AI Security & Deployment Design
Most organizations want to use AI but don't know how to deploy it securely — or at all. We design the deployment architecture AND secure it. From Microsoft 365 Copilot to custom LLM applications, MCP servers, and AI agent frameworks.
What's Included
AI Deployment Design
Architecture design for AI adoption based on your stack — Microsoft Copilot, Google Vertex AI, Azure OpenAI, Claude, or custom solutions. Clear deployment roadmap.
AI Security Review
Security assessment of existing or planned AI deployments. Data leakage risks, prompt injection vectors, access control, and compliance implications.
MCP Server Security
Security design and review of Model Context Protocol (MCP) servers. Access control, tool permissions, data boundaries, and audit logging.
LLM Application Security
Security review of custom AI/LLM applications — chatbots, agents, RAG pipelines. Guardrails, output filtering, and adversarial testing.
AI Governance Framework
Policies and procedures for AI usage in the organization. Acceptable use, data classification for AI, vendor risk, and regulatory alignment.
Shadow AI Discovery
Identify unmanaged AI tool usage across the organization. Map what employees are using, where data flows, and what risks exist.
How It Works
01
AI Landscape Assessment
Map current AI usage — sanctioned and shadow. Understand business goals, existing infrastructure, and data sensitivity.
02
Stack Selection & Design
Based on your environment (M365, Google Workspace, custom), design the optimal AI deployment architecture with security built in.
03
Security Review
Assess AI deployment for data leakage, prompt injection, unauthorized access, and compliance gaps. Test adversarial scenarios.
04
Implementation Support
Hands-on help deploying AI tools securely — configuration, access controls, monitoring, and user guidance.
05
Governance & Training
Establish AI usage policies, train teams on secure AI practices, and set up ongoing monitoring.
Who Needs This
- CTO / CIO evaluating AI adoption
- CISO concerned about AI data leakage
- Organizations deploying Microsoft 365 Copilot
- Teams building custom LLM applications
- Companies using AI agents and MCP servers
- Regulated industries (finance, healthcare) adopting AI