Federal agencies are accelerating the deployment of AI in an effort to modernize operations, overcome staffing shortages, and meet rising national security demands. These systems are being introduced into logistics planning, intelligence support, cyber defense workflows and the operational technology that drives core government functions. The motivation is clear: There is an explicit need for speed, scale and efficiency.
However, as agencies pursue this AI-driven transformation, they are colliding with the limits of existing operational frameworks and procurement assumptions. Leaders want tools that increase speed and reduce manual workload. The capabilities of these systems remain far narrower than the expectations placed on them.
The expectations placed on AI systems far exceed their current capabilities and, in many cases, the systems being integrated are operating without the visibility, oversight or testing that mission environments demand. Agencies are approving AI solutions through procurement pathways built for predictable software. In deployment, the risk then appears in that they no longer act like the known systems, but rather like operational actors. Agencies are integrating systems long before they understand how well these tools function in real operational conditions. The pace of innovation has outstripped the pace of governance, creating a widening gap between intention and execution.
Risks buried in old procurement models
Federal procurement is built on the expectation that technology behaves the same way every time it is used. AI does not.
Despite the release of the National Institute of Standards and Technology’s AI Risk Management Framework (AI RMF) and the October 2023 Executive Order on Safe, Secure, and Trustworthy AI, many agencies are still adopting AI solutions through acquisition models built for traditional software. These models assume deterministic, repeatable behavior. AI, by contrast, is adaptive and probabilistic, meaning that it shifts based on data quality, user behavior, environmental conditions and even subtle adversarial influence.
The gap widens when agencies rely on vendor messaging that presents AI as broad automation rather than a set of narrow capabilities. Once deployed, these systems are more than software. Microsoft is pushing multiple AI agents specifically to automate security operations; not just suggestions but workflow execution in security. AI is an operational actor, making decisions that shape outcomes across federated networks and multi-agency workflows. But even the most capable AI agents can handle less than 3% of the complex tasks typically managed by human contractors. Yet they’re often approved and integrated as if they are autonomous replacements, rather than narrow tools with defined roles.
The scale of federal operations magnifies every one of these gaps. A single AI agent introduced into one part of a workflow can influence decisions across bureaus and external partners. Without precise validation and ongoing oversight, agencies unintentionally invite uncertainty into environments where reliability and accuracy are essential.
Weak links AI creates across government systems
Complications create new forms of systemic risk for federal missions. AI introduces fresh data paths and decision points into systems that were once stable and well understood. Every one of those additions can become a new surface for adversaries to probe or exploit.
Federal procurement frameworks are not designed to capture the dynamic nature of AI behavior. When systems are approved based on static checklists and marketing language, critical assumptions about capability, context and performance go untested. Even small AI-driven mistakes can have outsized effects. A misclassification during scheduling can disrupt logistics across multiple agencies. An unexpected output in an intelligence support tool can shape downstream analysis. These ripple effects spread quickly through the interconnected networks that define federal operations.
Incident response becomes more challenging as well. AI does not fail the way traditional systems do. When an anomaly appears, teams must determine whether it stems from model training, data corruption or adversarial manipulation. That delay creates uncertainty at moments when agencies need clarity most. In missions where timing and accuracy drive national security outcomes, uncertainty becomes a real operational risk.
Building the guardrails AI needs to be useful
Federal agencies need to stop treating AI as a software upgrade and start viewing it as an operational actor inside mission systems. Once an AI agent begins making recommendations or taking actions, it becomes part of the machinery that supports readiness and national security. That requires a higher standard of scrutiny.
Operational success does not depend solely on whether an AI system “works.” It depends on whether humans can understand how it works, identify when it fails, and respond with confidence. AI that lacks transparency forces mission teams into reactive positions where they might have to waste time diagnosing behavior drift, resolving anomalous outputs, or questioning whether adversarial interference has occurred.
This is why the executive order on AI mandates increased testing, red-teaming and monitoring. But implementation remains inconsistent. Too many early deployments lack structured feedback loops or defined escalation paths for AI anomalies. In missions where timing and accuracy are essential, uncertainty becomes the risk.
Closing the AI procurement gap starts with acquisition rules, especially when procurement is the starting line for mission risk. Validation must challenge the system with heavy load, imperfect data and the types of probing adversaries regularly use. Early deployments should remain inside controlled mission sandboxes so teams can see how the AI behaves under pressure and where guardrails are needed. Security teams also need continuous visibility once the system goes live. AI changes as it interacts with people and data, which means behavior can drift over time. Behavioral analytics and automated detection help agencies spot those shifts before they influence downstream systems or create operational confusion.
Without this transparency, the burden falls entirely on the agency, which slows adoption and increases operational risk. AI must be treated like critical infrastructure, not a black box with a glossy datasheet.
Payoff of doing AI the right way
AI adoption will continue to accelerate; there’s no question about that. But the difference between enabling mission success and amplifying mission risk lies in how these systems are procured, tested and governed. Federal agencies that build efficient safeguards into their approach will set themselves up for steadier, more dependable AI adoption. Strong behavioral visibility gives analysts the context they need to understand how an AI system is influencing operations and where early signs of drift may appear. That context helps teams respond faster and with more precision.
When AI behavior is understood, visible and predictable, it becomes a force multiplier. Analysts gain context. Teams move faster. Agencies respond with confidence. Instead of introducing risk into trusted systems, AI becomes a stabilizing force that reinforces operational resilience at scale.
Federal AI adoption will not succeed through ambition alone. It will succeed when agencies, vendors and oversight bodies commit to shared responsibility, continuous validation and the rigorous governance mission systems deserve.
Kevin Kirkwood is chief information security officer at Exabeam.
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