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Give people and automated processes only the access they need for their assigned work, to the data they need, for an approved purpose and period. Start by mapping the project’s data, users, services and data flows; then define permissions, protect identities, constrain transfers, monitor use and review access as the project changes.
Start with the project, its data and its risks
Before changing settings in a cloud console, model platform or repository, establish what the AI project does and what its access controls need to protect. Record the intended use and lifecycle stage—such as development, evaluation or production—and identify the datasets and other components involved. For each data source, note whether it contains personal, confidential, regulated or third-party information, who may be affected, and any relevant legal, contractual, privacy or security restrictions.
Map how data enters the project, where it is stored or processed, which people and automated services can reach it, and where it can be sent next. Include hosted models, retrieval systems, plugins, exports and other connected services. This map helps reveal access paths that a list of employee accounts alone would miss.
NIST’s voluntary AI Risk Management Framework (AI RMF) organizes risk work into Govern, Map, Measure and Manage and is intended to apply across AI system design, development, use and evaluation. NIST’s AI RMF page says the framework is being revised; its Playbook says it will be updated after that revision. The framework and Playbook are guidance, not universal compliance checklists.
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Define who or what may do which tasks
Build the access model around actual duties and need-to-know. Define the datasets and operations each role requires, such as viewing, editing, exporting or administering. Include service accounts, model-serving processes, scheduled jobs and other non-human identities, and assign each a specific purpose and owner. Avoid shared accounts when individual accountability matters.
A role-and-data matrix can make gaps or excess permissions easier to spot. The entries below are illustrative; choose categories and permissions that fit your project rather than treating them as a standard template.
| Identity or role | Potential access | Boundary to define |
|---|---|---|
| Data steward | Approve dataset use and manage access decisions | Whether approval authority includes direct access to raw records |
| AI developer | Use approved training or test data and project tools | Which datasets, environments and operations are needed for assigned work |
| Production operator | Operate deployed services and investigate incidents | Whether routine duties require access to underlying personal records |
| Administrator | Configure identities, permissions or infrastructure | Which elevated functions are allowed and how privileged activity is recorded |
| Automated service | Read or write specific data as part of a defined process | Its owner, purpose, permitted systems, credentials and removal process |
For sensitive data, document the approved purpose, who authorized access, what access type is allowed, how long it is needed and any environment restrictions. Separate development, evaluation and production permissions where the architecture allows. NIST SP 800-171 Revision 3, requirement 03.01.05, states: “Allow only authorized system access for users (or processes acting on behalf of users) that is necessary to accomplish assigned organizational tasks.” That standard’s formal requirements concern protecting controlled unclassified information (CUI) in nonfederal systems and organizations; the quoted control is not a blanket legal requirement for every AI project.
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Keep identity, permissions and data movement distinct
Three different questions need answers. Authentication checks who or what is signing in. Authorization decides which data and actions that identity may access. Information-flow controls govern where data may move, including exports and transfers between systems or security domains. A strong sign-in does not, by itself, restrict what an authenticated account can read or send.
Limit administrative accounts and privileged functions to the roles that need them, use ordinary accounts for routine work, and log privileged actions. Choose authentication assurance in proportion to the impact of unauthorized access, while accounting for privacy, usability and user context. NIST SP 800-63-4 provides digital identity guidance, including phishing-resistant options at higher assurance levels and hardware cryptographic authenticators. A FIDO2 security key can strengthen sign-in; it does not determine which records the signed-in identity may query.
Separately define rules for exporting data, connecting external systems and moving information between environments. Apply restrictions appropriate to the data’s sensitivity and your policy. For personal data, document collection, use, management and disclosure under the organization’s privacy and data-governance practices. Do not treat de-identification alone as proof that every use or release is safe. For production systems, consider monitoring queries for patterns that could isolate personal records.
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Assess external AI services before sharing data
Before connecting a third-party model or service, establish what information it receives, where that information goes, who can access it, what its terms and technical controls permit, and how incidents or changes are handled. Apply the same scrutiny to plugins, retrieval providers and other connected systems—not just the model itself. Verify provider-specific practices against current contracts and documentation; services can differ in their data handling.
NIST’s Generative AI Profile (AI 600-1, July 2024) identifies potential privacy and information-security risks involving generative AI and describes due diligence, service-level agreements and assurance reports as possible risk-management inputs. Use those materials to inform a provider assessment, not as substitutes for checking the provider’s actual commitments and the project’s applicable obligations.
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Set a documented access-review frequency based on the project’s risk and applicable obligations; there is no universal interval established by the guidance described here. Review sooner when a person changes roles, a project moves to a new stage, a dataset is added or a provider changes. Confirm that permissions still match current work, correct excess access and remove permissions that are no longer needed. NIST SP 800-171 leaves the frequency of access reviews organization-defined within its scope.
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Test whether controls work as intended, and monitor for unexpected access or data movement. Retain the inventory, risk decisions, role definitions, approvals, review records, relevant logs, provider assessments and exceptions. Record why an exception is necessary and who accepted its residual risk. Revisit the control design when data, models, intended uses, staff or providers change; NIST’s AI RMF treats governance as cross-cutting and risk management as iterative across the lifecycle.
Choose guidance that fits your obligations
Identify the project’s jurisdiction, data types, organization and contractual commitments before concluding which rules apply. NIST SP 800-171 Revision 3 is specifically scoped to CUI in nonfederal systems and organizations, while SP 800-63-4 addresses digital identity. Neither standard alone determines every organization’s legal or contractual obligations. The AI RMF and its Playbook are voluntary resources. Obtain appropriate legal, privacy and security review for the project’s circumstances rather than treating this general guide as compliance advice.
AI security guidance is still developing. NIST’s Security and Resilience overview describes risks to AI data confidentiality, integrity and availability, notes unresolved coverage for some machine-learning attacks, and identifies active work on AI security control overlays. Access controls are one part of a broader risk program, not a complete defense against every AI-specific threat.
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