When it comes to implementing AI, federal IT professionals find themselves caught in the horns of a dilemma. You may be feeling it in your own agency customers right now. Leadership wants to move quickly to adopt AI solutions, while still being able to prove that these tools are completely safe to put into place.

It can seem difficult to strike a balance between those two needs. After all, modern AI isn't just about handing employees a simple chatbot to clean up their grammar. We’re talking about granting access to autonomous agents that can query databases, call APIs, and make operational decisions on their own.

That’s the problem in a nutshell: We have to keep up with the speed of innovation, but we cannot afford to expose critical federal networks to unnecessary security risks.

To navigate this shift as smoothly as possible, we need to rethink risk management. Moving fast and staying secure do not have to be opposing forces. If we line up our procurement strategies, identity protocols, and overall governance models, we can extricate ourselves from the dilemma and hit both targets at the same time.

That’s why AI security is becoming the enabling layer for federal adoption. It gives agencies a practical way to move faster while preserving visibility, access control, and confidence that mission systems are not taking on unnecessary risk.

Unpacking OMB Memo M-25-22

The Office of Management and Budget memo M-25-22, “Advancing the Responsible Acquisition of Artificial Intelligence in Government,” sets down direct requirements for federal procurement. It gives us clear guidance for how agencies should evaluate commercial AI tools before letting them touch government data.

The memo requires agencies to prioritize U.S.-developed AI solutions and verify tool origins through strict certifications. On the contract side, there are requirements to enforce continuous performance tracking, active risk management, and tight data-use restrictions. That last point is important, because it legally prohibits technology vendors from using non-public government data to train their public foundation models.

Implicit in these restrictions is the “principle of least privilege” for AI training and operations. Least privilege means restricting AI models, training pipelines, and agents to the bare minimum data, computations, and permissions they need to do their specific jobs. AI systems cannot have unrestricted access to corporate networks or user databases. Hard, immutable boundaries must be set up at every single stage of the AI lifecycle. 

OMB’s guidance also makes AI security an acquisition priority. Before AI tools are introduced into operational environments, agencies must be able to show that government data is protected, least privilege is enforced, and vendor accountability requirements are built into the buying process.

AI agents are not the same as software tools

To be successful in following these requirements, federal IT teams have to stop thinking of AI agents as passive chatbots or routine desktop applications. AI needs to be governed as secure digital identities, with specifically tailored permissions. In other words, autonomous AI systems need the exact same security, accountability, and restricted access controls that human employees follow.

Treating an autonomous agent like a simple piece of passive software runs the risk of creating a blind spot for your security operations center (SOC). When an AI tool starts running commands or pulling files, your team needs to know exactly who or what is executing that work. Lumping agent activity under broad software permissions makes it almost impossible to spot anomalous behavior or conduct proper incident response.

That’s why the principle of least privilege is so important here. In practical terms, it could include assigning every single AI agent a unique non-human identity (NHI) or dedicated service account. Think of it as issuing a PIV or CAC card and a username directly to the AI agent.

When you tie actions to a unique identity, every single step the AI takes can be traced straight back to that specific agent. It stops autonomous tools from hiding behind general system permissions.

For autonomous AI, identity-based control is not just a technical safeguard; it is a practical requirement for oversight. Clear ownership, defined permissions, and continuous monitoring help reduce blind spots and give security teams the auditability they need for incident response and compliance review.

Putting the NIST Framework to work

To put this into practice, NIST’s AI Risk Management Framework (AI RMF 1.0/SP 1270) provides a blueprint for authorizing AI systems. While this framework is voluntary and still evolving, it sets out clear ways to minimize the risk of applying AI to federal processes.

There are four parts to the NIST framework:

• Govern: Set the right culture, ensure legal compliance, and clearly define your organization's tolerance for risk.
• Map: Draw clear boundaries around your systems, understand your data context, and map out domain-specific risks.
• Measure: Run both quantitative and qualitative tests to evaluate system accuracy, spot bias, find security vulnerabilities, and test overall robustness.
• Manage: Keep up with continuous risk response, track system performance in real-time, and handle incident remediation quickly post-deployment.

Pairing the NIST Framework with solid non-human identity management gives us a clear, practical roadmap forward. We can give our agencies the cutting-edge tools they need while keeping our networks completely locked down.

None of this should come as a surprise. The federal government has said for years that security in general should not be bolted onto a product or service at the end. Al should not be treated any differently. Secure AI should be an evidence-driven lifecycle discipline, connecting governance, data, models, infrastructure, runtime behavior, people, and recovery.

The NIST Framework helps turn that need into an operating model. By connecting governance, risk measurement, monitoring, and response, agencies can move from policy intent to execution through a repeatable lifecycle rather than treating AI security as a one-time approval step.

Simplifying the lifecycle process

To make this lifecycle process easier, agency decision-makers and the partner ecosystem must work together. By doing so, you can be sure that the technology, the integration expertise, and the contract vehicles exist to make lifecycle discipline work smoothly from the pilot phase to mission fulfillment.

Security is the mechanism that makes federal Al deployable, not the reason it stalls. Protect the data Al touches — at rest, in motion, and inside every prompt, log, retrieval, and output. The result is better AI security and peace of mind about the applications that use it.

We’re essentially talking about a path to authorization for AI security. It is the evidence federal reviewers and decision makers need to safely and securely implement AI. But authorization doesn’t certify a model as trustworthy or fit for a given use. It’s a process that continues across the lifecycle, with measured performance and continuous monitoring, and rollback of previous decisions as required.

Getting to authorization in AI security is a team effort of agencies and vendors, each of which owns a distinct layer of the process. Most importantly, it also should be delivered by a partner who has done this type of integration work before.

Ultimately, the path to secure AI adoption must be practical, integrated, and repeatable. Federal end users will need technology, implementation expertise, procurement alignment, and operational support working together so AI can move from pilot programs to mission use with confidence.

Turn AI opportunity into action. Request a secure AI readiness review to uncover where federal funding is flowing, understand the policies driving decisions and strengthen your AI security positioning.