On-Premises Deployment
On-premises AI is a deployment system for owning data, model, update, and operating authority inside the organization—not merely an installation location.
Design network boundaries, supply chain, secrets, model import, observability, patching, backup, and handover from a threat model.
01
Problem definition
Even without internet access, image, model, and package import plus administrator authority form a supply chain.
Defining only installation abruptly leaves patching, capacity, incidents, and model updates with the client team.
- Data sovereignty, network isolation, latency, or regulatory boundaries constrain deployment
- The organization accepts long-term responsibility for infrastructure and model lifecycle
- A managed service meets the same control objectives with lower operating burden
- Owners for patches, secrets, backup, and incident response are undefined
On-Premises Deployment: system plate
- Even without internet access, image, model, and package import plus administrator authority form a supply chain.
- On-premises deployment is not inherently secure or compliant.
- Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import.
- Control validation and evidence completeness
- Operational handover readiness
- Certification or regulatory suitability is not guaranteed in advance.
- The workflow begins with Map data, model, control-plane, and user flows plus trust boundaries in a threat model..
- It reaches an acceptance decision through Control validation and evidence completeness.
03
Design method
On-premises AI is a deployment system for owning data, model, update, and operating authority inside the organization—not merely an installation location.
- 01
Stage 1
Map data, model, control-plane, and user flows plus trust boundaries in a threat model.
- 02
Stage 2
Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import.
- 03
Stage 3
Configure secrets, authority, audit logs, and model access with least privilege and separated roles.
- 04
Stage 4
Hand over operations with documentation and training only after performance, failure, recovery, patch, and rollback tests.
04
Application scenarios
Data sovereignty, network isolation, latency, or regulatory boundaries constrain deployment
Even without internet access, image, model, and package import plus administrator authority form a supply chain.
- APPROACH
- Map data, model, control-plane, and user flows plus trust boundaries in a threat model.
- BOUNDARY
- On-premises deployment is not inherently secure or compliant.
The organization accepts long-term responsibility for infrastructure and model lifecycle
Defining only installation abruptly leaves patching, capacity, incidents, and model updates with the client team.
- APPROACH
- Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import.
- BOUNDARY
- Certification or regulatory suitability is not guaranteed in advance.
05
Design choices
| Decision | Gain | Cost | Watch |
|---|---|---|---|
| Data sovereignty, network isolation, latency, or regulatory boundaries constrain deployment | Map data, model, control-plane, and user flows plus trust boundaries in a threat model. | On-premises deployment is not inherently secure or compliant. | Control validation and evidence completeness |
| The organization accepts long-term responsibility for infrastructure and model lifecycle | Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import. | Certification or regulatory suitability is not guaranteed in advance. | Recovery, patch, and rollback time |
On-Premises Deployment: system plate
System 1
Map data, model, control-plane, and user flows plus trust boundaries in a threat model.
System 2
Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import.
System 3
Configure secrets, authority, audit logs, and model access with least privilege and separated roles.
System 4
Hand over operations with documentation and training only after performance, failure, recovery, patch, and rollback tests.
- N1 N2context
- N2 N3decision
- N3 N4evidence
- The workflow begins with Map data, model, control-plane, and user flows plus trust boundaries in a threat model..
- It reaches an acceptance decision through Control validation and evidence completeness.
07
Validation plan
| Measure | Method | Pass condition | Caveat |
|---|---|---|---|
| Control validation and evidence completeness | Map data, model, control-plane, and user flows plus trust boundaries in a threat model. | Repeated runs satisfy the acceptance threshold agreed during discovery | On-premises deployment is not inherently secure or compliant. |
| Recovery, patch, and rollback time | Fix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
| Operational handover readiness | Configure secrets, authority, audit logs, and model access with least privilege and separated roles. | Repeated runs satisfy the acceptance threshold agreed during discovery | — |
08
Constraints and failure conditions
A managed service meets the same control objectives with lower operating burden
On-premises deployment is not inherently secure or compliant.
Owners for patches, secrets, backup, and incident response are undefined
Certification or regulatory suitability is not guaranteed in advance.
09
Engagement model
- 01
Diagnosis
PattyAnalyze the current system and its failure signals.
ClientProvide representative work, data boundaries, and operating constraints.
Control validation and evidence completeness - 02
Design
PattyFix the supply chain with signed artifacts, SBOMs, vulnerability checks, and approved import.
ClientConfirm owners and acceptance criteria.
Recovery, patch, and rollback time - 03
Validation
PattyConfigure secrets, authority, audit logs, and model access with least privilege and separated roles.
ClientMake the production-transition or stop decision.
Operational handover readiness
10
Durable deliverables
- On-Premises Deployment decision record
- On-premises AI is a deployment system for owning data, model, update, and operating authority inside the organization—not merely an installation location.Client-owned · Patty-reviewed
- Validation harness and acceptance criteria
- Control validation and evidence completeness · Recovery, patch, and rollback time · Operational handover readinessJointly maintained
- Operations and recovery runbook
- On-premises deployment is not inherently secure or compliant. · Certification or regulatory suitability is not guaranteed in advance.Operating-team owned
11
Terminology
- On-Premises Deployment
- Design network boundaries, supply chain, secrets, model import, observability, patching, backup, and handover from a threat model.
- Acceptance criterion
- Control validation and evidence completeness
- Operating boundary
- On-premises deployment is not inherently secure or compliant.
REFERENCES
References and primary material
- NIST SP 800-53 Rev.
5
Primary material for the method and terminology.
- SLSA
Primary material for the method and terminology.