Identity.
The first question is not what the AI can do. It is who the work is attributed to, which human started it, and which AI Worker acted inside the workspace.
FOR SECURITY LEADERS
AI work needs a record before it scales.
The old questionnaire asked whether customer data was encrypted at rest. The new one asks where the AI audit trail lives, who approved the access, and whether the record can be shown by named user for the last ninety days.
Vantage Workspace is built for that question. It is the sovereign self-hosted AI workspace where your AI workforce works alongside your humans: same identity model, one configured evidence path inside the selected customer environment.
SECURITY QUESTIONS
The proof has to survive the moment someone asks for the record.
- What is the security proof path for AI work?
- The proof path is identity, data boundary, model route, policy decision, and evidence trail. If any one of those is missing, the organization may have AI activity but not a defensible operating record.
- Can regulated teams use public LLMs safely?
- They can use approved public LLMs more defensibly when the route is governed by policy, constrained by the workspace boundary, and logged under the same audit model as other regulated work.
- What does NemoClaw do in public terms?
- NemoClaw is the AI ingress firewall outcome: sensitive data is checked before model access. The public claim is the boundary and the evidence it leaves, not the internal detection mechanics.
- What should a security leader ask during evaluation?
- Ask who acted, which source was touched, which model route handled the request, what policy decision was made, and whether that record can be shown later without stitching together separate systems.
THE PROOF PATH
The CISO question has moved from policy to evidence.
Connecting AI to business systems is like hiring a capable new colleague into a sensitive function. Capability is not the hard part. The hard part is knowing which drawer they opened, which route they used, who approved it, and what was left behind afterwards.
Data boundary.
Files, mail, chat, documents, meetings, and AI work sit inside a customer-owned environment instead of scattered across unmanaged side tabs.
Model route.
Teams can use approved model routes, including public LLMs where policy allows, without giving those models a free pass to every source of customer data.
Policy decision.
A request is allowed, denied, redacted, or escalated by policy before it becomes an invisible habit in the business.
Evidence trail.
The record is available afterwards: named user, named worker, source, route, decision, and time. That is the part the auditor can inspect.
PUBLIC LLM USE
The model is not the only boundary.
A bank, hospital, manufacturer, or public-sector team may still choose to use GPT, Claude, Llama, or another approved model. The security question is not simply whether a model is public or private. It is whether the route to that model is governed, logged, and constrained by the same rules the organization applies to people.
In Vantage Workspace, public LLM access can sit behind the workspace boundary instead of becoming a browser habit that no one can reconstruct later. The customer keeps the operating environment, identity model, and audit trail under its own control.
WHAT THE CISO GETS
A defensible control story for AI work.
A sovereign, self-hosted workspace running on infrastructure the customer owns.
Named AI Workers working under the same identity model and audit trail as human teammates.
Policy-controlled model routes, so approved public LLM use does not become unmanaged public LLM use.
NemoClaw at AI ingress, positioned by outcome: sensitive data is checked before it touches a model.
An assessment module mapped to eleven frameworks, with templates kept current as the enforcement schedule advances.
A running workspace that can be verified from deploy script to single-tenant environment in about sixty minutes.
BOUNDARIES
What this page is not claiming.
- It does not certify your organization by itself.
- It does not replace your security team, privacy function, or regulator-facing judgment.
- It does not require Handvantage to become your model provider. Customers bring the LLMs they approve.
- It does not expose confidential firewall mechanics in public material. Outcomes are public; deep internals stay restricted.
NEXT STEP
Bring one hard AI workflow to the table.
The useful conversation is not a product tour. It is one workflow: the data it touches, the model route it needs, the approval it requires, and the evidence a regulator or board would ask to see afterwards.
A 20-minute strategy session with a senior engineer is enough to pressure-test that first workflow.