From the Local Government edition of October 25, 2026
Jeonbuk's in-house staff AI reaches 9,300 accounts and 440,000 questions on a 300 million won budget, with DLP routing sensitive prompts inside the network
Jeonbuk State Government and Daegu Suseong-gu (presenting at the Korea Local Information Research and Development Institute forum) · Regional and local government internal staff AI · Jeonbuk State and Daegu Suseong-gu, South Korea
- Publisher
- Digital Daily (디지털데일리)
- Original publication
- 22 October 2026, 16:37 KST (Digital Daily)
- Source retrieved
- 2026-10-26
- Event date
- 2026-10-22
What happened
Jeonbuk State built an in-house staff AI for 300 million won rather than accept a 5 billion won private proposal or pay an estimated 2.57 billion won a year for commercial accounts. Hosted inside the administrative network on AMD MI300X servers after a national security review, it has 9,300 accounts, 440,000 questions and 99 features. External models are reached by API, with DLP rerouting prompts containing personal or sensitive data to the internal model. Staff-built tools include a treasury deposit optimiser projected to add about 1.6 billion won a year. Expansion to 14 cities and counties is planned by year end.
Why it matters
Why now: presented 22 October and not previously archived. A detailed cost, hosting and data-control comparison for government-run staff AI, relevant to U.S. counties weighing commercial copilots against hosted models; Korean security review and procurement rules limit transferability.
Evidence and measured results
Presenters' self-reported usage, cost and design figures; projections for interest gains; no evaluation report.
Limitations and uncertainty
Self-reported by the builders at a conference; no measured productivity or accuracy data; projected interest gain; Korea-specific security and budget context.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-25; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Staff are already using free external AI with government data, and commercial licences for all staff are costly.
- Stakeholders
- CIO or administrative information division
- Security office
- Finance and treasury
- Staff innovation groups
- Partner university
- Discovery questions
- How many staff use unapproved AI tools now?
- What would full commercial licensing cost per year?
- Where must prompts and data reside?
- Who would operate a hosted model?
- Value hypothesis
- Bounded: a hosted staff AI with DLP may lower licence cost and keep data in-network, but no measured productivity exists in this source.
- Potential engagement
- A total-cost and data-residency options study comparing commercial copilots with a hosted model and DLP gateway.
- Unsupported claims
- Measured time savings
- Realised 1.6 billion won interest gain
- Lower cost in a U.S. context
- Accuracy of outputs
Pre-sales engineering
Role takeaway
- Fit
- Regional or county governments with security requirements that rule out sending data to external AI services.
- Architecture and integration
- On-network GPU inference, API connections to external models, DLP routing, document generation and meeting transcription features.
- Prerequisites
- Security review
- GPU hardware
- Open-model licensing review
- Monitoring automation
- Constraints
- Korean security review regime
- Model provenance scrutiny for open models
- Single-operator staffing
- Security
- Keep internal prompts on the government network and enforce DLP before any external model call.
- Proof of value
- Measure DLP detection on a test set of sensitive prompts
- Benchmark task accuracy on common document types
- Load-test for the planned 15,000 users
Delivery
Role takeaway
- Work
- Run and scale a hosted staff AI platform and a staff-built tool program.
- Dependencies
- Hardware budget
- University partnership for staff
- Security approvals for each new tool
- Ownership
- Jeonbuk administrative information division (one officer also covering cloud work).
- Skills and adoption
- Monthly no-code AI building sessions and two-day training for 1,800 staff.
- Governance checkpoints
- Security review before each new external model
- Review of staff-built tools before citizen use
- Pre-expansion review before municipal rollout
- Acceptance criteria
- Proposed: measured task-time change on a sample of document workflows
- Proposed: DLP false-negative rate below an agreed threshold
- Proposed: second trained operator in place before expansion
- Risks
- Key-person dependency
- Unverified output quality
- Model supply-chain concerns
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
On-premises inference inside the government network with API access to external models and a DLP gateway; models swapped from DeepSeek V3.1 to GLM-family without vendor involvement.
Governance
Who approves, reviews and stays accountable for outcomes?
National Intelligence Service security review preceded launch; staff-built tools are encouraged through a research club, with a proposal to separate field development from expert security review.
Security and privacy
What data, permissions and controls need testing?
DLP detects sensitive or personal data and redirects prompts away from external AI; internal questions stay on the government network.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Monthly 'vibe coding' training and two-day courses for 1,800 staff; one part-time administrator runs the platform with heavy automation.
Procurement
What should contracts, pricing and exit terms secure?
In-house build cost 300 million won versus a 5 billion won proposal; a Rubin-class server is in next year's budget; plan to open-source the system.
Operating model
Which teams own the service once it runs?
Single-operator platform relies on 32 dashboards and 200+ nightly automated checks; key-person dependency is a risk.
Publication history
- 2026-10-25Local Government · Issue 507 resources
Stable resource ID: jeonbuk-kr-sovereign-staff-ai-9300-users-dlp-20261022