AI application security reports need to describe more than a model response. The relevant system may include prompts, agents, retrieval, tools, authorization, data stores, model providers, filters, logging, and human review. A finding should make clear which layer failed, under what conditions, what an attacker or user could achieve, and which requirement the observation affects.
The problem this guide solves
Raw probe output can be noisy, repetitive, and easy to overstate. A model may produce an undesirable response without creating an exploitable application weakness, while a secure-looking model response may hide a vulnerable tool or authorization flow. When teams paste prompts and outputs directly into a report, they can expose secrets or personal data and leave readers without the system context needed to judge impact or reproducibility.
Understand the standard and the boundary
OWASP AISVS is an open catalogue of testable security requirements for AI-enabled systems across their lifecycle. A project should identify the AISVS version and level, AI system type, components, trust boundaries, model or service dependencies, authorized test methods, and exclusions. Tools such as garak can generate useful candidates, but the reviewer must decide whether the application behavior represents a valid, reproducible security finding.
Read the official OWASP AISVS documentation
Who this workflow helps
- AI red teams and application security reviewers.
- Engineering teams building agents, RAG systems, and model APIs.
- Risk and governance teams coordinating technical remediation.
- Students and researchers learning structured AI security verification.
A professional workflow
A dependable assessment does not begin with a report button. It begins with a clear question, defined scope, the correct standard, suitable test methods, and a record that another authorized reviewer can follow. The sequence below is designed to preserve that chain. Adapt its depth to the engagement, but do not remove the review decisions merely to make the process appear faster.
- Map the AI application architecture, data flows, tools, identities, trust boundaries, and external providers.
- Select the AISVS version, assurance level, and applicable requirements.
- Define safe test accounts, data, rate limits, stop conditions, and evidence handling.
- Run manual tests and approved probes, then group related outputs by root cause.
- Record the affected component, risk type, attack vector, impact, evidence, and precise AISVS mapping.
- Assign remediation across model, prompt, integration, authorization, data, or monitoring layers.
- Retest the complete affected flow and generate a reviewed assessment report.
What to record
Record enough information to support reproduction, assignment, remediation, validation, and reporting. Each field should have one clear purpose. Keep identifiers and quoted evidence exact, distinguish observations from recommendations, and avoid collecting secrets or personal information that the work does not require. A smaller complete record is more useful than a large collection of disconnected text and files.
- AI system type, architecture area, model or service context, and environment.
- AISVS requirement, chapter, and verification level.
- Finding summary, description, severity, status, validation, and owner.
- Sanitized prompt and response evidence only when necessary.
- Attack vector, reproducibility conditions, affected component, and security impact.
- Remediation, due date, and retest notes across every affected system layer.
How voiqq supports the work
voiqq uses one project and finding foundation across Programs while each Library controls its own requirements, fields, metrics, mapping, automation boundary, and report rules. That means teams can reuse assignments, comments, evidence, validation, history, permissions, imports, exports, and recovery without pretending that every standard reaches the same kind of conclusion.
voiqq AI security projects use canonical AISVS fields and the same controlled finding lifecycle as other Programs. Supported garak results can become Pending review candidates with source metadata and deterministic duplicate handling. Reviewers can preserve additional tool data without forcing it into standard fields. The AISVS PDF report calculates coverage from canonical requirement mappings, active gaps, and assessed evidence, with warnings for records that remain unmapped.
Quality checks before sharing
- Separate model behavior from application authorization, tool, data, and integration failures.
- Sanitize prompts and outputs before storing or sharing them.
- Confirm that the claimed impact is achievable in the assessed system.
- Record provider and model changes that may affect reproducibility.
- Do not present probe success as a validated vulnerability without human review.
Before distribution, ask a second question beyond whether the file generated: can the intended reader understand the scope, trace important statements to project evidence, distinguish active and resolved work, and see the limits of the conclusion? Review permissions and attachments as carefully as report wording. Preserve an approved snapshot when the deliverable must remain stable after the live project changes.
A practical next step
Start with one end-to-end AI feature, document its trust boundaries, and test a small set of applicable AISVS requirements. Use that project to define safe evidence, severity, duplicate, and retest conventions before increasing probe volume or assessment scope.
Treat the first result as a review draft. Check it with the people who perform the work and the people who receive the outcome. Their questions will reveal missing context, confusing terminology, weak permissions, and report assumptions sooner than another decorative dashboard will. Improve the project model, then repeat the same disciplined workflow.
Explore AI application security in voiqq
voiqq uses the stable OWASP AISVS 1.0 catalogue: 191 requirements in 12 chapters with verification levels 1, 2, and 3. AISVS requirements are assessment requirements, not prewritten findings; several findings can be connected to one requirement when the evidence warrants it.