AI Implementation & Security
Put AI to Work Without Losing Control of Your Data.
Launch MSP helps federal contractors and regulated organizations evaluate, build, secure, and operate AI. From Microsoft 365 Copilot readiness to custom applications and agents, we build governance, identity, data protection, and monitoring into the workload from the start.
Workplace AI
Copilot, Deployed Like It Touches Real Data
Microsoft 365 Copilot works within each user's existing permissions. Where SharePoint access has drifted over the years, it does not create a new problem so much as make the existing one far easier to discover. Most of this work happens before anyone turns Copilot on.
Copilot Readiness Reviews
SharePoint and Permission Remediation
Oversharing and Sensitive Content Risk
Microsoft Purview and Data Loss Prevention (DLP)
Copilot Studio Agents
Licensing, Policy, and Adoption
Custom AI Workloads
Applications and Agents Built to Survive an Audit
When an off-the-shelf assistant is not enough, we start from the data: what the AI will use, and what contractual, regulatory, and security requirements apply. That decides the deployment — which may be Microsoft Foundry (formerly Azure AI Foundry), Azure OpenAI, or an approved option for Claude or the OpenAI API — chosen for the data involved, not the other way around.
An AI system should return only what the person asking is allowed to see. We enforce that where the information is retrieved — not only through instructions given to the model, which can be worked around.
We work in Microsoft 365 GCC High and Controlled Unclassified Information (CUI) enclave environments, where the deployment model rather than the feature list decides what is available and what is permitted.
Foundry Applications and Agents
Built, versioned, and deployed with an owner and a rollback path.
Secure Retrieval and Knowledge Systems
Retrieval that respects the permissions of the person asking.
Identity and Private Networking
Workload identity, private connectivity where supported, and managed secrets.
Evaluation and Red Teaming
Evaluated against representative prompt-injection, authorization, leakage, and reliability scenarios.
Monitoring, Cost, and Lifecycle
Usage, spend, and model changes visible before they become surprises.
Where the Real Risk Sits
The Questions an Auditor Will Ask Before You Do
Access to a model is not the hard part, and it is not what we sell. The hard part is being able to answer, in writing, what the system can reach and who decided that.
We use the NIST AI Risk Management Framework and its Generative AI Profile to structure risk decisions, documentation, evaluation, and ongoing management. The framework is voluntary and there is no NIST certification for AI.
- What data can the AI actually reach?
- Is CUI, protected health information (PHI), or client data in scope?
- Which deployment and contract terms fit that data?
- How are users, agents, and applications authorized?
- How are prompts, outputs, and actions logged?
- Where must a human stay in the decision path?
How We Work
From Use Case to Production, Without Skipping the Middle
Stalled AI projects often did not fail technically. They reached the point where somebody asked what data the thing could see, and there was no answer ready.
Discover
Identify use cases worth doing and map the data each one touches, including the sensitive data nobody listed.
Govern
Agree policy, ownership, acceptable use, and risk tolerance before anything is built.
Build
Establish identity, networking, data boundaries, and application architecture appropriate to the classification involved.
Validate
Test permissions, outputs, prompt attacks, reliability, and the human controls that are supposed to catch failures.
Operate
Monitor usage, risk, quality, cost, and model changes over the life of the workload.
Let's Talk
Bring Us the Use Case You Are Not Sure Is Safe
Whether you are evaluating Copilot, already running something in production, or holding back because of the data involved, we will talk through what it touches and what it would take to run it properly.