Private AI, on-prem or cloud
Confidential computing & TEE
AI that staysinside your boundary.
On-prem, air-gapped, and private cloud environments need models, RAG, and agents that never leave approved enclaves—including data protected in use through confidential computing and trusted execution environments (TEEs). OWCER designs private AI architecture, guardrails, and activation paths without forcing public SaaS where policy forbids it.
When private AI matters
Public AI isn’t always the answer
Defense contractors, healthcare operators, and financial services buyers often need models and data planes they can explain to auditors—not just a checkbox on a vendor trust center. Public SaaS LLMs may meet productivity goals, but they rarely satisfy policy when prompts, RAG corpora, and model weights must stay protected in use—not only at rest and in transit.
Data residency & enclave requirements
GCC High, IL5-adjacent patterns, air-gapped labs, and private cloud landing zones need deployment models that match contract language—not generic public-cloud defaults or “region pinning” on a shared multi-tenant stack.
Data in use without hardware isolation
TLS and disk encryption do not answer who can read GPU memory during inference. Regulated buyers ask who sees prompts, embeddings, and weights while models run—including platform operators and privileged host access.
Public SaaS trust gaps
No-training clauses and enterprise agreements document vendor intent. They do not remove the architectural reality that shared infrastructure, operator access, and legal process can expose workload memory unless hardware isolation shrinks the trust boundary.
Self-hosted models with no activation plan
On-prem GPU clusters, private endpoints, and TEE-enabled inference sit idle without workflow integration, champion networks, and governance prerequisites.
Audit questions you can’t answer yet
Who can prompt which model? What data is indexed? Where are logs retained? We implement controls before scale, not after an incident.
Data privacy reality
Why data in use matters for AI
Productivity gains from public SaaS AI often arrive before security finishes its review. Uploading proprietary designs, customer contracts, or unreleased financials sends that material into a vendor’s trust boundary—where training opt-outs and retention windows describe intent, not who can read prompts and outputs while models run on shared infrastructure.
Encryption at rest and in transit is necessary but insufficient: during inference, inputs are decrypted, tokenized, and loaded into GPU memory; RAG retrieval merges sensitive corpus text into the context window. At every step, data exists in use—visible to anything inside the host, hypervisor, and privileged administrators unless hardware isolation limits who can observe memory.
Recent litigation and preservation orders against major AI vendors show client content can be compelled into discovery—regardless of no-training clauses. Private AI architecture, narrowly scoped contracts, and hardware isolation reduce exposure before litigation, not after.
Hardware TEEs materially raise the bar, but no isolation is perfect. We design for defense in depth—realistic threat models, logging, and governance—not absolute security marketing.
Key privacy risks
What confidential computing addresses
Four architectural realities compliance reviewers and security teams ask about before approving AI in regulated environments.
Multi-tenant & operator risk
Shared cloud stacks, co-located tenants, and platform operations teams sit outside your direct control. Subpoenas, insider access, and compromised host kernels are threat models compliance reviewers ask about—not edge cases.
What AI exposes in memory
User prompts, RAG index content, embeddings, adapter weights, and conversation history are all processed together during a single request. A policy breach in use can leak the corpus, not just one query.
Hardware isolation (TEE)
Confidential VMs on CPU—paired with GPU trusted execution where workloads require it—run code and data inside an attested enclave the host OS and hypervisor cannot read or modify. On-prem and air-gapped deployments push the same boundary entirely inside your facility.
Remote attestation
Before decryption keys, model weights, or corpus data enter the enclave, a remote verifier checks cryptographic evidence that CPU and GPU firmware, measurements, and policy match a known-good state. Security and audit teams get proof—not a slide deck—that the environment is genuine.
Related proof
GCC High activation with governance built in
A defense consulting firm moved to Microsoft 365 GCC High but still worked like Google. OWCER restructured SharePoint and Teams, automated provisioning, and delivered audit-ready governance alongside daily operations.
“OWCER mapped our governance prerequisites alongside activation—so we could scale Copilot without waiting for the next audit finding.”
Capabilities
Private AI services
Architecture & landing zones
We design reference architectures for on-prem GPU clusters, private cloud landing zones, and air-gapped labs—not generic public-cloud defaults with a region pin. Self-hosted Llama, Mistral, and other approved model stacks sit behind private endpoints, segmented subnets, and egress controls so inference traffic never crosses boundaries your contract forbids. Compute, vector stores, and key material are placed where your ATO, SOC, or IL5-adjacent scope expects them; identity, logging, and backup paths are wired in from the start so security reviewers see one coherent diagram instead of a bolt-on AI subnet. Whether you need sovereign cloud, GCC High–adjacent patterns, or fully disconnected facilities, we map deployment choices to the language auditors already use—data residency, operator access, and blast radius—before hardware is ordered.
Confidential computing & TEE
Hardware trusted execution environments shrink the trust boundary during inference: confidential VMs on CPU—paired with GPU TEE where embedding generation and model forward passes require it—keep prompts, RAG chunks, adapter weights, and conversation context inside an enclave the host OS, hypervisor, and platform operators cannot read or modify. Remote attestation produces cryptographic evidence that firmware, boot measurements, and policy match a known-good state before a key broker releases decryption material, model weights, or corpus data into the enclave; we document that chain so compliance reviewers get proof, not vendor assertions. We map attestation reports, key-release policies, and enclave boundaries to the questions security teams already ask—who can observe GPU memory, what happens under legal process, and what remains visible to privileged administrators. TEEs materially raise the bar for data in use, but we design for defense in depth: realistic threat models, logging, and governance alongside hardware isolation, not absolute-security marketing.
Grounded with approved data and memories
We index only sources you explicitly approve—SQL and object databases, wikis, SharePoint libraries, file shares, CRM exports, and the enterprise apps your teams already trust—and enforce boundaries so a cleared user’s query cannot retrieve chunks from an adjacent program or classification. Your existing access controls and classification labels travel with content into the index and back out at retrieval, so higher-trust material does not bleed across scopes. Answers include citations leadership can review in audit discussions. An ongoing memory and data-management feedback loop—refresh cycles, governance checkpoints, and human-in-the-loop curation—keeps corpora and memories current instead of freezing knowledge at go-live. In private and air-gapped deployments, embedding generation and vector stores stay inside your network boundary—separate per source where policy requires—so cross-corpus leakage is a design constraint, not an afterthought.
Guardrails & logging
We define policy-enforced use cases before rollout—who may prompt which models, on what data classes, and which outputs require human review before release. Prompt and response filters block prohibited content, PII leakage, and off-scope requests at the gateway; high-risk actions route through human-in-the-loop approval flows instead of silent automation. Immutable audit logs capture identity, model, corpus scope, retrieved sources, and full prompt/output pairs where your retention policy allows—evidence compliance teams can replay without vendor black boxes. Guardrails connect to your existing AI governance and identity controls so access, logging, and policy stay in one reviewable chain.
