AI is quickly becoming an integral part of the assessment lifecycle. From accelerating test content creation and improving test security to enhancing the test taker experience, AI is creating opportunities. But as the interest in AI-driven test development grows, one question keeps coming up in our conversations with testing programs: “If we use AI, is our content truly secure?”
It’s a fair concern. The core materials that shape your exam content – your blueprints, reference materials, item-writing guidelines, SME reviews, and ultimately, the items themselves – represent years of expertise and investment. With the right system design, AI-driven test development can be both innovative and secure.
We are often asked about three key areas when it comes to secure AI test development for assessment programs:
- Inputs: the approved materials used within an AI environment.
- Process: SME reviews, edits, and interaction data.
- Outputs: the final items with full source traceability.
Inputs: Protecting your materials in secure AI test development
In AI-driven test development, ‘inputs’ refer to the content used by AI agents to generate and review items, including:
• Test blueprints.
• Reference materials.
• Item-writing guidelines.
• Job task analyses.
• Scoring rubrics.
• Example items or item shells.
These assets form the backbone of an assessment program. So naturally, programs want to know whether using AI puts them at risk of exposing sensitive material to other organizations. With a platform designed for high-stakes testing, it doesn’t.
Secure architectures use a combination of:
• Tenant-level isolation (your organization operates in a private, encrypted environment).
• Cryptographic separation of data.
• Role-based access controls (RBAC) to limit access.
• Tenant-specific storage of logs, prompts, and interaction data.
• No sharing of training data across clients.
In PSI’s AI test development environment, for example, your materials stay inside the secure tenant environment. They are not visible to other organizations, and they are not used to train or alter the underlying AI models. This is a key distinction between general-purpose AI tools and platforms purpose-built for assessment content management.
Read how to navigate AI in test development.
Working with your reference publishers
Even with robust system protections in place, testing programs must still ensure their use of reference materials aligns with existing licensing agreements.
This is why we encourage our clients to speak to their publishers early. If you use third-party references, discussions should cover your current agreement and any restrictions. As well as whether notification or explicit permission is required for AI use.
These conversations are becoming increasingly common across the industry. Being proactive helps avoid uncertainty and gives publishers confidence that their materials will be used securely and appropriately.
Process: how AI and SMEs work together securely
Security isn’t just about protecting inputs. The development workflow itself must be secure, especially when SMEs are reviewing, editing, and approving content.
Secure workflows are essential, whether you use AI or not, and a defensible AI workflow should protect and track:
• SME interactions and feedback.
• Item iterations and version histories.
• Reviewer comments.
• Quality-control metadata.
• Scoring or analytic outputs.
In PSI’s system, all of this stays inside the client’s secure environment. Logs, reviews, and metadata are encrypted and isolated. SMEs work in a controlled editorial environment where:
• Every edit is tracked.
• All item history is preserved.
• Audit trails are automatically maintained.
• All interactions stay inside the tenant environment.
You always have proof of who did what, when, and why. A complete record that is crucial for accreditation, legal defensibility, and internal governance.
Discover how to maximize your SME’s time and impact in test development.
Outputs: security, ownership, isolation, traceability
With AI-generated items, programs typically want clarity on three things: Who owns them? Where are they stored? Can they leak across tenants?
In a well-designed system:
• You own everything produced in your environment.
• Your items and metadata are stored in your secure tenant only.
• Outputs are never visible or reusable outside your environment.
Each item is tied directly to its underlying source material, edit history, reviewer decisions, and approval status. This clear lineage is what makes AI-assisted items genuinely defensible.
Secure AI test development in practice
Security matters, but so does effectiveness. In a recent large-scale licensure project, PSI’s secure AI-driven workflow delivered:
• 77.4% of AI-generated items meeting all psychometric thresholds.
• 65.6% of AI-generated items approved for pretesting by SMEs.
• A 10% increase in SME acceptance rates between batches of AI-generated items.
All of this was achieved without exposing inputs, sharing data across tenants, or compromising IP. It shows that programs don’t need to choose between innovation and protection. Both are achievable with the right system design.
Questions to ask any AI test development vendor
Before adopting an AI workflow, consider asking:
• How are inputs isolated and encrypted?
• Are client materials ever used to train or tune the model?
• Who owns the items generated by the system?
• How is item lineage tracked?
• Can other organisations see or benefit from our materials or outputs?
• How do you protect SME comments and interaction data?
If a vendor can’t answer these clearly, the system may not be designed for high-stakes assessment.
Innovation only works when trust is built in
AI brings real opportunity to test development. Faster cycles, larger item banks, more structured SME involvement, and strong psychometric consistency. But none of these benefits matter if the system isn’t secure.
The future of AI in assessment depends on solutions built from the ground up with security, transparency, and expert oversight at the core.