Selected Engineering Work
x402Shield
Verified selected engineering work for Rust-based AI-agent, MCP-tool, and API-payment security.
View caseAI System & Model Security Review
From S$10,700~US$8,360Reference date: 9 Oct 2026 · Local price is authoritative; this is not a payment or settlement rate.
Buyer outcome
A scoped security review of an AI system, model-connected workflow, chatbot, or internal assistant, focused on data exposure, prompt injection, unsafe outputs, permission boundaries, and written remediation priorities before wider rollout.
Why security-led AI integration matters
AI workflows often connect to documents, customer channels, internal tools, APIs and business systems. Each connection can introduce new risks: data exposure, prompt injection, unsafe outputs, excessive permissions, tool misuse and unclear accountability.
This review focuses on prompt injection, data exposure, insecure output handling, model or tool abuse, retrieval risks and permission boundary failures.
BilgeQor's AI services are designed around written scope, approved data sources, permission boundaries, human approval points and review before wider rollout.
Scope drivers
Number of AI workflows or assistants reviewed
Internal-only vs customer-facing exposure
Model, tool, and integration architecture
Knowledge source and retrieval design
Permission boundaries and action capability
Sensitive data exposure risk
Prompt injection and abuse scenario depth
Required remediation and retest depth
Ideal for
- Singapore-based teams preparing to launch or expand an AI system
- SaaS, fintech, professional services, education, support, or operations teams using LLM-powered workflows
- Businesses with customer-facing chatbots, internal assistants, or RAG-style knowledge systems
- Teams that need a written risk view before connecting AI to sensitive workflows
- Leaders who want practical remediation priorities without overstating compliance or certification
What is included
- AI system and workflow scope review
- Prompt injection and abuse scenario testing
- Data exposure and sensitive information review
- Tool, permission, and action boundary review
- Knowledge source and retrieval risk observations
- Unsafe output and handoff review
- Prioritised findings and remediation notes
- Written review summary
What is not included
- Full penetration test of unrelated systems
- Formal compliance certification
- Legal advice
- Model training, fine-tuning, or model replacement
- 24/7 monitoring or managed detection
- Production fixes unless separately scoped
- Direct checkout before written scope confirmation
Delivery process
1
Scope and architecture intake
Review the AI workflow, user exposure, data context, tools, knowledge sources, and intended actions.
2
AI risk testing
Test agreed scenarios around prompt injection, data exposure, unsafe output, permission boundaries, and handoff behavior.
3
Findings and prioritisation
Document risk observations, severity, business impact, and practical remediation notes.
4
Review and next steps
Walk through findings, limitations, remediation priorities, and any recommended retest scope.
Representative deliverable
Prompt Injection, Data Exposure and AI Permission Boundary Findings Report
All AI services are request-first. Scope is confirmed in writing before any payment, deposit, or implementation commitment.
Frequently asked questions
Related evidence
Relevant Engineering work
Selected public case records related directly to this service scope. Each record keeps its attribution and disclosure boundary visible.
Request a scope review
Tell us about your team, workflows, and data context. We will respond with a written scope and confirmed deliverables before any commitment.