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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAn effective enterprise AI security strategy starts by mapping where AI is used, what information and systems it can reach, and what could happen if it fails or is misused. Then it assigns accountable owners, applies controls proportionate to each use case, and reviews those decisions as systems change. Treat AI security as an ongoing risk-management program—not a checklist that guarantees safety.
Start with an inventory of AI use and impact
Before selecting controls, create a working inventory of AI systems in use and under consideration. Include approved tools, internally built applications, embedded AI features in other services, and employee use that may not yet have formal approval. This inventory is a practical way to organize risk work; no particular inventory format is mandated by the NIST AI Risk Management Framework.
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- Ownership: Record a business owner, technical owner, and security or risk contact for each use.
- Purpose and workflow: Describe what the system does, who relies on it, and whether its output informs or triggers a consequential decision.
- Data and access: Identify information sent to the model, retrieved for it, stored in logs, or returned in outputs, as well as the systems and permissions it can reach.
- Architecture and dependencies: Note whether the system uses a third-party service, an internally hosted model, retrieval or vector components, extensions, external tools, or custom code.
- Failure impact: Consider the effect of incorrect output, unauthorized disclosure, harmful actions, or service unavailability, along with how easily an action can be reversed.
Use these records to prioritize review. A system that drafts public-facing copy has a different risk profile from one that can retrieve confidential records or change them in a business application.
Use NIST AI RMF to organize the program
NIST AI RMF 1.0, released January 26, 2023, is a voluntary framework organized around four functions: Govern, Map, Measure, and Manage. NIST says the framework is being revised; check its framework page for the latest status before treating any version as current. The functions provide an organizing structure, not a certification or a guarantee that a system is safe.
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| Function | Enterprise question | Practical implementation |
|---|---|---|
| Govern | Who is accountable, and what rules apply? | Set ownership, decision rights, risk tolerances, approval paths, and policies for development, acquisition, use, and incident escalation. |
| Map | What is the system, its context, and its likely harm? | Document intended use, users, data, dependencies, access, affected parties, misuse cases, and consequences of failure. |
| Measure | How will the organization assess whether controls and outcomes are acceptable? | Test security and behavior against defined scenarios; monitor performance, access, incidents, and changes relevant to the use case. |
| Manage | What will the organization do with the findings? | Prioritize mitigations, assign actions and deadlines, accept or reduce residual risk through an accountable process, and respond when conditions change. |
The NIST AI RMF Playbook offers suggested actions, references, and documentation practices aligned with these functions. Adapt its guidance to the organization’s requirements and resources rather than treating every suggestion as a universal requirement.
Set controls according to capability and consequence
Do not assign controls from the label “AI” alone. Base review depth on what the system can do, what it can access, how exposed it is to untrusted input, and the consequences of failure. The following is a practical decision aid, not a standardized scoring scheme.
| System capability | Risk questions | Controls to consider |
|---|---|---|
| Answer-only or drafting assistant | Could prompts or responses expose sensitive information? Will people mistake plausible output for verified fact? | Limit data entered, define permitted uses, protect logs, provide review guidance, and check outputs before external or consequential use. |
| System that retrieves internal information | Can a user retrieve records beyond their authorization? Can untrusted content manipulate answers? | Enforce access checks at retrieval time, restrict indexed data, separate data by permission boundary, and test retrieval and prompt-injection scenarios. |
| System that writes to business applications | Can an incorrect or manipulated output modify important records or trigger a workflow? | Use narrow permissions, validate proposed changes, require human approval for higher-impact actions, and retain an auditable record. |
| System that executes code or takes external actions | Could a model response cause irreversible, costly, or unauthorized action? | Constrain execution environments and allowed actions, gate sensitive operations, set limits, and monitor activity for anomalous or out-of-scope behavior. |
Across these cases, raise scrutiny when data is more sensitive, inputs are less trusted, dependencies have uncertain provenance, actions are harder to reverse, or downtime would disrupt a critical workflow. A risk decision should record both the controls in place and the remaining exposure the accountable owner accepts.
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AI security builds on established cybersecurity and privacy practices. NIST’s security and resilience overview identifies confidentiality, integrity, and availability concerns for AI systems and for training and output data, as well as the security of underlying software and hardware. Apply those familiar objectives across the complete system, including data pipelines, models, integrations, and operational infrastructure.
For generative AI, NIST’s cross-sector AI 600-1 Generative AI Profile, published July 26, 2024, supplements the AI RMF with suggested actions for generative AI risks across lifecycle stages. NIST describes a profile as an application of the framework for a particular setting, application, or technology, shaped by user requirements, risk tolerance, and resources.
OWASP’s 2025 Top 10 for Large Language Model Applications identifies these risk categories. Treat them as prompts for threat modeling, not proof that every application has the same exposure. The linked page may indicate a newer edition, so confirm the canonical version before using this 2025 list as a current baseline.
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- Prompt injection: Test whether instructions in user input or retrieved content can override intended behavior or influence access and actions.
- Sensitive information disclosure: Restrict what data enters prompts, retrieval stores, outputs, and logs; check that authorization boundaries also apply to retrieved content.
- Supply-chain vulnerabilities: Track model, dataset, library, service, and other component origins, versions, and changes.
- Data and model poisoning: Protect data collection and training or tuning processes against unauthorized or harmful changes.
- Improper output handling: Treat model output as untrusted input when passing it to software, queries, commands, or downstream workflows.
- Excessive agency: Limit the permissions and actions available to systems that use tools or extensions.
- System-prompt leakage: Avoid relying on hidden instructions as a security boundary or as a way to protect secrets.
- Vector and embedding weaknesses: Assess retrieval stores, their access controls, and the integrity and separation of indexed content.
- Misinformation: Decide where factual verification or human review is necessary, especially when outputs affect important decisions.
- Unbounded consumption: Set practical limits and monitor resource use to reduce exposure to runaway cost or service exhaustion.
For a system that can invoke tools, extensions, or other services, ask what it may read or change, what input can influence its decisions, and how the organization can stop or reverse an action. OWASP describes excessive agency as the possibility of damaging actions following unexpected, ambiguous, or manipulated model outputs. Least-privilege access, narrowly allowed actions, approval gates, and monitoring are risk-based design choices that can reduce that exposure; they are not guarantees against it.
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Security review should cover the model and the surrounding application, not just the model provider. For internally developed systems, include the data, code, interfaces, infrastructure, and operational changes that shape behavior. For acquired systems, assess the supplier and the specific service configuration the organization will use.
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NIST SP 800-218A, published July 26, 2024, augments Secure Software Development Framework (SSDF) 1.1 with practices and tasks specific to AI model development over the software development lifecycle. NIST says it is useful to model producers, producers of AI systems that use models, and acquirers of those systems. Use it to inform development and acquisition reviews, while tailoring requirements to the system and the parties involved.
- For development teams: define security requirements early; document data and component provenance; review changes to models, dependencies, and permissions; test relevant abuse cases; and control release and rollback.
- For procurement and supplier review: clarify which model and service components are involved, how the service handles organizational data, what security information and change notifications the supplier provides, and how the organization can respond to an incident or discontinue use.
- For acceptance testing: test the configured system with realistic inputs and access boundaries, including failure and misuse scenarios tied to its intended workflow. Record findings, remediation owners, and unresolved risks before expanding use.
Operate the strategy as a recurring risk loop
Assign responsibility for maintaining the inventory, approving risk decisions, testing controls, and coordinating incident response. The precise roles and review cadence should fit the organization; the following are implementation recommendations, not universal NIST requirements.
- Approve a defined use: document its purpose, owner, data, capabilities, dependencies, and intended users before broad deployment.
- Test before and after material changes: revisit the relevant scenarios when the model, data, permissions, integrations, or use case changes.
- Monitor operations: track access, failures, unusual activity, resource use, and reports of harmful or incorrect behavior in ways appropriate to the system and privacy obligations.
- Prepare incident handling: establish who can disable access or actions, preserve relevant records, investigate exposure or misuse, and communicate with affected stakeholders.
- Reassess residual risk: update the decision when controls fail, new dependencies appear, business impact changes, or monitoring reveals a different exposure than expected.
Legal and regulatory duties depend on jurisdiction, sector, system role, and use case. Organizations should have qualified legal and compliance advisers assess the requirements that apply to their deployments rather than infer a universal compliance answer from a voluntary framework.
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