient agencies.
The findings from this Forrester study make one thing clear: the agencies that will emerge strongest from this period of constraint are those that treat resource limitations not as a barrier, but as a forcing function for smarter, more deliberate security strategy. By concentrating investments in high-risk areas, embracing AI and automation and consolidating their security stack, Government cybersecurity teams can build a leaner, more resilient security posture that holds up under pressure, today and in the years ahead.
Download the full study, “Smarter Security for Leaner Budgets and Teams” and join our webinar as experts and Government showcase the key findings in depth and discuss the path forward.
A commissioned study conducted by Forrester Consulting on behalf of Carahsoftand Broadcom, March 2026.
Disconnected, vendor-dependent security stacks leave smaller teams blind to threats and overwhelmed by noise they’re not equipped to manage.
Symantec CBX unifies Symantec and Carbon Black capabilities into a cloud-based XDR platform that delivers native telemetry correlation, AI-driven insights and enterprise-grade protections without enterprise-level complexity.
Built for resource-constrained teams, Symantec CBX reduces costs, cuts alert fatigue, accelerates response and gives organizations a long‑overdue advantage against increasingly sophisticated, AI-powered attacks.
See Symantec CBX in action in Booth N-5345 at RSAC 2026 Conference.
It’s time for the cybersecurity industry to face an uncomfortable truth: The tools meant to make organizations safer are often the very systems slowing them down, and sometimes leaving them vulnerable.
The problem is that security stacks are built over time from disparate tools that prevent analysts from seeing the full operating environment. Smaller security teams have relied on vendors to solve the challenge of integrating various products—and too often, vendors have fallen short, making it too difficult to gather and correlate the telemetry needed to understand what’s really happening across endpoints, networks and data.
While large enterprises have the resources to manage and integrate complex security stacks, left behind are the organizations that make up the largest swath of the cybersecurity customer market: smaller, less-resourced security teams that increasingly face AI-powered, enterprise-grade threats but lack the budgets and in-house expertise to implement enterprise-grade defenses. These sophisticated attacks can decimate smaller organizations, turning them into casualties of an escalating cyber war fueled by nefarious AI agents that never miss a day of work.
These security teams don’t just need better tools. They need an advantage. Now they have one.
XDR from the pioneer of EDR
Today, we’re introducing Symantec CBX, a groundbreaking new extended detection and response (XDR) solution that combines all the best capabilities of Symantec and Carbon Black into a unified, cloud-based platform. Symantec CBX is the first new product to integrate features from these two iconic brands. But more importantly, it’s the first fully featured XDR platform built expressly for smaller teams looking to evolve their security protections, but that lack the expertise and resources needed to configure and optimize traditional enterprise-class XDR solutions.
In Symantec CBX, we’ve distilled decades of innovation from Symantec and Carbon Black into a platform that solves the problem of correlating and making sense of telemetry across endpoints, networks and data. Typically, the various tools within security stacks attempt this via API integrations. But those fragmented couplings are often incomplete and leave dangerous gaps in visibility and actionable insight. Security analysts may understand that something is happening—they just don’t always know what it is or what to do about it.
The problem grows worse as attack surfaces expand. Organizations send more and more data to costly SIEM platforms, leading to a waterfall of challenges, from endless false positives that waste analyst time to murky outcomes that frustrate corporate management looking for evidence that security programs are working. These are costs smaller organizations can’t afford.
Symantec CBX solves this by combining into a single cloud platform Symantec’s robust prevention, data security and network security features with Carbon Black’s pioneeringEDR technology for deep visibility, exceptional threat detection and rapid response across attack surfaces. Spared from log-centric ingestion, security teams detect incidents more precisely and can act more confidently.
Native correlation is just the beginning
With Symantec CBX, native telemetry correlation sits at the center of a vast array of advanced capabilities that, until today, were available only from multiple point solutions. In CBX, we have integrated breakthrough features from Symantec and Carbon Black that make teams smarter and more efficient. Here’s what security teams can look forward to:
AI that makes life easier for humans at the helm. We’ve strategically deployed AI to deliver meaningful improvements to security workflows, resulting in capabilities that simply aren’t available anywhere else. Take Carbon Black Threat Tracer, which allows any analyst to see all adversary activity in a single pane. (Even junior analysts can understand immediately where attackers came in, how they executed their attack and what data they accessed across endpoint, network, email and cloud environments.) The CBX platform also includes Symantec Adaptive Protection, which uses AI to stop living off the land (LOTL) attacks before they do damage. And Symantec’s Incident Prediction, the groundbreaking feature we introduced last year, predicts an attacker’s next four to five moves so teams can stop threat actors moving laterally to steal data or shut down systems.
More complete insights for faster remediation. Incident Summaries, another AI-powered feature, gathers comprehensive data about incidents and presents them in well-written, intuitive summaries and remediation guidance so any analyst can engage mitigation when and where it makes sense.
Enterprise-grade network and data protections. Drawing from the best of Symantec Secure Web Gateway (SWG) and Symantec DLP solutions, this new XDR platform defends the network and data domains by stopping malicious traffic at the network edge, while packaging data security essentials from our acclaimed DLP offerings to ensure that sensitive data stays where it belongs. Via the integrated Symantec Cloud SWG
Express, this new platform even supports post-quantum computing cryptography protocols, thus shielding organizations from the threat of increasingly common “harvest now, decrypt later” attacks and relieving concerns over the prospect of attackers someday unlocking encrypted data.
Meaningful outcomes and rapid time to value. Security managers are expected to continuously improve their team’s performance, but that’s not easy when disjointed solutions create needless friction and confusion, and multiple dashboards steal time from an already busy day. We built Symantec CBX with the features and unified managementconsole that enable the outcomes security teams need most: driving down SIEM and operational costs, rescuing analysts from alert fatigue, speeding time to resolution, meeting governance requirements and demonstrating progress by improving metrics.
Out-of-the-box policy configurations make CBX easy to implement and deliver immediate value.
The Goldilocks platform for the heart of the market
Symantec CBX is aimed squarely at the heart of the cybersecurity market, empowering and enabling security teams of virtually any size with a platform that puts them first. No other XDR solution is built so specifically for organizations laboring under tight budgets, too few resources, a persistent lack of senior expertise, chronic alert fatigue and the ever-more–daunting threat of AI-powered attacks.
Symantec CBX is the XDR platform for this moment and this market. As the first new solution from Broadcom to integrate capabilities from both Symantec and Carbon Black, CBX is the realization of our strategy to deliver on the “better together” pledge we made when these two legendary brands first came together under Broadcom’s Enterprise Security Group. And it’s the ideal solution for our global network of Catalyst Partners, with their deep regional expertise and close customer relationships, as they help organizations struggling to keep up in an environment of constant change and unrelenting challenges.
Overwhelmed security teams need an advantage, and now they have one.
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.
This post originally appeared on Security.com, and is re-published with permission.
As if gaining visibility into all human and non-human identities wasn’t a big enough task for security teams, adding AI agents into the mix takes identity complexity to a new level. Organizations of all sizes are tackling this new reality, where it feels premature to confidently say they know about all the AI agents running in their environment.
That uncertainty is not a knowledge gap. It is an attack surface.
Gartner’s new report on IAM for AI agents names the real nugget of truth: “Purpose/intent cannot be discovered after the fact by monitoring and observability capabilities.”
That is not just analyst language. It is a fundamental shift in how we need to think about governing agents. You cannot govern agents by watching them after-the-fact. You must know who they are, what they are for, and who is accountable before they run.
The numbers that should change your priorities
Gartner’s data reinforces the urgency. By 2029, over 50% of successful attacks against AI agents will exploit access control weaknesses. By the year before, 90% of organizations that share credentials between humans and agents will need to make significant investments to undo that design.
Those numbers are consequences, not causes. The root cause is structural: IAM maturity for agents is uneven. The Gartner lifecycle maturity assessment makes this visible. Authentication and monitoring capabilities are relatively mature. Identity registration and authorization are not. That gap is the story.
Weak identity registration means the agent was never properly onboarded as an identity. No defined owner. No declared purpose. No documented scope. It has credentials and it is running, but nobody can tell you who built it, what it is supposed to do, or what happens when it breaks. When registration is weak, ownership is unclear. And when ownership is unclear, accountability does not exist.
Weak authorization means the agent has more access than it needs. It can reach databases, APIs, and workflows that have nothing to do with its intended function. Nobody scoped it down because nobody defined what “down” looks like. When authorization is weak, privilege is excessive.
Now combine excessive privilege with autonomy. An agent that can reason, chain tools, and act on its own, with more access than it should have, and no one clearly accountable for what it does. That is the exploitable attack surface. That is the chain revealed in Gartner’s data.
You cannot protect what you cannot see
Before you can govern agents, you need to find them. All of them. Not just the ones your platform team sanctioned. The ones that developers spun up to solve an issue. The ones contractors built. The ones that exist because someone needed to “just get this working.”
We hear this consistently from security teams. As one InfoSec manager at a professional services firm put it: “We do not find out about it until someone goes and does an actual audit of the system.”
Gartner’s assessment confirms it: identity registration is one of the least mature IAM capabilities for AI agents. Most organizations cannot answer the basics: What is this agent supposed to do? Who owns it? What happens when it breaks?
Discovery is not a checkbox. It is the foundation. Without it, every policy you write is based on assumptions, and assumptions do not survive first contact with autonomous agents operating at machine speed.
The identity registration gap
Most organizations are trying to govern agents with the wrong tools. They are monitoring. They are logging. But monitoring tells you what happened. Identity registration tells you what should happen. Authorization enforces the boundary between them.
If your governance model depends on catching problems after they occur, you are always going to be behind.
This is where many organizations reach for familiar tools. IGA platforms can help with registration and lifecycle management. IAM solutions like Okta or Entra ID can register agent identities. These are necessary steps. But they stop there. They can tell you an agent exists and who requested it. They cannot enforce anything at the moment that agent acts.
That is the gap: governance on paper versus enforcement in production.
Agents are identities, but not like any you have managed before
The way I read Gartner’s recommendations, there is a unifying thread: treat AI agents like you would treat any identity in your organization. They authenticate. They access resources. They act on behalf of someone. That is not a tool. That is an identity.
But agents are more complex than traditional identities. They are what we call composite identities. They combine the blast radius of service accounts with the unpredictability of human decision-making at machine speed.
Four reasons that make them different:
They act autonomously, unlike service accounts that execute predefined operations.
They may inherit human delegation, creating privilege escalation risk.
They may chain multiple machine identities in a single task.
They may operate across trust boundaries your IAM system was not designed to handle.
Think about how you onboard an employee. You do not give them admin access on day one. You define their role, their manager, their scope. You review their access as responsibilities change. Agents need that same lifecycle. But right now, most organizations are skipping straight to “give them credentials and hope for the best.”
What runtime enforcement actually looks like
Gartner calls out the authorization gap. But what does closing that gap look like in practice?
Even modern IAM systems, including conditional access and continuous evaluation, were designed primarily to evaluate who is signing in and what that identity is generally allowed to do. Agents introduce a different problem. They do not just sign in. They execute. They invoke tools dynamically. They operate across multiple identity contexts within a single task.
Traditional conditional access evaluates who is signing in and under what conditions. Agent governance must also evaluate what is being executed, at the moment of execution.
Here is what that looks like: an agent is about to call a tool, read from a database, trigger an API, or execute a workflow. Before that happens, there is a decision point. Runtime enforcement evaluates the composite identity: the human owner, the agent itself, the tool credentials, and the defined purpose, all at execution time. Is this agent authenticated? Does it have permission for this specific action? Is this behavior consistent with its intended function?
That is runtime enforcement. Not configuration-time policies that assume the agent will behave as designed. Decisions at execution time, every time.
What Silverfort does differently
If the failure pattern is identity immaturity, then the control point must also be identity. Most AI agent security approaches start at the model or application layer. We start at the identity layer. Because if identity is uncontrolled, everything above is fragile.
Human accountability by design
Every AI agent is explicitly tied to a real human owner in policy. Not informally. Not in documentation. In enforcement logic.
Every action can be traced back to a real chain of accountability: which human owns this agent, what identity the agent is operating under, and what credentials it uses to access resources. That is what we mean by composite identity. And it is what makes enforcement possible before monitoring even begins.
Runtime enforcement at the identity layer
Silverfort enforces at the identity decision point at runtime. For MCP-connected agents, that means sitting in line between the agent and the MCP server. For platform-native agents, enforcement is delivered through native integration, directly within the platform.
Before a tool call executes, we evaluate identity, context, delegation, and policy in real time. If the action exceeds scope, it does not execute. This is not configuration-time IAM. This is execution-time identity enforcement. That distinction matters.
Least privilege that survives autonomy
Static least privilege assumes predictable behavior. Agents break that assumption. They reason. They chain tools. They drift from what they were originally authorized to do. Least privilege must be validated at runtime, not just set at provisioning.
That means if an agent tries to access a resource outside its declared purpose, it gets blocked. If delegated privileges start expanding beyond what was originally scoped, they are contained. This is the same enforcement model we apply to humans and service accounts, now extended to AI agents.
One Identity Security Platform
AI Agent Security is not a standalone product. Agents sit at the intersection of human identities, non-human identities, service accounts, cloud resources, SaaS applications, and protocol layers like MCP. If those domains are secured separately, agents will exploit the seams.
Silverfort unifies this. One policy framework. One observability layer. One enforcement architecture. Across humans, machines, and AI. That is the architectural difference.
Enabling AI innovation without slowing it down
Security leaders are not trying to stop AI adoption. They are trying to make sure it does not outrun their ability to govern it. The organizations moving fastest with AI agents are the ones that figured out early: the right security model is a speed advantage, not a drag.
Cars have brakes so you can drive fast. The same principle applies here.
But, the brakes only work if they’re connected to the same system. Today, most organizations secure human identities in one tool, service accounts in another, and AI agents (if at all) in a third. If those domains are secured separately, agents will exploit the seams.
One policy framework means a CISO can define “no agent accesses production data without human approval” once and have it applied across every agent, every platform, every protocol. No per-tool configuration. No coverage gaps.
One observability layer means when an agent acts, you see the full chain: which human triggered it, which NHI it authenticated with, which tool it called, and what data it touched. Not three dashboards stitched together after the fact, but a single view that makes incident response possible in minutes instead of days.
One enforcement point means policy is applied at runtime, at the moment of action, not retroactively through quarterly access reviews. When an agent requests access, the decision happens inline. Allow, deny, or step up. Before the action executes, not after.
This is what shifts AI agent security from a governance exercise to an operational capability. Discovery tells you what exists. Registration tells you who owns it. Runtime enforcement tells agents what they’re actually allowed to do, in the moment, every time.
AI agents represent the next frontier of identity. Identity Security must evolve accordingly, from governance alone to continuous, runtime enforcement. Discover what is running. Register who owns it. Enforce at the moment of execution. That is the path.
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 Silverfort, 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.
This post originally appeared on Silverfort.com, and is re-published with permission.
