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A secure enterprise AI strategy combines accountable governance, a clear inventory of AI uses, risk-based controls, secure development and procurement, and ongoing monitoring. Start with the business process and information each system touches; then scale safeguards to what it can do and the consequences of failure. No framework or checklist guarantees safety, and legal requirements vary by jurisdiction, sector, and use case.
Start with an inventory of AI uses and consequences
Before choosing controls, establish what AI the organization uses or plans to use. Include approved systems, pilots, embedded AI features in existing software, and employee use of external services where it can be identified. The inventory is a practical management recommendation, not a format mandated by NIST.
For each use, record enough information to make a risk decision and assign responsibility:
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- Purpose and owner: the business process, accountable business owner, technical operator, and teams responsible for security, privacy, and risk review.
- Capability: whether the system only drafts or answers, retrieves information, writes to business systems, executes code, or takes actions outside the organization.
- Information: what users submit, what the system retrieves, what is used for training or adaptation, where inputs and outputs are stored, and who can access them.
- Dependencies: model provider, hosting arrangement, connected tools, data sources, third-party components, and any human review in the workflow.
- Failure impact: potential harm from an incorrect answer, unauthorized disclosure, manipulated output, unavailable service, or unintended action—and whether the action can be reversed.
These details help distinguish a low-impact drafting assistant from an AI feature that can expose sensitive records or change a customer account. They also give reviewers a baseline for reassessing risk when the model, data, access, or business purpose changes.
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Organize the program around NIST AI RMF
NIST AI RMF 1.0 provides a voluntary structure for managing AI risks through four functions: Govern, Map, Measure, and Manage. NIST says the framework is being revised; check its current framework page for status. The framework is an organizing aid, not a certification or a guarantee that a system is safe.
| Function | Enterprise question | Practical implementation |
|---|---|---|
| Govern | Who is accountable, and what rules apply? | Assign decision rights, establish approval and exception paths, define acceptable-use and data-handling policies, and make ownership of each use explicit. |
| Map | What is the system used for, in what context, and who could be affected? | Document the use case, users, information flows, external dependencies, operating environment, foreseeable misuse, and consequences of errors or service disruption. |
| Measure | How will the organization evaluate risk and control effectiveness? | Assess relevant security and reliability risks before release; test representative inputs and misuse cases; monitor performance and incidents after deployment. |
| Manage | What will the organization do about the risks it finds? | Prioritize risks, select safeguards or restrict the use, document residual risk and approvals, and define response and recovery actions. |
NIST’s AI RMF Playbook offers suggested actions, references, and documentation practices aligned to these functions. Use it to shape local procedures, rather than treating every suggested action as a universal requirement.
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Scale safeguards to capability and business impact
A useful enterprise decision model considers both what the system can do and what a failure could cause. This is a risk-based recommendation, not a scoring method prescribed by NIST or OWASP. Give closer review to systems that handle sensitive data, accept untrusted inputs, rely on uncertain provenance, take hard-to-reverse actions, or support critical workflows.
| System pattern | Questions to resolve | Risk-based control emphasis |
|---|---|---|
| Answer-only assistant | Can it reveal restricted information or present incorrect answers as reliable? | Limit accessible data, explain appropriate use, test for disclosure and misleading outputs, and provide a way to report problems. |
| Retrieval-enabled assistant | Which repositories can it search? Are permissions enforced for each user? Could retrieved content contain malicious instructions? | Apply existing access controls at retrieval time, restrict data sources, review ingestion and updates, and test adversarial documents and queries. |
| System that writes to business applications | Can it modify records, send messages, or trigger transactions? Can changes be undone? | Constrain operations and data scope, validate proposed changes, require human approval where impact warrants it, and log actions for review. |
| Agent that uses tools, executes code, or acts externally | What tools are exposed? What is the maximum damage from a mistaken or manipulated instruction? | Grant only necessary permissions, restrict available actions, gate consequential operations, isolate execution where appropriate, and monitor tool calls. |
Controls should reflect the use case rather than the label “AI.” A system may move into a higher-risk category if a new integration gives it access to sensitive records or the authority to change an operational workflow.
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Address generative AI threats in the actual architecture
NIST’s cross-sector Generative AI Profile (NIST AI 600-1), published July 26, 2024, supplements the AI RMF with suggested actions for generative AI risks across lifecycle stages. OWASP’s LLM application risk list identifies categories to consider, including the following. The categories are a threat checklist, not evidence that every system is exposed in the same way; the page indicated that a newer edition may exist, so check the canonical page for the edition currently in force before using the list as a formal reference.
| Risk category in OWASP’s 2025 list | Questions and controls to consider |
|---|---|
| Prompt injection | Can untrusted user input or retrieved content influence instructions? Separate trusted instructions from untrusted content where possible, constrain what the model can access, and test direct and indirect manipulation attempts. |
| Sensitive information disclosure | Could prompts, retrieved records, outputs, logs, or training data expose confidential information? Minimize data sent to the model, enforce authorization at the data source, and define retention and logging rules. |
| Supply-chain vulnerabilities | Can the organization establish the source and integrity of models, datasets, libraries, and hosted services? Record provenance, review suppliers and dependencies, and plan for changes or compromise. |
| Data and model poisoning | Who can contribute or alter training, fine-tuning, retrieval, or evaluation data? Restrict write access, review data sources and changes, and test whether unexpected changes affect behavior. |
| Improper output handling | Is model output passed into a browser, database, shell, or another system? Treat output as untrusted input: validate, encode, and apply the receiving system’s normal security checks. |
| Excessive agency | Can unexpected, ambiguous, or manipulated output trigger a consequential tool action? OWASP describes this risk for systems able to invoke tools or extensions. Limit access and allowed actions, add approval gates according to impact, and monitor activity. See OWASP’s LLM06:2025 explanation. |
| System-prompt leakage | Would disclosure of internal instructions reveal sensitive information or undermine controls? Do not rely on prompt secrecy to protect credentials, authorization rules, or confidential data. |
| Vector and embedding weaknesses | Could retrieval return the wrong tenant’s information, stale or poisoned content, or data the user is not authorized to see? Test retrieval boundaries and maintain source-level access controls. |
| Misinformation | What happens if a plausible but incorrect response reaches a customer or operational decision? Set review requirements based on consequence, and provide a route to verify or escalate uncertain results. |
| Unbounded consumption | Could repeated, oversized, or costly requests exhaust capacity or budget? Set practical usage limits, monitor consumption, and plan how to degrade or suspend the service if needed. |
NIST frames AI security as part of security and resilience, not a replacement for established cybersecurity. Its overview identifies confidentiality, integrity, and availability concerns for AI systems and for training and output data, as well as security of underlying software and hardware. Apply relevant security and privacy practices to the full system, including its data flows, infrastructure, integrations, and operational access—not just the model interface. See NIST’s security and resilience overview.
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Build security into development and acquisition
Enterprise risk depends on more than the model provider. Review the complete system: model, application code, datasets, retrieval components, tools, hosting, and operational processes. For internally developed systems, include security review throughout design, development, testing, release, and maintenance. For acquired systems, establish what the supplier is responsible for and what the enterprise must configure or monitor.
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- Ask about provenance: identify relevant model, data, and component sources; understand how updates are introduced; and record dependencies that matter to security decisions.
- Set security expectations in procurement: specify the intended use, data handling, access boundaries, integration responsibilities, incident communication, and change notification needed for the deployment.
- Review the system, not only the contract: validate configuration, access controls, data flows, and behavior in the organization’s environment before connecting sensitive systems.
- Test before release and after material change: include misuse and adversarial cases relevant to the architecture, plus ordinary software and infrastructure security checks.
- Maintain a response path: know how to restrict access, disable an integration, preserve necessary evidence, and restore service or data if a component or model change creates unacceptable risk.
Run AI security as a continuous operating loop
Assign a named owner for each production use and a cross-functional group to resolve risks that span business, security, privacy, legal, procurement, and engineering. The exact roles and approval mechanisms are organizational choices; make them proportional to the system’s impact.
- Review before use: assess a new system or material change to its model, data, connected tools, permissions, users, or purpose.
- Record the decision: document identified risks, selected controls, the person accepting any remaining risk, and any use restrictions.
- Monitor operation: watch for access anomalies, unexpected outputs or actions, service degradation, and changes in cost or usage that matter to the deployment.
- Handle incidents: route reports to accountable responders, contain the affected capability, investigate data and system impact, and decide whether users or other stakeholders need to be informed.
- Reassess: revisit the risk decision when the operating context, threat picture, model or component provenance, or consequences of failure change.
Keep the inventory, tests, approvals, incidents, and changes connected, so teams can see why a control exists and whether it still fits. Regulatory obligations depend on geography, sector, system role, and use case; organizations should determine applicable requirements with qualified legal and compliance advice.
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