A design review for an AI application benefits from the same structure used for conventional software design review, examining architecture, data flow, and trust boundaries, extended with specific questions that target where AI applications actually fail in practice. Run a conventional design review without these additions and you tend to approve architectures that look sound on a diagram while missing the specific model-behavior and tool-authority risks running throughout this content series.
Review the content trust map as a first-class design artifact
Ask the design to produce an explicit map of every content source the model will process and its trust level, similar to the instruction hierarchy covered in prompt security guidance. A design review should treat this map as a first-class artifact worth reviewing directly, the same way a conventional review examines a data flow diagram, rather than inferring trust boundaries implicitly from a general architecture description that never specifically calls them out.
Question every tool for its actual authority, not its intended use
For every tool the design proposes handing the model, ask what it actually allows technically, not just what the feature intends to use it for. A tool that can technically perform a broader operation than the feature's intended use case, built broader for convenience or future flexibility, carries the broader risk regardless of the narrower intent, and design review should push back on this gap specifically, requiring either narrowing the tool or explicitly justifying and additionally safeguarding the broader capability.
Ask what happens if the model is fully compromised at each design decision point
Apply the assume-compromise exercise directly during design review: at each point where the model's decision leads to a real action, ask what the worst outcome would be if that decision were fully attacker-controlled. Asked explicitly and repeatedly through the design, rather than once at a high level, this question surfaces specific points where additional authorization or limits are needed that a more general review would likely miss entirely.
Review data minimization as a specific design decision, not an assumption
Ask the design to justify what data enters the model's context for each distinct task, instead of accepting a design that passes broad objects or full records for convenience. This question should be specific enough that the answer names actual fields or document scopes, not a general assurance that "only necessary data" is included, since that general assurance tells a reviewer nothing about whether the actual implementation will match it.
Review failure behavior explicitly, not as an afterthought
Ask the design to specify what happens when each security-relevant dependency, an authorization service, an approval workflow, goes down, and confirm the specified behavior defaults toward denying or holding sensitive actions rather than proceeding. This question frequently goes unaddressed in an initial design pass, and design review is the right point to demand an explicit answer before implementation starts building around an implicit, potentially unsafe default.
Review logging and traceability design alongside functional design
Ask the design to specify what will get logged for security and traceability purposes, following the metadata-focused logging guidance covered elsewhere, and confirm this gets designed alongside the feature's functional logging rather than tacked on as an afterthought once the feature's otherwise complete. Logging designed retroactively tends to capture whatever happened to be convenient to add, not what an investigation would actually need.
Involve someone with AI-specific security experience directly
A design review run entirely by reviewers experienced only in conventional application security, with nobody specifically familiar with AI-specific failure modes, tends to approve designs that pass conventional scrutiny while missing model-behavior-specific risks. Make sure at least one reviewer with genuine AI security experience participates directly in reviews for any feature above a low risk classification, rather than trusting general security reviewers to independently apply AI-specific scrutiny they may never have had training for.
Document review findings and track them to resolution
Record specific findings from the design review, the same way conventional design review findings get tracked, and confirm each one actually gets addressed before implementation proceeds, rather than noted during review and then lost as the project moves forward under its own momentum.
Where TELEON fits
Where a design review identifies a need for independent tool authorization, approval workflows, or fail-safe behavior, TELEON's policy enforcement, applied through the gateway or supported middleware, provides an implementation path for those specific findings, and its audit trail supports confirming the design's intended logging is actually realized in production.
Conducting the review itself, asking the specific AI-relevant questions about content trust, tool authority, and failure behavior, remains work that has to be done by reviewers with the right expertise engaging directly with the specific design.
A short checklist
- Review an explicit content trust map as a first-class design artifact.
- Question every tool's actual technical authority, not just its intended
use.
- Apply the assume-compromise exercise at each design decision point
involving model-driven action.
- Require data minimization to be justified with specific fields or
scope, not general assurance.
- Require explicit, deny-by-default failure behavior for every security
dependency.
- Design logging and traceability alongside functional design, not as an
afterthought.
- Involve a reviewer with genuine AI-specific security experience.
- Track findings to resolution before implementation proceeds.
Secure design review for an AI application works best as an extension of existing design review discipline, with specific, pointed questions about content trust, tool authority, and failure behavior added to catch the risks that a conventional review, applied without these additions, consistently misses.