Secure AI Implementation

Field notes

We design AI deployments from published security reference architectures, not vibes: input/output validation at every trust boundary, least-privilege credentials for anything the model can call, human approval gates on consequential actions, and logging that lets you reconstruct what the AI did and why. Most of the risk is decided before the first prompt: in vendor tier selection, data-retention settings, and which data sources the model may touch. We make those decisions deliberately, with you.

What we do

• Secure architecture review

  Reference-architecture-based design: validation boundaries, sandboxing, and human-in-the-loop controls for consequential actions.

• Data governance for AI

  What data can touch which model? DLP controls so customer PII never lands in a third-party training set.

• Vendor selection & configuration

  Enterprise vs consumer AI tiers, data-retention settings, SSO/MFA enforcement. Most AI data leaks are a settings problem.

• Guardrails & monitoring

  Content filtering, prompt/response logging, and incident-response runbooks extended to cover AI systems.

• Acceptable-use policy & training

  A practical AI policy your team will actually follow, because they are already pasting data into free AI tools.