Lock AI completely
inside your own office
Public APIs are convenient — but your financial records, customer lists, contract terms and source code: do you really want them passing through a third party's GPUs? The OpenClaw security architecture wraps LLM inference, vector retrieval and Agent execution entirely inside a Mac Studio cluster in your office or IDC. Your data never leaves the building, and AI works as usual.
4 irreversible risks of handing your data to a public API
Cheap, easy and fast — the advantages of public AI APIs are undeniable. But for core enterprise assets, once any of these four things happens, there is no way back.
Your data gets trained on
A single line in the terms — "we may use your data to improve our services" — is enough for your contract clauses, patent documents and customer lists to enter the training set of the next-generation model.
Cross-border compliance
GDPR, China's Personal Information Protection Law, cross-border data transfer security assessments — with public API servers located overseas, every call is a "cross-border transfer," and the compliance risk compounds.
Service availability
Public API outages, account bans, rate limits, model swaps — your AI workflow is entirely beyond your control. Betting critical systems on "someone else's cloud" is essentially gambling.
Audit blind spots
On the API side you cannot see the call records, the prompt logs or the output content. If a leak happens later, you cannot even establish who leaked it, from where, or how.
From hardware to application · fully private
"Installing a local model" does not make something secure. Genuine enterprise-grade security requires every layer, from hardware to prompt, to be controllable, auditable and traceable.
① Hardware layer · Mac Studio cluster
3–8 M4 Ultra Mac Studios form a local inference cluster. Each unit has 512 GB of unified memory, runs models up to 128B locally, and costs only about 1/5 of an equivalent A100/H100 setup.
② Network layer · zero-trust
Office egress NAT + leased line + WireGuard. All inference, RAG and Agent execution happen entirely on the internal network. Only the audit interface is exposed externally.
③ Model layer · local weights
Claude-style local models (Glass / Qwen3 / DeepSeek) have their weight files stored encrypted and loaded on demand. Model upgrades are pulled and reviewed centrally by an administrator.
④ Data layer · end-to-end encryption
The vector store, KB and conversation logs are AES-256 encrypted before being written to disk. Keys are managed by an HSM with role-based, fine-grained access. Triple protection at the database, file and backup layers.
⑤ Identity layer · enterprise SSO
Integrates with Feishu / DingTalk / AD / LDAP. Every AI session is bound to a real employee identity. No anonymous calls, no shared accounts.
⑥ Monitoring layer · audit trails
Every prompt, every tool call and every model output is written to a WORM (append-only) audit log. Paired with SIEM for real-time alerts, with output ready for MLPS assessment.
A 5-step method · from assessment to compliance
Assess
Take stock of data sensitivity levels, user scale and compliance goals (MLPS / crypto eval / GDPR). Deliverable: a "Private Deployment Feasibility Report."
Build
Procure the Mac Studio cluster or reuse an existing IDC · network segmentation · zero-trust gateway · HSM deployment. Infrastructure completed in 2–4 weeks.
Go live
Model deployment · Agent integration · enterprise SSO connection · audit log pipeline wired up. Gradual rollout · A/B cutover.
Audit
Align with MLPS 2.0 / the Data Security Law / cryptography evaluation · complete self-testing and third-party assessment. Deliverable: a "Compliance Self-Assessment Report."
Certify
Accompanied passage of MLPS Level 2/3 assessment · cryptography evaluation (optional). Annual re-testing · ongoing regulatory compliance.
3 types of organization · 3 deployment modes
The same security architecture can run on a Mac Studio cluster tucked in the corner of your office, or be deployed in a defense-grade IDC. Choose by your compliance level.
Choose a package by compliance level · review annually
Every package can be upgraded to crypto evaluation Level 2 / MLPS Level 3. All of our private-deployment consultants hold cryptography-evaluation engineer / MLPS assessor qualifications.
Small teams / studios
Mac Studio × 1–2 · single-office deployment · suited to teams under 30 people.
- M4 Ultra × 1–2 units
- Local inference + RAG
- Enterprise SSO
- Basic audit logging
100–500-person enterprises
Cluster × 3–4 · zero-trust network · MLPS 2.0 ready · with annual maintenance.
- Cluster × 3–4 units
- Zero-trust + HSM
- MLPS 2.0 Level 2 ready
- SIEM audit integration
- 12 months of maintenance
Government / finance / defense
Crypto eval Level 2+ · MLPS Level 3+ · cross-border data transfer assessment · full-stack compliance consulting.
- Crypto eval / MLPS accompanied testing
- Cross-border data compliance
- Red-blue team exercises
- End-to-end audit
- Annual compliance review
Free security architecture assessment
Keep the data in your office, keep the AI for your team
We send 1 security architect and 1 compliance consultant who, on-site or remotely, spend 2 hours assessing your data sensitivity levels, compliance requirements and existing infrastructure, then deliver a "Private AI Architecture Blueprint."
Request a security assessment →Frequently Asked Questions
What does the Huihuo Agent AI security architecture solve?
For AI Agents and enterprise data, it wraps LLM inference, vector retrieval and Agent execution on-premise so that data never leaves the building.
What hardware does it run on?
It can be deployed on a Mac Studio on-premise cluster (M4 Ultra ×3–8) in your office or IDC, sized by scale and compliance level.
Which compliance requirements does it meet?
Zero-trust access control plus a full-stack MLPS 2.0 / cryptography-evaluation compliance solution. Contact Qingdao Huoyiwu (phone 18554898815, email support@huo15.com).