AI Security
Threat modeling, security architecture, and adversarial testing for AI systems — designed in from week one, not reviewed at the end.
AI SecurityPrivate AI
Production AI systems that run entirely inside your infrastructure. No third-party data processor. No inference traffic leaving your network boundary.
The problem this solves
The standoff between the mandate and the review
Most enterprise AI programs stall at the same place: the moment someone asks where the data goes. For organizations under HIPAA, GLBA, CMMC, ITAR, or attorney-client privilege, "it goes to a vendor API" ends the conversation — correctly.
Private AI removes the question from the architecture. Models run on hardware you control, in a network you define, under access controls you already operate. There is no external processor to assess, no data processing agreement to negotiate, and no egress path to defend in an audit.
Open-weight models have reached the point where this is a genuine engineering choice rather than a compromise. For retrieval, extraction, classification, summarization, and most agentic workflows, a well-served open model on your own hardware is competitive with a frontier API — and it is the only option that clears your review.
The hard part was never the model. It is GPU infrastructure, model serving, retrieval architecture, identity-aware access control, evaluation, and operations. That is the work we do.
Definition
What "private" actually means
Vendors use the word loosely. Four boundaries have to hold.
Network boundary
Inference executes on compute inside your perimeter. No outbound connection is required for the system to function.
Storage boundary
Embeddings, indexes, caches, and logs persist in your storage, under your encryption and retention policy.
Identity boundary
Retrieval and tool access run under the requesting user's existing entitlements, enforced at query time.
Telemetry boundary
Observability data stays internal. No usage, prompt, or content telemetry is emitted to a third party.
A deployment that satisfies three of four is not private. We design to all four and document which controls enforce each.
Reference architecture
Nothing crosses the boundary
Scope
What we deliver
- GPU infrastructure — capacity modeling, hardware specification, cluster deployment, scheduling and quota configuration, utilization monitoring.
- Model serving — inference stack deployment, autoscaling, batching and throughput tuning, quantization where it pays, multi-model routing.
- Retrieval architecture — ingestion and chunking, embedding strategy tuned on your corpus, hybrid search, incremental re-indexing, citation grounding.
- Identity and access control — entitlement propagation from your existing directory, enforcement at query time, per-document authorization.
- Integration — connectors to the systems of record your business runs on, with transactional writes, error handling, and reconciliation.
- Security architecture — threat model, network segmentation, secrets management, adversarial testing, and the evidence package your security team needs.
- Evaluation — an evaluation harness built on your data with acceptance criteria agreed before development, plus regression testing in CI.
- Operations — observability, drift and regression monitoring, model lifecycle management, cost attribution, and runbooks.
Deployment options
Four degrees of isolation
| Model | Where inference runs | Connectivity | Typical driver |
|---|---|---|---|
| Private cloud | Your VPC or dedicated tenant | Controlled egress | Most enterprise workloads; fastest path to approval |
| On-premise | Your datacenter | Internal only | Sustained volume, data gravity, cloud prohibited |
| Edge | Facility, device, or plant floor | Intermittent | Latency, bandwidth, or disconnected operation |
| Air-gapped | Isolated enclave | None | Classified or controlled data; no external path permitted |
Air-gapped deployment changes model update logistics, monitoring, and dependency management substantially. These are solvable engineering problems, but they must be designed for from the start rather than retrofitted.
Candour
When private AI is not the right answer
Private AI carries real cost: capital or reserved compute, GPU capacity planning, model lifecycle ownership, and operational staffing.
It is the wrong choice when your data is not sensitive, your volume is low or highly variable, you need frontier-model capability that open weights do not yet match, or you have no team to operate it and no appetite for managed operations.
We will tell you when a managed API is the better architecture, and we have.
Questions
Frequently asked
Are open models good enough for enterprise work?
For retrieval, extraction, classification, summarization, and most agentic workflows, yes. For the hardest reasoning tasks, frontier models still lead.
We evaluate against your actual workload rather than against public benchmarks, because benchmark rank rarely predicts performance on a specific enterprise task.
Do we need to buy GPUs?
Not necessarily. Options include reserved cloud GPU capacity inside your tenant, colocated hardware, or existing on-premise capacity. We model the options — purchase is one outcome, not the assumption.
Isn't on-premise AI more expensive than an API?
Per token, almost always. At sustained volume, frequently not — and for a large share of our clients the comparison is moot, because the API was never an approvable option.
We model both before recommending either.
Is our data used to train models?
No. On a private deployment there is no third party with access to train on. Where a managed API is used for a non-sensitive workload, we configure and verify no-retention and no-training terms and document them.
Can you deploy in a fully air-gapped environment?
Yes. Air-gapped deployment changes model update logistics, monitoring, and dependency management significantly. Those are engineering problems with known solutions, and they need to be designed for from the start.
Related
Our Implementation Framework
Five phases from assessment to operations, each with defined artifacts and a gate before anything reaches production.
The frameworkEvaluate private AI for your organization.
We assess your data, infrastructure, and regulatory position, then tell you which deployment model your constraints actually permit — and what it costs to build and to run.
Direct response from an engineer. Typically within one business day.