Prevent Data Leaks & Cost Spikes in LLM Apps
I implement access controls, monitoring, and spend limits for RAG, agents, and chatbots—so you avoid tool misuse, data exposure, and surprise bills.
Access Controls · Monitoring · Spend Limits · RAG & Agents
About Me
Get to know the person behind the code
Production controls for RAG & agent systems
I help companies ship AI products that don't embarrass them. I implement access controls, monitoring, and spend limits for RAG, agents, and chatbots.
Access controls + monitoring implementation
24+ automated checks (security, reliability, cost)
Spend limits + alerting setup
Before/after evidence verification
Google Cloud Professional Cloud DevOps Engineer
Verify on Credly
Core Practices & Technologies
Site Reliability Engineering
Monitoring, alerting, and system reliability best practices
Infrastructure as Code
Terraform, configuration management, and declarative infrastructure
Observability & Monitoring
Logging, metrics, tracing, and comprehensive system visibility
CI/CD & Automation
Pipeline automation, testing, and deployment strategies
GitOps & Config Management
Config Connector, Git-driven deployments, and declarative workflows
Container Orchestration
Kubernetes, containerization, and cloud-native architectures
Skills & Expertise
AI Security & Reliability
LLM Red Teaming
RAG Security & Reliability
Guardrails (NeMo, Custom)
Prompt Injection Testing
PII Detection/Redaction
Backend Development
Python / FastAPI
LangChain
Vector DBs (Pinecone/Qdrant)
PostgreSQL / SQLModel
Node.js
Frontend Development
React / Next.js
TypeScript
MUI / Tailwind
HTML5 / CSS3
DevOps & Infrastructure
GCP / Cloud Run
Terraform / IaC
Docker / Kubernetes
CI/CD (Cloud Build)
Observability & Monitoring
Featured Work
A showcase of projects I'm proud to have built
Featured Projects
Services I Offer
Implementation-first solutions for production LLM safety
Incident Prevention Sprint
Fixed Price
I ship code: access controls, monitoring, and spend limits—so your LLM app doesn't leak data or spike costs.
- Merged PRs with guardrails implementation
- Dashboards & alerts configuration
- Runbook for incident response
- Before/after evidence verification
Typical duration: 1-2 weeks
48-hour Baseline (Evidence + Plan)
Fixed Price
Baseline findings across security, reliability, and cost vectors. Includes repro steps, severity ratings, and sprint scope.
- Security baseline (prompt injection, data leakage)
- Reliability checks (hallucinations, tool misuse)
- Cost vulnerability assessment
- Prioritized sprint scope document
Typical duration: 48 hours
My Process
A systematic approach to production LLM safety
Discovery
Understand your AI application and attack surface
Baseline
Run comprehensive safety verification
Evidence
Deliver findings with repro steps and severity ratings
Implement
Ship guardrails and safety controls
See What You'll Get
Download a sample baseline report to see the depth of analysis and implementation roadmap.
LLM Production Safety Report
Baseline findings + implementation roadmap for a RAG chatbot
Critical Findings Preview
• System prompt extraction + PII leakage
• Hallucinations + competitor mentions in output
• $500+/day uncontrolled spend exposure
11 Issues Found
- CRITICAL
System Prompt Extraction via Role Play
- HIGH
PII Leakage in Context Window
- HIGH
RAG Document Access Bypass
- MEDIUM
Competitor Mention Generation
What Clients Say
Don't just take my word for it - here's what my clients have to say
Musah delivered an exceptional web application that exceeded our expectations. His technical expertise, attention to detail, and ability to understand complex business requirements made our project a huge success. The platform he built has significantly improved our operational efficiency.
Managing Director at Coriable
Working with Musah on our AI-powered analytics platform was transformative for our business. His expertise in machine learning and web development helped us create a solution that not only looks great but provides incredible insights from our data. The results have been game-changing.
Co-Founder at AfriShopa
Talk About Your LLM App
If you're building or running a RAG app, agent, or chatbot, book a call. I'll outline the first controls I'd implement.