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Evaluate an open-source AI assistant as a complete system in the environment where your organization plans to use it—not as a model label or a feature checklist. Define the work it will do and the consequences of failure, set testable security and governance requirements, examine evidence across the system’s lifecycle, and document the conditions under which you would accept or reject it.
Start with the use case, not the product label
“Open source” by itself does not establish that an assistant is secure, suitable for enterprise use, or safe for a particular workflow. The assistant’s behavior depends on the full deployment: model, application, data flows, connected tools, permissions, configuration, and operating practices. Evaluate the implementation you intend to run, using evidence and tests tied to your organization’s requirements.
Before comparing candidates, write down the context in which the assistant would operate:
- Tasks: What should it do, and what must remain outside its scope?
- Users: Who will use it, and who will administer or review it?
- Data: What information may users submit or retrieve, and how sensitive is it?
- Access and integrations: What systems, files, tools, or actions could it reach?
- Consequences: What could happen if it produces a wrong answer, exposes information, or is manipulated?
- Acceptance conditions: What evidence and test results would be enough to approve a limited pilot or production use?
Use those answers to set priorities. An assistant that drafts internal summaries has a different risk profile from one that can retrieve confidential records or take actions in business systems. The NIST AI Risk Management Framework (AI RMF) is a voluntary framework for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems. NIST released it on January 26, 2023; its current page says a revision is in progress.
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Separate governance from security verification
A useful evaluation needs both an organizational risk process and technical security checks. They answer different questions: governance clarifies who owns decisions and how risks are handled; verification checks whether the system meets specific security requirements.
The following resources can structure that work, but they have different scopes:
| Resource | Useful for | Boundary to keep in mind |
|---|---|---|
| NIST AI RMF | Organizing consideration of AI trustworthiness across design, development, use, and evaluation. | It is voluntary; it does not certify an assistant or prescribe a universal product score. |
| OWASP Artificial Intelligence Security Verification Standard (AISVS) | Vendor-neutral, testable security requirements covering the AI lifecycle. | OWASP says AISVS is not a governance framework, risk-management methodology, or product recommendation list. |
| OWASP Large Language Model Security Verification Standard (LLMSVS) v2.0 | Requirements and tests for LLM-backed applications, including architecture, model lifecycle, operation and integration, storage, and monitoring. | OWASP recommends open-book assessment and says the standard should complement, not replace, other security practices. It does not authorize a claim of official OWASP certification. |
| OWASP AI Maturity Assessment | Structuring discussion of organizational readiness across strategy, design, implementation, operations, and governance. | It can help assess the organization’s practices; it does not select a product for you. |
| NIST SP 800-218A | Reviewing secure development practices for generative AI and dual-use foundation models. | It explicitly excludes AI system deployment and operation, and most data governance and management lifecycle activities. |
Use standards as ways to organize questions and evidence, not as proof that a particular candidate is safe or appropriate. The NIST AI Resource Center is another source of AI risk-management resources.
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Build a security test plan for the actual deployment
Test the assistant as users and connected systems will encounter it. Include its interfaces and integrations as well as model and agent behavior; a model-only review can miss risks introduced by the surrounding application or its permissions.
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OWASP describes AISVS as an open, community-driven, vendor-neutral catalogue of testable security requirements. Its 1.0 release in June 2026 reports 191 requirements across 12 chapters and three appendices, with verification levels 1, 2, and 3. Use the requirements to select checks proportionate to your use case and risk, and record which ones you tested and what evidence supports the result.
LLMSVS v2.0, published by OWASP in 2026, provides requirements and tests for LLM-backed applications. Its open-book approach is useful in practice: request access to relevant documentation, source code, authenticated interfaces, and people who can explain how the system works. If a claim cannot be verified from available evidence, record it as unresolved rather than treating it as a control.
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Test threats that match your workflows
OWASP’s GenAI Security Project identifies concerns including prompt injection and data leakage. Treat these as categories for threat modeling and testing, not as proof that a specific assistant does or does not mitigate them.
- Try representative user inputs and adversarial or misleading content relevant to the assistant’s role.
- Check whether instructions embedded in retrieved or user-supplied content can cause behavior outside the intended task.
- Test whether users can retrieve or expose information beyond their authorized access through prompts, integrations, or shared context.
- Verify that connected tools and actions are limited to the permissions and tasks your requirements allow.
- Record the test conditions, expected behavior, observed behavior, and any mitigation or follow-up needed.
These are evaluation prompts, not a universal pass/fail suite. Define expected outcomes from your own threat model and validate them in the intended configuration.
Review development evidence and operational controls separately
Ask the vendor or project maintainers for evidence relevant to development and release practices, vulnerability handling, component and model documentation, and procedures for the proposed deployment. NIST SP 800-218A offers secure-development practices for generative AI and dual-use foundation models, including AI-related requirements. Its stated scope does not cover deployment and operation, so a development review alone cannot establish whether your live environment is adequately controlled.
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For the deployment itself, assess each of these areas against your organization’s requirements:
- Data handling: Where inputs, outputs, retrieved content, and logs go; who can access them; and what retention rules apply.
- Identity and permissions: How user and service access is granted, limited, and reviewed, especially for connected tools.
- Logging and monitoring: What activity is visible to operators and how issues are detected and investigated.
- Updates and changes: How model, application, and configuration changes are assessed before and after release.
- Incident response: Who responds to a suspected exposure, misuse, or service compromise, and what actions are available.
The cited frameworks support lifecycle evaluation, but they do not prescribe one architecture or configuration for every organization. Translate your requirements into tests for the specific implementation you plan to operate.
Compare candidates on the same evidence
Apply the same evaluation axes and test conditions to every candidate. This makes differences visible without pretending that a single score can represent every organization’s risk tolerance.
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|---|---|---|
| Task performance | Results on representative workflows and inputs, with the evaluation conditions recorded. | Does it meet the quality and reliability requirements for the intended tasks? |
| Security verification | Test plan, results, supporting documentation, and unresolved findings. | Are the applicable requirements tested, and are remaining risks acceptable or mitigated? |
| Governance and ownership | Named owners, review responsibilities, escalation routes, and approval records. | Can the organization oversee the system through its intended use? |
| Data and integrations | Data-flow information, permissions, retention behavior, and integration tests. | Does the implementation handle information and connected actions within policy? |
| Operational maintainability | Update, monitoring, logging, vulnerability, and incident procedures. | Can the team maintain and respond to issues in the intended environment? |
| License obligations | Applicable licenses for the assistant’s components and the planned use. | Can the organization comply with the obligations that apply to its deployment? |
Do not treat missing documentation as evidence that a control exists. Capture unresolved questions, proposed mitigations, accountable owners, and acceptance conditions in the decision record. The frameworks offer structure, not a universal scoring formula or a recommendation for a particular assistant.
Make approval conditional and documented
Decide whether to reject a candidate, continue evaluation, approve a constrained pilot, or approve the proposed use only after the relevant tests and reviews meet your requirements. Record the intended use and configuration, evidence reviewed, test outcomes, unresolved risks, mitigations, owners, and any conditions on approval. Reassess when the use, integrations, permissions, or system implementation materially changes.
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