Secure Code.
Safe Models.
Trusted AI.
A quiet, engineering-grade AI security lab. We provide the control, audit, and risk reduction required for modern enterprises to safely deploy LLMs and AI-assisted development.
AI adoption is blocked by risk.
Enterprise leaders want the speed of AI, but cannot compromise on data privacy, compliance, and code integrity. We bridge the gap between rapid innovation and strict governance.
Data Exposure
LLMs inadvertently ingest and leak PII, PHI, and proprietary corporate data.
Code Vulnerabilities
AI-generated code introduces silent vulnerabilities and insecure open-source patterns.
Lack of Audit
No visibility into what prompts are being sent, what responses are returned, and who is accessing models.
Our Solutions
AI Security by Design
An AI security agent embedded directly into the development process. Analyzes source code, detects vulnerabilities, controls open-source components, and embeds DevSecOps.
A proxy gateway for protecting and safely using LLM models. Filters requests, masks sensitive personal data, blocks prompt injections, and provides a complete audit trail.
Security for autonomous AI agents. Scan skills and MCP servers, observe reasoning and tool use, and enforce policy before agent actions execute.
AI.Pentest
Advanced controlled penetration testing and confidential incident investigation. Time-boxed maximum-coverage assessments, attack-cost analysis, and a dedicated hardware & embedded security lab.
Technology
Control the data flow.
MOAI Labs deploys as an invisible layer between your applications and your AI infrastructure, logging and sanitizing interactions. Performance is measured against your traffic and selected deployment.
Zero-Trust Architecture
Never assume trust. Every prompt is analyzed, scored, and sanitized before reaching the model.
Air-gapped Deployment
Choose a deployment boundary that fits your requirements, including customer-controlled infrastructure where available.
Comprehensive Audit
Review policy decisions and interaction events, then define the export and retention controls your team needs.
Business Value
Build an evidence-led AI security case.
Security should not block innovation. Start with a baseline, test the controls that matter to your team, and agree on success criteria before deciding whether to roll out.
- Unblock InnovationGive engineering teams the green light to use LLMs knowing data is sanitized.
- Prevent Costly LeaksApply explicit masking and policy controls to the sensitive data classes your team identifies.
- Measure Before RolloutCompare detection, policy, audit, and performance observations with your agreed baseline.
Illustrative planning model
These are assessment inputs, not published customer results. Replace them with observations from your own traffic and workflow.
Outcomes vary by traffic, policy configuration, deployment, and baseline. No guarantee or benchmark is implied.
Evidence & Assessment
Evidence starts with a scoped assessment.
We do not publish customer outcomes, testimonials, logos, or certifications without source evidence. Instead, an evaluation can produce a factual record for your own review.
Map the boundary
Document model endpoints, request paths, sensitive data classes, deployment boundaries, retention, and access assumptions.
Exercise the controls
Use approved synthetic or customer-provided scenarios to check masking, policy decisions, prompt-injection handling, and false positives.
Review the evidence pack
Capture test inputs, decisions, audit events, performance observations, and open risks so your team can decide what comes next.
Explore the existing technical and commercial detail.
Read the AI.Guard architecture, compare pricing, or email a review request. Emailing is a request only, not a confirmed booking.
Free text download. No email or registration required.
Built for regulated environments
Request an architecture review.
Email our existing team address to request a 30-minute conversation about your AI architecture, data flows, and evaluation criteria. If a pilot is appropriate, scope and availability can be discussed in the reply.
- 30-minute architecture and integration conversation
- Review of data-boundary and policy questions
- Discuss whether a scoped evaluation is a fit
Email a review request
Tell us which product or architecture you are evaluating and what you want to validate. We will reply through the existing email channel.
Request a 30-minute architecture reviewEmail request only. Sending this message does not confirm a meeting, pilot, or outcome.