The Tool Desk
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What should the review cover?
Start by distinguishing how AI is involved. A tool may have code written or assisted by an AI coding system but no AI feature at runtime. Another may send information to a model, retrieve documents for prompts, or let model output call APIs. Some tools do both. Review the first like any other application; apply the additional AI checks wherever models or AI-mediated actions are part of its operation.
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NIST’s Secure Software Development Framework (SSDF), SP 800-218 version 1.1, provides a general secure-development frame. NIST’s SP 800-218A, finalized in July 2024, adds a Community Profile for generative AI and dual-use foundation models to use with the SSDF. OWASP’s application-security and LLM-application guidance can help organize risk checks. None of these frameworks is a test result or a guarantee about your tool.
NIST explains why secure development has to be made explicit: “Few software development life cycle (SDLC) models explicitly address software security in detail, so secure software development practices usually need to be added to each SDLC model to ensure that the software being developed is well-secured.” That statement appears in the 2022 SP 800-218 publication.
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How to evaluate an AI-generated internal tool
1. Define its purpose, users, environment, and data
Record what the tool is for, who owns it, who is meant to use it, where it runs, and which systems it connects to. Inventory the information it accepts, retrieves, stores, sends to third parties, displays, and writes to logs or error output. Mark which information is personal, confidential, regulated, or operationally sensitive.
Do not treat “internal” as a data-protection control. The review needs to follow data wherever it goes, including into an external model or service and into records that may be available to support staff. This inventory supports security and privacy review; it is not a jurisdiction-specific legal compliance checklist.
2. Map identities and test authorization boundaries
List human roles as well as service identities, integrations, and other credentials. For each, document which records it can read and which actions it can take. Check that the application or service enforces authorization at the point where data or an operation is accessed. A hidden button, a model instruction, or an assumption that users will behave appropriately is not an authorization boundary.
Test the meaningful boundaries for this tool. For example, try to access another user’s records, invoke an action outside the user’s assigned role, or use a service identity beyond its intended scope. Review default permissions and what happens when authorization fails. NIST’s SSDF materials emphasize protection from unauthorized access and least privilege; least privilege should apply to users, services, integrations, code, configuration, and AI resources, not just the visible user interface.
OWASP’s 2025 Top 10 places broken access control first. Its introduction reports that 3.73% of applications tested on average had one or more of the 40 CWEs in that category in the contributed application dataset. That is not a measured rate for AI-generated tools, internal tools, or your organization.
3. Trace sensitive data from entry to deletion
Choose representative data and follow it through application code, any model or external service, retrieval sources, storage, outputs, logs, and error handling. At each step, ask:
- What information is retained, where, and for how long?
- Who or what can retrieve it, and how is that access controlled?
- Is masking or minimization appropriate at this point?
- How are access and deletion requests handled?
- Could a response reveal information to someone who should not see it, or could a downstream system interpret the response unsafely?
OWASP’s LLM-application guidance identifies sensitive information disclosure and insecure output handling among the risks to consider. Check the actual data paths and consumers rather than assuming a model response is safe because it is phrased naturally.
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4. Examine untrusted inputs, integrations, and model authority
If users can submit content or the tool retrieves documents, consider whether untrusted text could steer the model or its connected tools. For each plugin, API, database, or other integration, document what it can do and which credentials it uses. Ask whether a manipulated or simply incorrect model output could trigger an operation with more authority than the requesting user should have.
OWASP’s LLM guidance identifies prompt injection, insecure plugin design, and excessive agency as risks. Use those categories to ask concrete questions about this system’s inputs and available actions; their relevance depends on whether the tool actually uses prompts, retrieval, or integrations.
5. Review code, dependencies, and operational ownership
Inspect the implementation and configuration rather than relying on a successful demo or a claim from the code-generating model. Establish how code and configuration are stored and changed, how dependencies and external components are managed, and who reviews changes before release. Confirm who owns logging, updates, incident response, and remediation.
The SSDF groups its practices around preparing the organization, protecting software, producing well-secured software, and responding to vulnerabilities. Those groups help reviewers check that security work continues after launch instead of ending with a pre-release checklist.
6. Record evidence and unresolved risk
For each concern, record the affected asset or data, expected control, evidence reviewed, observed result, owner, and any remaining risk. Useful evidence can include code and configuration review, a permission matrix, test results, a dependency inventory, and operational procedures. A tool working as demonstrated, a model asserting it followed best practices, or a completed checklist is not by itself evidence that the implementation is secure.
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What evidence should reviewers request?
| Review area | Evidence to inspect | What it helps establish |
|---|---|---|
| Permissions | Role and service-identity matrix; authorization logic; results of tests against relevant boundaries | Whether each identity is limited to intended records and actions, including when access is denied |
| Data handling | Data-flow inventory covering inputs, retrieval, storage, external services, outputs, logs, and errors; retention and deletion procedures | Where sensitive information travels, persists, and can be accessed |
| AI features and integrations | Prompt and retrieval flows; list of connected actions, APIs, and credentials; relevant tests of untrusted inputs and tool use | Whether inputs can steer model behavior and what authority model-connected components have |
| Code and dependencies | Code and configuration review; dependency inventory; change and review ownership | What is deployed and how changes or external components are controlled |
| Operations | Named owners and procedures for monitoring, incident handling, updates, and vulnerability remediation | Whether problems can be detected and addressed after release |
How to compare multiple tools or designs
Compare candidates on the same dimensions rather than collapsing them into an unvalidated overall score. NIST’s secure-development practices and OWASP’s application and LLM risk categories inform these comparison axes; they are not a published scoring standard.
| Dimension | Questions to compare |
|---|---|
| Permission granularity | Can access be constrained by role, record, operation, and service identity? Is least privilege maintained? |
| Data exposure | What sensitive data enters, leaves, persists, or appears in outputs, logs, and errors? |
| Integration and model authority | Which actions can connected services perform, and can untrusted content steer them? |
| Development and supply-chain evidence | Can reviewers inspect code, configuration, dependencies, and change ownership? |
| Operations and response | Is there a named owner for monitoring, updates, incident handling, and residual vulnerabilities? |
Which guidance editions should you use?
NIST SP 800-218 version 1.1 is the final SSDF publication identified here, and SP 800-218A is the final July 2024 Community Profile. NIST’s publications listing showed SP 800-218 Rev. 1 / SSDF 1.2 as an initial public draft published December 17, 2025; check NIST’s current publication status before treating that draft as final.
For the application-risk statistic above, the edition is OWASP Top 10:2025. OWASP’s LLM Top 10 project page describes a 2026 release as current, while the named LLM risk categories discussed here are from 2025-edition material. Confirm the live OWASP edition when selecting guidance for a review.
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