Buyer guide: cloud AI vs private AI
FedRAMP / GCC High AI vs On-Prem AI for Your ERP: An Honest Comparison
Short answer
FedRAMP High or GCC High authorization tells you a cloud service's security controls have been assessed to a federal baseline, it does not by itself resolve ITAR deemed-export risk, CUI handling scope, or where your ERP's underlying data and embeddings physically sit. On-prem or private-cloud AI grounded in the ERP removes that ambiguity by keeping data inside a boundary the organization already controls, at the cost of owning more of the deployment. The right answer usually depends on data classification, not on which option sounds more compliant.
- ERP
- SAP S/4HANA, Oracle EBS, Infor CloudSuite Industrial, Microsoft Dynamics 365, Deltek Costpoint
- Industries
- Aerospace, Defense, Government Services, Manufacturing
- Written for
- CISO
A CISO evaluating AI for an ERP holding CUI or ITAR-controlled data usually hits the same wall: a vendor pitches a FedRAMP High or GCC High authorized copilot as the compliant answer, procurement likes that it comes with a badge, and the security team is left to explain that a FedRAMP authorization is a statement about the cloud service's controls, not a guarantee that every category of data in the ERP is cleared to go there.
The gap matters most for ITAR technical data. ITAR is about the nationality of the people who can access controlled technical data, not the compliance certification of the infrastructure it sits on. A FedRAMP High or GCC High environment staffed or supportable by non-US-person personnel does not automatically satisfy ITAR, and a vendor's FedRAMP badge does not answer that question for you; only the vendor's specific access model and your own legal review do.
On-prem or private-cloud AI grounded in the ERP sidesteps this ambiguity by design: the model, the retrieval index, and the ERP connector all run inside a network boundary the organization already controls and has already had assessed for the ERP itself. The tradeoff is real too, the organization owns GPU procurement, model updates, and operational support instead of consuming a managed service, and that is a legitimate cost to weigh against the compliance clarity.
Most contractors end up with a hybrid answer rather than an absolute one: FedRAMP High or GCC High tenancy for genuinely non-sensitive workloads where a managed service is the pragmatic choice, and on-prem or private infrastructure for anything touching CUI, ITAR technical data, or contract terms that restrict where the information can be processed. This page is built to help make that split defensibly, not to argue either option is always right.
What usually gets in the way
The problems we hear most from ciso teams running SAP S/4HANA.
"FedRAMP authorized" gets treated as a blanket compliance answer
Procurement and business stakeholders often assume a FedRAMP badge means any data can go to that service, when the authorization is specific to the cloud service's security controls, not to every data classification the organization holds.
ITAR deemed-export risk survives a FedRAMP authorization
Even a FedRAMP High or GCC High environment can expose ITAR technical data to non-US-person access through support staff or infrastructure operations unless the vendor's specific personnel model is verified separately.
Embeddings and vector data location is often unclear
A copilot's marketing rarely specifies where the vector embeddings derived from ERP data physically reside or how long they persist, which matters as much as where the raw data sits.
The authorization boundary rarely covers the ERP itself
A FedRAMP authorization applies to the AI service, not to the ERP hosting environment feeding it, so the overall data flow can still cross an unauthorized boundary even when the AI layer alone is compliant.
Procurement pressure to just buy the built-in copilot license
When the ERP vendor's own copilot is already licensed and marketed as compliant, the CISO is often asked to approve it quickly rather than walk the data classification question through to a real answer.
Where AI earns its place in SAP S/4HANA
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Natural language query over CUI-tagged ERP data
A program manager asks a plain-language question about contract funding or schedule status, and the model answers grounded in the ERP's project and contract tables, with the underlying records shown.
Touches: Project ledger, contract and funding tables, CUI-marked document metadata
Outcome: Where this runs determines the compliance story: on infrastructure the organization controls, the answer stays inside the existing CUI boundary by construction.
Purchase order exception triage
Summarizes open PO exceptions (past due, price variance, quantity mismatch) for a buyer, grounded in live purchasing data rather than a static report.
Touches: Purchase order header/detail, receiving and invoice matching tables
Outcome: A useful, low-sensitivity workload that is often a reasonable first candidate for a FedRAMP-hosted copilot if the underlying PO data itself is not CUI.
Engineering change impact summary
Lists open jobs, orders, and BOM lines affected by a newly released engineering change, drawing on ERP BOM and job data.
Touches: Engineering change records, BOM and routing tables, open job/order tables
Outcome: On defense programs this data is frequently ITAR-relevant, which pushes this specific use case toward the on-prem option even if other workloads sit in a FedRAMP tenant.
Indirect rate and cost variance narrative drafting
Drafts a first-pass explanation of indirect rate variance from the project ledger for a cost accountant to review, the same underlying workflow regardless of deployment model.
Touches: Project ledger, indirect rate pool and base tables
Outcome: Illustrates why deployment choice tracks data sensitivity, not use case: the same task can sit in either environment depending on what data it touches.
Shop floor work instruction lookup
An operator asks a natural-language question about a routing step or work instruction and gets an answer grounded in the ERP's routing and document data.
Touches: Routing/operations tables, linked work instruction documents
Outcome: Usually low sensitivity and a reasonable candidate for whichever deployment model is already in place, since the content itself carries little classification risk.
Supplier risk and AVL question answering
Answers questions about approved vendor list status and known supply risk for a given part, grounded in item master and supplier data.
Touches: Item master, AVL/AML cross-reference, supplier master
Outcome: Sensitivity depends on whether the supplier data ties back to a classified or ITAR program; the same tool can sit in either deployment depending on the program.
Audit document retrieval for a DCAA or DCMA request
Pulls a specific document or transaction and its approval trail from the ERP on request, grounded in project ledger and approval workflow data.
Touches: Project ledger, approval workflow logs, AP/invoice records
Outcome: Given the audit sensitivity of the data involved, most contractors keep this workload on infrastructure they control rather than a managed cloud service.
Reference architecture
Regardless of which deployment a given workload lands on, the five-layer pattern is the same; what changes is where the model-serving and data layers physically sit, and therefore where the authorization boundary needs to be drawn and documented.
- 1
ERP connectors
Read-only integration into the ERP's native API (OData, IDO, BAPI/RFC, SuiteQL, BOD) scoped to a dedicated service account, identical whether the AI layer sits on-prem or in an authorized cloud.
- 2
Data / semantic layer
A mapping and classification layer that tags which fields and tables carry CUI or ITAR-controlled content, which is what actually determines routing to the on-prem versus cloud deployment.
- 3
Model serving
Either an open-weight model self-hosted on the organization's own or private-cloud GPUs, or a model consumed through a FedRAMP High / GCC High authorized service, chosen per workload based on the data classification tag.
- 4
Retrieval and agents
Retrieval-augmented generation and narrow task agents, built the same way in either deployment, with agents that touch classified or ITAR data restricted to the on-prem instance by policy, not just by convention.
- 5
Governance and audit
A single audit log design spanning both deployments, so a security team can show, for any query, which environment handled it and why, based on the data classification tag at the time.
Integration notes for your ERP team
- A data classification pass over the ERP's tables and fields (which modules touch CUI, which touch ITAR-controlled technical data) should happen before choosing a deployment model, not after.
- For workloads sent to a FedRAMP-authorized service, confirm in writing where the model's embeddings and any retrieval index are stored and for how long, not just where the raw data sits.
- For on-prem deployments, the ERP connector pattern (OData, IDO, BAPI/RFC, SuiteQL, BOD) is unchanged from a cloud deployment; only the model-serving location moves.
- Legal counsel, not the AI vendor, should confirm the ITAR deemed-export position for any specific FedRAMP or GCC High service under consideration, since vendors have an incentive to describe their own offering favorably.
- A single governance and audit log design across both environments makes it possible to demonstrate, workload by workload, which environment handled which query and why.
- Role-based access should mirror the ERP's existing permissions in both environments, so moving a workload between on-prem and cloud does not silently change who can see what.
Deployment options
FedRAMP High / GCC High tenant
Non-sensitive, non-CUI, non-ITAR workloads where a managed service reduces operational burden
Uses an authorized cloud service for the model and, often, the ERP's own cloud copilot, appropriate once the organization has verified the specific data types involved are genuinely out of ITAR and CUI scope.
Air-gapped on-prem
CUI, ITAR technical data, or any workload a contract flowdown restricts to a controlled boundary
Model, retrieval index, and ERP connector run entirely inside infrastructure the organization controls with no outbound path, removing the deemed-export and data-residency questions by construction.
Hybrid, split by data classification
Most defense-adjacent manufacturers and government contractors in practice
A documented classification policy routes each workload to the appropriate environment, FedRAMP-hosted for cleared low-sensitivity use cases, on-prem for anything CUI, ITAR, or contractually restricted.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
FedRAMP High authorization scope
Treated as evidence about the cloud service's security controls, not as a substitute for the organization's own data classification decision about what may be sent to that service.
ITAR / EAR deemed export
Assessed independently of any cloud authorization, based on the nationality and access model of the personnel who can reach the service, not the service's compliance badge.
DFARS 252.204-7012 / NIST SP 800-171
Controls applied consistently across whichever environment handles CUI, with the on-prem option typically simplifying the assessment boundary since it does not introduce a new external system.
CMMC 2.0 Level 2
The AI layer's placement (on-prem versus a FedRAMP-equivalent cloud offering) directly affects the CMMC assessment scope, so the classification decision is made before, not after, the deployment is built.
Where Netray fits
ERPray
Deploys on-prem or in a private/sovereign cloud, which is the deployment model most defense-adjacent manufacturers land on once the ITAR and CUI classification work is done.
Custom build
A documented data classification and routing policy, and the agents that respect it, is usually a bespoke piece of work built around the organization's specific contract flowdowns.
How an engagement runs
Phase 1 . 2-3 weeks
Data classification workshop
- -Field-level classification of ERP data touching CUI or ITAR technical data
- -Written position on ITAR deemed-export risk for any FedRAMP/GCC High option under consideration
- -Draft routing policy for which workloads go to which environment
Phase 2 . 6-8 weeks
Pilot
- -On-prem model deployment for the highest-sensitivity workload identified
- -Audit logging design spanning both environments
- -Security team sign-off on the routing policy in practice
Phase 3 . 4-6 weeks
Production
- -Full rollout of the on-prem deployment for CUI/ITAR-tagged workloads
- -Documented boundary diagram for the CMMC or NIST 800-171 assessment
- -Change control process for adding new workloads to either environment
Phase 4 . Ongoing
Scale
- -Periodic re-review of the classification policy against contract changes
- -Model refresh cadence for the on-prem deployment
- -Annual review of any FedRAMP/GCC High vendor's access model for ITAR fit
Questions to ask any vendor, including us
A short list that separates real SAP S/4HANA AI work from a chatbot demo.
- Does your FedRAMP or GCC High authorization cover the specific service we would use, and at what impact level?
- Where do embeddings and retrieval index data physically reside, and for how long are they retained?
- What is your personnel access model for this service, and can you confirm it in writing for an ITAR deemed-export assessment?
- If we later determine a workload needs to move on-prem, what does that migration actually involve?
- Does the authorization boundary include the ERP connector itself, or only the model-serving component?
- How do you handle a subpoena or government data request for content processed through your service?
- What audit logging do we get natively, and does it meet our NIST 800-171 or CMMC evidentiary needs without added tooling?
- Can we run a side-by-side pilot of the same use case in your cloud service and an on-prem deployment before committing?
Frequently asked questions
Is FedRAMP High authorization enough for ITAR-controlled ERP data?
Not by itself. FedRAMP addresses the cloud service's security control baseline, while ITAR governs the nationality and access rights of the people who can reach controlled technical data. A FedRAMP High authorization does not automatically satisfy ITAR; that requires a separate review of the vendor's personnel and access model.
Is GCC High the same thing as FedRAMP High?
No. GCC High is Microsoft's specific government community cloud offering, built to support ITAR, CJIS, and DoD IL4/IL5 requirements, and it is a different scope than a generic FedRAMP High authorization from another vendor. The two terms get used loosely in vendor marketing, so it is worth confirming exactly which one a given AI service actually holds.
When does on-prem clearly win over a FedRAMP-authorized AI service?
When the workload touches ITAR-controlled technical data, CUI under a contract that restricts processing location, or any data a contract flowdown explicitly requires to stay within the organization's own boundary. In those cases, on-prem removes an ambiguity that a cloud authorization alone does not resolve.
When is a FedRAMP-hosted AI service the more sensible choice?
For genuinely non-sensitive workloads, general question-answering over public catalog data, non-CUI operational dashboards, where the organization would rather consume a managed service than operate GPU infrastructure, and where legal counsel has confirmed no ITAR or CUI data is involved.
Does on-prem cost more than a FedRAMP-authorized service?
Usually yes in upfront and operational terms, since the organization owns GPU procurement, model updates, and support rather than paying a subscription for a managed service. That cost is weighed against the compliance clarity and the avoided cost of a deemed-export finding or a failed CMMC assessment.
Can we split workloads across both environments?
Yes, and most organizations in this situation end up doing exactly that, routing each workload to on-prem or a FedRAMP-authorized service based on a documented data classification, rather than picking one environment for everything.
Who should make the ITAR determination, the AI vendor or our own legal team?
Your own legal or export control team, not the vendor. A vendor has an incentive to describe their own service as compliant; the deemed-export determination depends on facts about your specific data and the vendor's specific personnel access model, which only your counsel can weigh with authority.
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Talk it through with an engineer who knows SAP S/4HANA
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.