Evaluate an AI-generated internal tool as software that must pass ordinary application security and privacy review. Inspect its actual code and configuration, map who and what can access data or perform actions, test those boundaries, trace sensitive information through the system, and assign owners for ongoing monitoring and fixes. If the tool uses a model, retrieval, or plugins, add checks for prompt injection, disclosure, unsafe outputs, and excessive authority. A framework can organize this review; it cannot certify that a particular tool is safe.
What should an evaluation cover?
“AI-generated internal tool” can mean code drafted with an AI coding assistant, an application that calls a model, or both. The review should follow the implementation, not the label: examine the code, configuration, identities, data flows, and connected services actually used by the tool.
NIST’s Secure Software Development Framework (SSDF), SP 800-218, provides a general secure-development frame. Its companion SP 800-218A, finalized in July 2024, adds practices for generative AI and dual-use foundation models. OWASP’s application-security and large-language-model guidance can help organize risks. These are aids for structuring a review, not certifications, legal determinations, or proof that an application is safe.
NIST’s final SSDF version 1.1 is SP 800-218. NIST’s publications listing also showed SP 800-218 Rev. 1 / SSDF 1.2 as an initial public draft published December 17, 2025; that status alone does not establish that a later version is final. OWASP’s project page describes a 2026 LLM Top 10 as its current release, while the detailed risk descriptions available for this review included 2025 material. Confirm the live edition and labels when using the list; the specific risks discussed below are the ones identified in the available OWASP guidance.
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How should you scope the tool and its data?
Start with a short system description that lets a reviewer understand what is being approved and where it runs. Do not treat “internal” as a data-protection control: employees, service accounts, integrations, and logs can all expose information beyond its intended audience.
- Purpose and accountability: Record the intended use, business owner, technical owner, deployment environment, and expected users.
- Connected systems: List models or external services, databases, retrieval sources, APIs, plugins, and other integrations.
- Information handled: Inventory what the tool accepts, retrieves, stores, sends to third parties, returns to users, and records in logs or errors.
- Sensitivity: Mark data that is personal, confidential, regulated, or operationally sensitive, and identify applicable organizational requirements.
This inventory helps reviewers see the boundaries that need testing. It is not, by itself, a jurisdiction-specific privacy-law checklist.
How do you verify permissions and authorization?
Build a permission map for both people and non-human identities. For each role or service identity, record the records it can read, the operations it can perform, and the systems it can reach. Check where authorization is enforced: a hidden button or an instruction to the model is not an adequate substitute for an application or service check at the point of access.
Rank #2
Test the boundaries that matter to this tool rather than relying on a successful demonstration. At minimum, try to access another user’s records, invoke an action the test user was not assigned, and use a service identity beyond its intended scope. Include default access and failure behavior: when identity information is missing or a request is denied, the tool should not silently grant broader access.
Least privilege applies to users, services, integrations, code, configuration, and any AI resources the system handles. NIST’s SSDF and its AI profile emphasize protection against unauthorized access and least privilege. OWASP’s 2025 Top 10 ranks broken access control first. Its introduction reports that an average of 3.73% of applications in its contributed dataset had one or more of the 40 CWEs in that category. That is not a measured rate for AI-generated or internal tools, nor a prediction for a particular organization.
How do you trace sensitive information?
Follow representative sensitive data end to end, including paths that are easy to miss during a feature demo. For each data item, establish where it originates, where it passes, who can retrieve it, and what happens when processing fails.
- Input: Identify data entered by users or supplied by connected systems.
- Processing: Follow it through application code, prompts, models or external services, and retrieval sources.
- Persistence: Identify databases, caches, files, backups, and retention periods that apply.
- Output: Check what users receive and whether downstream systems can expose, store, or act on the result.
- Operational traces: Inspect logs, analytics, error messages, and support access for unnecessary sensitive content.
Ask how access, masking where appropriate, retention, and deletion are managed at each relevant point. Also check generated responses for unintended disclosure and for unsafe interpretation by systems that consume them. OWASP’s LLM guidance identifies sensitive information disclosure and insecure output handling as risks to consider.
What additional checks apply when the tool uses AI?
If users can submit content, or the model can retrieve documents, treat that content as potentially untrusted. Determine whether it can steer the model into revealing information or using connected tools in ways the user was not authorized to request. A model instruction is not an authorization boundary.
For every plugin, API, database, or other integration, document its available actions and the credentials used. Consider whether manipulated input or an incorrect model response could trigger an operation with more authority than the initiating user should have. Review how outputs are validated before they are displayed, stored, or passed to another system.
Rank #4
OWASP’s LLM application guidance identifies prompt injection, insecure plugin design, and excessive agency, alongside disclosure and unsafe output handling. Apply the checks that match the tool’s actual model, retrieval, and integration features; a tool without those features does not need an invented AI-specific threat model.
What should you examine in the code and its operation?
A working demo shows that a particular path functions; it does not establish that implementation and configuration are secure. Request material that lets reviewers inspect how the software was built and how it will be maintained.
- Code and configuration: Review the implementation, deployment settings, identity configuration, and changes made since the version proposed for release.
- Dependencies and external components: Request an inventory and understand how components are selected, updated, and reviewed.
- Change controls: Identify who can change code or configuration and who reviews changes before release.
- Operations: Name owners for monitoring, updates, incident handling, and vulnerability remediation; establish how issues are reported and addressed.
NIST’s SSDF groups its practices around preparing the organization, protecting software, producing well-secured software, and responding to vulnerabilities. Use all four areas to plan maintenance as well as pre-launch review; a one-time approval does not address later changes or newly discovered flaws.
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What evidence should reviewers record?
Keep a review record that connects each concern to the system and the evidence used to assess it. The record should make residual risk and accountability visible rather than collapsing the outcome into a vague “secure” label.
| Record | What to capture |
|---|---|
| Asset or data | The affected record type, service, integration, model resource, or operation. |
| Expected control | The access restriction, data-handling behavior, or operational practice the tool needs. |
| Evidence inspected | Relevant code and configuration, permission matrix, test results, dependency inventory, or operating procedure. |
| Observed result | What the review or test showed, including the conditions under which it was checked. |
| Ownership and residual risk | The person or team responsible and any remaining exposure requiring a decision or follow-up. |
Do not treat a generated model’s assurance that it followed best practices, a completed checklist, or a successful demo as a substitute for inspecting implementation and exercising meaningful access and data boundaries. This evidence-recording approach is a practical way to support the secure-development and vulnerability-response goals in NIST guidance; it does not itself certify the tool.
How should you compare multiple tools or designs?
Use the same dimensions for each option so that reviewers compare real differences rather than impressions. These axes synthesize NIST secure-development practices and OWASP application and LLM risk categories; they are not a published scoring standard.
| Comparison axis | Questions to ask |
|---|---|
| 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? |
A single overall score is meaningful only if the organization has defined and validated how the score is calculated. Otherwise, compare the evidence and unresolved risks directly.
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Use the record to decide whether the tool’s controls match its purpose, users, data sensitivity, and connected-system authority. Where an important boundary has not been inspected or tested, record that as an unresolved risk rather than treating absence of evidence as proof of safety. Assign an owner and a response plan for any risk the organization accepts.
NIST and OWASP guidance can structure this decision, but neither establishes that an individual application is safe or compliant. No reliable prevalence statistic specific to vulnerabilities in AI-generated internal tools is established here, and a broader application dataset should not be used as a substitute for evaluating the tool in front of you.
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