PUBLIC LLM SECURITY / OUR DEVELOPMENT VISION
Expand AI adoption. Keep your data under control.
The AI-DLP approach aims to make sensitive data transfer visible and manageable when employees interact with public LLM tools.
What we deliver for your organization
All solutions- 01
Corporate oversight
Aims to strengthen corporate oversight of AI use.
- 02
Reduced data transfer risk
Aims to reduce the risk of sensitive information being transferred to external systems.
- 03
Controlled use
Supports employees in using AI tools in a controlled way.
Overview
An architecture for applying corporate policy across channels such as prompt text, copy-paste and file uploads, assessed together with data types, the client environment and management needs. The approach aims to support corporate data protection processes.
Service scope
- Use case analysis
- Sensitive data classification
- Allow / warn / block policy approach
- Client and browser compatibility
- Assessment of a local inspection architecture
- Management and reporting needs
- Phased rollout plan
Examples of sensitive data: personal data, source code, access keys, corporate documents.
TARGET CAPABILITIES TO ASSESS
- Chrome and Edge compatibility
- Local scanning
- Deployment via GPO / SCCM / Intune
These capabilities are not guaranteed; they are targets to be assessed against your environment.
DELIVERY APPROACH
From deployment to capability, alongside your team.
A working model that understands your current setup, builds a practical architecture and transfers knowledge to your team.
- 01
Discovery and Assessment
We assess your needs, existing infrastructure and technical priorities together.
- 02
Deployment and Integration
We deploy systems to suit your environment and integrate them with the relevant components.
- 03
Training and Handover
We transfer knowledge to your team and share operating documentation and usage processes.
- 04
Management and Support
Within the agreed scope, we provide improvements, consulting and technical support.
Frequently asked questions
Is the aim to block employees' AI use entirely?
The aim is a controlled approach in which the usage and data-sharing rules defined by the organization can be applied.
Where is content inspected?
Architectural choices, including the local inspection target, are assessed together with data security requirements and technical conditions.
How do we get started?
Use cases and data types are identified first; then a phased plan is prepared for monitoring, warning and blocking steps.
Let's build a stronger foundation together.
Let's talk about your current environment and goals, and define the right roadmap for your needs together.
Discuss Your Project