The Tool Desk
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How can governance keep AI adoption moving?
Give each proposed use a clear purpose and a business owner who is accountable for its value, alongside a technical owner responsible for how it is built or configured and operated. Include security and privacy stakeholders early enough to shape the use case, rather than treating their review as a final approval gate.
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As a practical starting point, maintain an inventory of proposed and active AI uses. This is an implementation recommendation, not a prescribed NIST process. For each use, record:
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- Who operates the model, infrastructure, integrations, and supporting data.
- What information enters the system, how sensitive it is, and where it may be stored or processed.
- What the system can do: provide suggestions, influence a decision, or take actions through tools or connected services.
- Who can assess the system and its dependencies, monitor it in operation, and respond to problems.
Use those details to set review depth and safeguards according to the likely impact and the organization’s ability to manage the risk. A bounded pilot with limited data and permissions is a more manageable starting point than an open-ended deployment, but it is not automatically safe; its actual risks still depend on the system and use.
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NIST describes its AI Risk Management Framework (AI RMF) as voluntary and intended to help organizations incorporate trustworthiness considerations across AI design, development, use, and evaluation. It is a resource for adapting risk practices, not a certification or a guarantee of security. NIST says the framework is being revised as part of the White House AI Action Plan, so consult the current framework page for status. NIST released its Generative AI Profile, NIST-AI-600-1, on July 26, 2024.
Which safeguards fit the AI system you plan to use?
Do not treat every AI deployment as the same kind of exposure. Use the operating model and capabilities below as a decision aid: the more sensitive the data or consequential the actions, the more important it is to understand and control the relevant data flows, access, dependencies, and operations.
| System or workflow | Questions to resolve | Security focus |
|---|---|---|
| Externally developed or operated service | Who operates the model and infrastructure? What data is sent to the service, and what does the service connect to? | Assess the provider and deployment context; protect data and credentials; understand service and integration dependencies; plan how to detect and respond to issues affecting the service or its data. |
| Organization-developed or fine-tuned system | Who controls development, training or tuning data, model components, and deployment? | Build security into design and development; assess data and component provenance; protect the development and deployment environment; maintain controls during operation. |
| LLM assistant | Does it only produce suggestions, or can it access sensitive information, tools, or connected services? | Limit data and permissions to the task; consider prompt injection and unauthorized actions; monitor interactions and relevant integrations. |
| Predictive AI | What decisions or workflows rely on its predictions, and what happens if its performance changes? | Assess the data and dependencies, evaluate behavior for the intended use, and monitor for changes such as drift. |
| Single-agent or multi-agent system | Which actions can agents take, what tools can they invoke, and how do actions move between agents? | Constrain action permissions and tool access; account for the combined workflow and its handoffs; detect and respond to unexpected or malicious activity. |
| AI developer workflow | Which models, datasets, code, and infrastructure enter the development process? | Protect the development supply chain and track the origin and integrity of data and components. |
These categories can overlap: for example, an externally operated LLM assistant may also have agent capabilities. Assess the system as deployed rather than relying on a product label. NIST’s Control Overlays for Securing AI Systems (COSAiS) project reflects these distinct use cases, including LLM assistants, predictive AI, single- and multi-agent systems, and AI developers. NIST’s project page records an annotated discussion draft in January 2026; it should not be described as a finalized, complete set of overlays.
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Data security is a lifecycle and supply-chain concern, not only a question of whether an input is sensitive. Consider the data used to train, fine-tune, test, and operate the system, as well as the components and services on which it relies.
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- Know where data came from. Track provenance so teams can evaluate whether data is trusted and appropriate for its intended use.
- Protect trusted versions. Use digital signatures for trusted revisions where applicable, so teams can verify that data or other materials have not been silently changed.
- Review the supply chain. Identify relevant infrastructure, datasets, models, components, and services, and assess how their compromise or failure could affect the AI workflow.
- Watch for malicious modification. Training or operational data that has been deliberately altered can undermine system behavior; establish ways to identify and investigate suspicious changes.
- Account for drift. Data and system behavior can change over time. Monitor for relevant changes and reassess whether the system remains suitable for its intended use.
- Limit exposure. Decide which data the system actually needs, who or what can access it, and how integrations handle it. Pay particular attention when sensitive information could be exposed or inferred.
NSA’s May 22, 2025 guidance on AI data security addresses data used to train and operate AI systems, including trusted infrastructure, provenance, digital signatures for trusted revisions, supply chains, maliciously modified data, and drift.
What does secure deployment and operation require?
For externally developed systems, deployment choices and ongoing operations matter as much as the model itself. Joint guidance announced by NSA in April 2024 focuses on securing externally developed AI systems and notes broader applicability to managed environments, particularly high-threat or high-value ones. The guidance emphasizes protecting confidentiality, integrity, and availability, mitigating known vulnerabilities, and protecting, detecting, and responding to malicious activity against AI systems and related data and services.
Translate those aims into the controls that fit the deployment:
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- Protect: Control access to the system, its data, and connected services; keep relevant components maintained; and address known vulnerabilities.
- Detect: Monitor system and service activity for suspicious behavior, including misuse of integrations or unexpected actions, and define who reviews alerts.
- Respond: Decide how to contain a problem, preserve needed information, assess affected data and services, and restore operations. Integrate AI-related scenarios into existing incident-response arrangements.
AI introduces attack paths alongside familiar cyber threats. A joint NSA, CISA, NCSC-UK, and partner announcement in November 2023 identifies prompt injection and training-data poisoning as examples of adversarial machine-learning attacks. Such attacks may impair model performance, trigger unauthorized actions, or extract sensitive information. Their relevance depends on the system’s design and use; they are reasons to assess exposure, not proof that every AI system is vulnerable in the same way.
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NSA’s November 2023 secure AI development guidance organizes its recommendations around secure design, development, deployment, and operation. It explicitly says the guidance does not replace general cybersecurity, risk management, or incident response. Treat AI-specific safeguards as additions to those established practices, not as a separate program that displaces them.
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The following staged approach is a practical synthesis of the guidance, not a universal sequence established by the sources:
- Select a bounded use case. State the intended task, expected value, users, and limits. Start with the smallest practical scope that can answer whether the use is useful.
- Assign owners and map the system. Identify business and technical owners, the operating model, data flows, integrations, and any actions the system can take.
- Assess risk and set controls. Consider data sensitivity, dependencies, likely impact, provider or internal operating responsibilities, and the organization’s ability to monitor and respond. Apply safeguards proportionate to that assessment.
- Deploy with operational readiness. Confirm that access, vulnerability handling, monitoring, and response arrangements are in place for the system and its related data and services.
- Review evidence from use. Evaluate whether the system meets its purpose, whether its behavior or data has changed, and whether incidents or near misses indicate a need to adjust the use or controls.
- Expand deliberately. Broaden access, data, or permissions only after owners have reviewed the results and determined that the organization can manage the changed exposure.
This approach makes risk review part of adoption decisions: controls can be tightened, the scope changed, or a use stopped when the organization cannot manage the exposure. NIST’s Cybersecurity, Privacy, and AI program addresses both how broad AI adoption affects cybersecurity and privacy risk management and how security concerns affect AI adoption. NIST notes that AI may augment cybersecurity capabilities, while AI can also enable attacks and create risks such as privacy re-identification and expanded tracking. The program page was updated July 15, 2026.
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