Selected Engineering Work
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Verified selected engineering work for Rust-based AI-agent, MCP-tool, and API-payment security.
View caseAI System Security Review
FromIDR 135,000,000~US$7,550Reference date: 9 Oct 2026 · Local price is authoritative; this is not a payment or settlement rate.
What you get
A scoped security review of a deployed or near-launch AI system — LLM workflow, chatbot, internal assistant, retrieval system, or API-connected automation. Written findings covering data exposure, prompt injection, permission boundaries, and remediation priorities before wider rollout.
Why security-led AI integration matters
AI systems connect directly to user inputs, internal data, APIs, and business tools. Without a deliberate security review, common failure modes go undetected: prompt injection through WhatsApp or chat inputs, data exposure through retrieval systems, unsafe outputs reaching users or downstream systems, and actions taken without human approval.
This review focuses on prompt injection, data exposure through connected tools and retrieval flows, insecure output handling, model or tool abuse, knowledge-base and retrieval risk, permission boundary weaknesses, unsafe API or tool access, and missing logging and human approval points.
BilgeQor's AI services are built around written scope, approved data sources, permission boundaries, human approval points, and review before wider rollout.
Scope drivers
Number of AI workflows or systems reviewed
Internal vs customer-facing exposure — app, WhatsApp, web chat, helpdesk, or internal tool
Model, tool, and integration architecture
Knowledge source and retrieval design
Permission boundaries and action capability
Sensitive data exposure and handling risk
Prompt injection and abuse scenario depth
Required remediation and retest depth
Ideal for
- Indonesian product, app, SaaS, and e-commerce teams preparing to launch or expand an AI feature
- Support and operations teams using AI-connected WhatsApp, chat, CRM, or helpdesk workflows
- Businesses with customer-facing chatbots, internal assistants, or retrieval-based knowledge systems
- Technical teams or CTOs who have built an AI workflow and need an independent review before wider rollout
- Leaders who want practical risk findings and remediation priorities without overstating certification
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
Excluded
- Full penetration test of unrelated systems
- Formal compliance certification or audit
- Legal advice
- Model training, fine-tuning, or model replacement
- 24/7 monitoring or managed detection
- Production fixes unless separately scoped
- Payment or checkout before written scope is confirmed
How it works
1
Scope and architecture intake
Review the AI workflow, user exposure, data context, tools, knowledge sources, intended actions, and API or channel connections.
2
AI risk testing
Test agreed scenarios covering 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 require scope confirmation before work begins. Submit a request — we review and confirm in writing before sending a payment link.
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.
Ready to get started?
Submit a service request — we confirm scope before any payment