Frame usage before deployment
An AI assistant, local inference on AMD/NVIDIA GPUs or business integration must not become a security blind spot. We analyze data, users, flows, logs, permissions, models and regulatory constraints to define a realistic and verifiable framework.
Typical engagements
- Internal AI use-case framing: authorized data, risks, confidentiality, traceability and validation.
- Business process automation: task qualification, handled data, human validation, permissions and logging.
- Local inference on AMD/NVIDIA GPUs or internal assistant architecture inside a controlled perimeter.
- Governance of prompts, documents, access, logs and datasets used by AI systems.
- Risk analysis for AI SaaS tools, browser extensions, connectors and business integrations.
- Internal AI policies understandable by IT, business teams, DPOs and CISOs.
Goal
- Enable the right uses without exposing sensitive data.
- Give leadership a clear view of AI risks.
- Deploy useful, controlled and auditable assistants.
- Avoid technical decisions that cannot be maintained.