Data Governance & Isolation
Keep sensitive data out of public models. We architect AI to run on-prem or inside your own tenant, so your prompts and outputs never leave your control.
AI Security & Governance How to secure AI -
How to secure AI -
before you deploy it.
Most organizations aren't afraid of AI. They're afraid of deploying it without guardrails. We build the guardrails, so cautious and regulated teams can adopt AI without handing over the data underneath it.
The problem isn't AI. It's the missing guardrails.
Your team wants the productivity. Your board wants to know the client data, the PHI, and the trade secrets aren't about to leak into someone else's model. Both can be true, but only if the controls are in place first. Today, in most companies, they aren't: staff paste sensitive data into public tools, there's no policy, and nothing is logged.
The six guardrails we put around your AI
A complete perimeter, from where the data lives to who reviews the output, so AI works for you without becoming your next breach.
Access & Identity Controls
Role-based access to models and data, scoped API keys, and least-privilege service accounts, so only the right people and systems can ever invoke your AI.
Input & Output Guardrails
Prompt-injection defense, output filtering, and PII redaction sit between your users and the model, the OWASP LLM Top 10 risks, actively contained.
Model & Supply-Chain Security
We vet the models, libraries, and vendors in your AI stack, provenance, licensing, and known vulnerabilities checked before anything reaches production.
Monitoring & Audit Trails
Every prompt, response, and decision logged and reviewable. Full audit trails for regulators, plus monitoring for abuse, data leakage, and model drift.
Policy & Human Oversight
A written AI-use policy, human-in-the-loop on high-stakes decisions, and staff guidance, mapped to the NIST AI Risk Management Framework.
Run AI where your data lives
The single biggest risk in enterprise AI is data leaving your control. So for sensitive and regulated workloads, we don't send your data to a model, we bring the model to your data. Private AI that runs on your hardware, in your building, means prompts and outputs never touch a public cloud, and the audit trail is entirely yours.
That's the idea behind Reservoir, our private on-prem AI package, the same "run AI where the data lives" principle, packaged and ready to deploy.
Explore Reservoir & our packages →
How we secure your AI
A straight path from "we're nervous about this" to "it's deployed, governed, and we can prove it."
Assess
We map where AI touches your data and where the exposure is, shadow AI, third-party tools, and the models already in use.
Design guardrails
We design the controls: data boundaries, access rules, filtering, logging, and a written policy scoped to your risk and industry.
Deploy securely
We stand the AI up the right way, on-prem or in your tenant where the data is sensitive, with the guardrails wired in from day one.
Monitor & govern
We watch it in production: audit trails, abuse detection, and periodic review as models, threats, and regulations change.
Anchored to the standards that matter
We measure your AI program against recognized frameworks, and help you close the gaps, honestly.
NIST AI RMF
The NIST AI Risk Management Framework, the U.S. benchmark for governing, mapping, measuring, and managing AI risk.
OWASP LLM Top 10
The industry list of the top security risks in LLM applications, prompt injection, data leakage, and more, each with a control.
ISO/IEC 42001
The international standard for AI management systems, for organizations that need a formal, auditable governance program.
AI security, answered
The questions cautious teams ask us before they adopt AI.
Is it safe to use AI with sensitive or regulated data?
It can be, but only with guardrails in place. The risk is not AI itself; it is sending regulated data to a model you do not control, with no logging and no policy behind it. We put the boundaries, access controls, and audit trails in place so your team can use AI without exposing the data underneath it.
What exactly are AI guardrails?
Guardrails are the technical and policy controls that sit around an AI system: where its data can go, who can use it, what goes in and comes out, what gets logged, and who reviews high-stakes decisions. Together they let you adopt AI while keeping it inside the lines your industry and regulators require.
Do we have to send our data to a public cloud model?
No. For sensitive or regulated workloads we run AI where your data already lives, on-prem or inside your own tenant, so prompts and results never leave your control. Our Reservoir package is built exactly for this: private AI that runs on your hardware.
What frameworks do you follow?
We align our AI security work to the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications, and ISO/IEC 42001 for AI management systems. We help you measure against these frameworks and close the gaps, we do not claim they make you automatically compliant.
We are a small team, is this overkill?
No. Smaller teams are the ones most likely to have staff quietly pasting sensitive data into public AI tools with no policy at all. The controls scale down: even a written AI-use policy, scoped access, and basic logging remove most of the real risk.
How do we get started?
Start with a free assessment. We map where AI already touches your business, show you the exposure, and give you a prioritized plan for the guardrails that matter most, no obligation.
See the wider security program these controls plug into, Managed Security and our full security services.
Adopt AI without the risk.
Book a free assessment. We'll show you where AI already touches your business, where the exposure is, and the guardrails that close it, no obligation.
Get a Free AI Security Assessment