Top 10 AI Prompts and Use Cases and in the Government Industry in South Korea

South Korea's AI Basic/Framework Act turns abstract tech debates into immediate public‑sector priorities: hailed as the first APAC omnibus AI law, it adopts a risk‑based approach that tightens oversight on “high‑impact” systems (health, energy, public services), mandates labeling for generative AI, and has extraterritorial reach - yet pairs rules with active government support for AI data centers and SME‑friendly infrastructure to keep innovation moving.

Agencies face a one‑year transition before the law takes effect in January 2026, so practical steps - impact assessments, human‑in‑the‑loop safeguards, and procurement alignment - matter now; see the detailed analysis from the Future of Privacy Forum analysis of South Korea AI Framework Act and the official MSIT official announcement on the AI Framework Act.

For public servants and teams that must turn compliance into capability, focused upskilling - like Nucamp's Nucamp AI Essentials for Work bootcamp - bridges policy to hands‑on prompts, risk management, and practical AI use in government workflows.

Bootcamp	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15 Weeks)

"We consider the passage of the Basic Act on Artificial Intelligence in the National Assembly to be highly significant as it will lay the foundation for strengthening the country's AI competitiveness."

Selection prioritized prompts and use cases that map directly to South Korea's risk‑based AI framework: systems touching “high‑impact” sectors (health, energy, public services, biometric screening and public decision‑making) were weighted heavily, as were generative‑AI scenarios that trigger mandatory labeling and transparency duties; projects that benefit from MSIT's push for AI data centers, training‑data programs, and SME support were scored higher because the law pairs oversight with active industrial support.

Practical filters included extraterritorial exposure (does the use affect Korean users?), the likelihood of requiring an impact assessment or domestic representative, and whether lifecycle risk management, explainability, and human‑in‑the‑loop controls are feasible within the one‑year transition to Jan 22, 2026.

The methodology leaned on official guidance and expert summaries - see the MSIT AI Basic Act announcement and the Future of Privacy Forum risk-based analysis - to ensure each prompt is both compliant and immediately useful in Korean public‑sector contexts.

Selection Criterion	Why it matters in KR
High‑impact sector fit	Triggers stricter safety, oversight, and impact assessments
Generative AI / labeling risk	Requires user notice and content labeling under the Act
Extraterritorial impact	Applies to services affecting Korean users, even if foreign
Infrastructure & SME support	More deployable where MSIT backing and data centers exist

"We consider the passage of the Basic Act on Artificial Intelligence in the National Assembly to be highly significant as it will lay the foundation for strengthening the country's AI competitiveness."

South Korea is turning AI into a practical lifeline for flood and wildfire response: the Ministry of Environment's LSTM‑powered flood forecasting system now runs at roughly 223 monitoring locations, learning from rainfall, water level and soil‑moisture feeds to predict river heights at 10‑minute intervals and push immediate alerts via SMS and Cell Broadcast Service - while the government accelerates pilots of smart CCTV and IoT slope sensors to speed detection ahead of the rainy season.

The World Meteorological Organization's MoU with the Republic of Korea formalizes collaboration on AI and Digital Twin technologies to deliver richer, high‑resolution simulations and a digital twin platform expected in 2026, and Korea's KOICA projects have already tested these models abroad to validate upstream gauge forecasts.

The practical payoff is simple: more lead time for evacuations, navigation‑aware advisories, and targeted inspections of fire‑damaged slopes, turning river and slope data into actionable warnings instead of last‑minute scrambling.

Workflow Step	Detail
Data inputs	Real‑time rainfall, water levels, soil moisture
Model	LSTM combined with hydrological/hydraulic methods
Forecast cadence	Predictions at 10‑minute intervals
Alerting	SMS to local governments; CBS to the public
Integration	Navigation services for near‑real‑time risk maps
Next step	Digital twin with high‑res 3D simulation operational in 2026

Border control and identity verification in South Korea are rapidly moving from token checkpoints to data‑rich, AI‑driven workflows - and regulators are racing to catch up.

The Personal Information Protection Commission (PIPC) is drafting a formal biometric framework to clarify when facial recognition, iris scans, palm‑vein checks and other identifiers can be used, a response to growing deployments from airport palm‑scan boarding to private venues; one high‑profile example of operational scale is INSPIRE Resort's choice of Regula's document reader to speed passport onboarding and AML checks.

Lawmakers have also proposed tougher PIPA amendments that would explicitly expand protected biometric categories and tighten consent and purpose limits, while broader PIPA guidance highlights strict transparency, minimisation and DPO requirements for any agency handling biometric data.

At the same time, cross‑border tech projects such as the U.S.–Korea International Remote Baggage Screening pilots show how identity and screening systems are becoming entangled in international flows - a single misstep could turn a fast, seamless arrival into a privacy and compliance headache.

For public servants designing these systems, the practical challenge is clear: balance operational gains with legally grounded consent, narrow purpose clauses, and robust impact assessments so biometric speed doesn't outpace citizens' rights; see reporting from BiometricUpdate: Korean regulators plan formal biometrics framework, the IDTechWire analysis of proposed PIPA biometric data protection amendments, and a comprehensive PrivacyEngine guide to South Korea's Personal Information Protection Act (PIPA).

"We aim to strike a balance between protection and regulation."

Gyeonggi Province is piloting an AI

social companion

that turns cold welfare checklists into warm, conversational outreach: an LLM‑powered agent (developed with Naver) places weekly, up to three‑minute calls to residents aged 65+, retrying unanswered numbers up to three times before human welfare staff step in - a practical safety net that identified about 15 clear distress cases and helped roughly 800 seniors in the program's first two months, out of some 2,050,000 eligible seniors in the province; see the Ministry of Culture, Sports and Tourism's coverage of Gyeonggi's AI chat services for seniors and the Korea Bizwire report on the

Elderly Malbut

rollout for enrollment and operational details, while local sign‑up windows and municipality logistics are described on Gyeonggi Province's information page.

The design balances companionship and escalation: emotional check‑ins can trigger telephone counselling, referrals to the emergency welfare hotline, or in‑person visits - a vivid reminder that a three‑minute AI call can be the difference between loneliness left unseen and timely help delivered.

Feature	Detail
Target group	Residents aged 65 and older (Gyeonggi‑do)
Launch date	June 19, 2023
Call cadence / length	Weekly calls, up to 3 minutes each
Retry policy	Up to 3 additional call attempts
Early impact	~800 beneficiaries in 2 months; 15 distress cases identified
Scale	~2,050,000 seniors in the province
Partner	Naver (AI agent development)

Turning paper archives into AI‑readable gold starts with disciplined scanning, OCR and metadata: professional government document scanning services can convert thousands of pages into searchable, indexed files that feed downstream automation, reduce storage costs and unlock personalized citizen workflows - think instant retrieval of a full case file instead of digging through dusty boxes.

Best practices combine high‑quality image capture and quality assurance with accessibility and legal compliance (Section 508 style conversion for people with disabilities) so outputs are both machine‑usable and human‑friendly; see a practical primer on government document scanning services primer and vendor approaches to accessibility like Section 508‑compliant conversion guidance.

Once documents are structured and tagged, AI platforms can auto‑generate citizen instructions, fill multilingual forms, and populate templates at scale - a workflow outlined in enterprise solutions for public agencies that automate content assembly and translation to speed service delivery and cut repetitive work, while keeping certified translations and records auditable.

“Please submit certified translations for all foreign language documents. The translator must certify that s/he is competent to translate and that the translation is accurate.”

AI-assisted e‑litigation can turn backlog and manual follow‑ups into a smoother, faster courtroom experience: platforms that automate filings, docketing and post‑settlement workflows - like CrimsonLogic's eLitigation solution for end‑to‑end case management - pair naturally with digital settlement tools that move payments and paperwork online; Milestone's Pathway, for example, shrinks months‑long paper check distributions to as little as 10 business days while offering secure payment choices and real‑time dashboards for administrators, all features that South Korean courts and tribunals could adapt to shorten time‑to‑resolution, reduce paper trails, and improve transparency for litigants and unbanked claimants alike.

Practical priorities for public sector pilots include reliable e‑signing, payment reconciliation, and auditable records that satisfy PIPA and court evidentiary rules; agencies exploring modernization will find concrete design patterns in both case‑management and settlement distribution platforms.

Learn more from the CrimsonLogic eLitigation case study and Milestone's Pathway platform.

“Pathway makes my life easier by handling the entire settlement distribution process. We've gotten great client feedback too; clients can easily navigate Pathway and manage signing final paperwork and arranging payments all on their own, they don't even need to call me to ask questions!” - Adrian, Paralegal

Accuracy in public‑sector procurement forecasting can be the difference between ready forces and wasted budgets, and South Korean research shows practical AI and time‑series tools already closing that gap: studies using the KF‑16C fighter's maintenance logs applied SARIMA to seasonal items and introduced Majority‑Voting and Hybrid methods for low‑consumption, irregular parts to raise forecast accuracy (KF‑16C spare parts demand forecast study (KoreaScience)), while a navy study analysed 18,476 component records with data‑mining (regression trees, random forest, neural nets) and found random forest delivered the best MSE for ship spare parts prediction (Naval vessel spare parts data‑mining study (KoreaScience)).

For procurement teams and inventory managers, the takeaway is concrete: blend proven statistical models for seasonal lines with hybrid or ensemble methods for intermittent demand, and link forecasts to automated reorder rules so scarce defence or medical items are where they're needed without bloating stock - see practical deployment considerations in the Nucamp guide to using AI in government workflows (Nucamp AI Essentials for Work bootcamp syllabus and guide to using AI in government workflows).

Study	Data / Size	Methods	Key result
KF‑16C spare parts (J. KIMS Technol.)	KF‑16C consumption data	SARIMA; Majority Voting; Hybrid	Improved forecast accuracy for low‑consumption, unclear patterns
Naval vessel spare parts	18,476 component records	Regression tree; RandomForest; Neural net; Linear regression	RandomForest achieved best MSE; data‑mining improved accuracy vs. traditional time series

AI is reshaping public‑health tools in South Korea - from automated triage and continuous device telemetry to early outbreak detection - but those gains arrive inside a tight, newly clarified regulatory frame: the Digital Medical Products Act now sets the national baseline for AI, DTx and health software, while the MFDS will be the principal regulator for market authorization and clinical use (see the Digital Medical Products Act text at the KLRI).

Practical requirements reported in the 2025 ICLG Korea chapter spell out what that means for triage models and telemetry platforms: clinical validation or real‑world evidence, cybersecurity and detailed verification & validation documentation, plus classification into four risk classes and linkage to national reimbursement pathways.

Device telemetry and cloud‑based monitoring must also navigate strict patient‑data rules under the Medical Service Act and PIPA, and federated or pseudonymised approaches are often the only viable research route.

The policy bottom line is simple and vivid: when an AI‑powered triage or imaging assistant is wrong, the harm can be clinical as well as legal, so developers and agencies should pair ambitious pilots with MFDS‑grade validation, privacy guards and clear pathways to reimbursement and scale.

Item	Detail
Principal regulator	Ministry of Food and Drug Safety (MFDS)
Key statute	Digital Medical Products Act (framework for AI/DTx)
Enforcement date / milestone	Market authorisation under the Act and MFDS oversight (see ICLG 2025 Korea report)
SaMD approvals (2020–2023)	376 approved products (MFDS data reported in ICLG)

Transparency and accountability are now operational requirements in South Korea's AI era: the amended PIPA and PIPC guidance give data subjects rights to concise, meaningful explanations and to request reviews of decisions made by

fully automated

systems, and when an automated choice significantly affects rights or obligations the individual can refuse it and trigger human intervention - controllers must disclose the decision‑making criteria and processing procedures (posted publicly) and establish easy, accessible procedures for exercising these rights, with measures generally required within 30 days (extendable by 60).

Regulators have also tightened governance: qualified, independent privacy officers are mandated for large processors, liability‑covering insurance is expanded to more data controllers, and the PIPC expects proactive disclosure, impact assessment and lifecycle risk controls for AI - measures that dovetail with the forthcoming AI Framework Act.

Public servants designing automated decisions should treat explainability as a legal and operational control (not a checkbox): publish the criteria, build clear objection paths, and ensure human‑in‑the‑loop workflows so one contested algorithmic outcome can be paused and rechecked.

See PIPC guidance for details and practical steps in the updated enforcement regime via the PIPC official guidance on automated decisions and legal summaries on automated‑decision rights from Chambers legal summaries on automated-decision rights and Ius Laboris legal summaries on automated-decision rights.

Right / Requirement	Key point
Explanation & review	Concise, meaningful explanation on request; procedures comparable to access requests
Refusal / human intervention	Data subjects can refuse impactful automated decisions; controllers must reprocess with human review unless justified
Timelines	Measures generally within 30 days, extendable up to 60 days
CPO & liability	Stricter CPO qualifications and independence; insurance/liability expanded to more data controllers

South Korea's public‑communications playbook must now fold in fast, pragmatic responses to synthetic media: the Korea Communications Commission's Guidelines on the Protection of Users of Generative AI Services (published Feb 28, 2025) signals that regulators expect platforms and publishers to manage generative‑AI harms, but real‑world detection remains fragile - journalists and platforms should heed reporting that many deepfake detectors “cannot be trusted to reliably catch AI‑generated or ‑manipulated content” and that results are often ambiguous and easily evaded.

International incidents underscore the stakes - an executive‑impersonation deepfake helped defraud Arup of $25.5 million - and illustrate why South Korean agencies and broadcasters should combine labeling and disclosure rules with verified takedown workflows, secondary authentication channels for official messaging, and layered resilience (behavioral checks, MFA and incident playbooks).

In short: KCC guidance sets the expectation for user protection and labeling, but preserving public trust will require operational safeguards, realistic vetting procedures, and cross‑sector detection + verification protocols rather than blind reliance on any single detector - aligning domestic rules with emerging global practice while keeping citizen safety front and center; see the Korea Communications Commission generative AI guidance (Feb 28, 2025) - Kim & Chang and practical detection cautions from the Tow Center / Columbia Journalism Review deepfake detection guide.

“We really do have to start questioning what we see.”

Policy teams drafting regulations and running scenario planning for South Korea's public sector should treat the AI Framework Act as both a compliance checklist and a playbook for practical rollout: the Act's risk‑based split between “high‑impact” and generative AI means ministries must map which services trigger Article 31–34 rules, designate domestic representatives where thresholds apply, and build lifecycle risk‑management into procurement and pilot timelines during the one‑year transition to 22 January 2026; MSIT will be the principal investigator and enforcer, but alignment with the PIPC on personal‑data touchpoints is a live coordination risk, so joint testbeds and staged impact assessments are essential.

Scenario planners should model audits, on‑site inspections and modest administrative fines (up to KRW 30 million) alongside reputational and service‑continuity impacts, and take advantage of the Act's support measures - AI data centres, training‑data projects and standardisation - to pair safety controls with capacity building.

For concise legal analysis of the Act's obligations and timelines see the Future of Privacy Forum summary and a practical breakdown from Araki Law, which both unpack Article 31's transparency duties and the Act's innovation‑support measures.

Item	Detail
Effective date / transition	One‑year transition; enforcement from 22 Jan 2026
Principal regulator	Ministry of Science and ICT (MSIT)
Key obligations	Transparency for generative AI; risk management for high‑impact AI; domestic representative thresholds
Penalties	Administrative fines up to KRW 30 million
Support measures	Government backing for AI data centres and training‑data projects

"Article 31(1): Notify users in advance that the product/service utilizes AI (for high‑impact or generative AI)."

Beginners should treat South Korea's AI Framework Act as both a checklist and a launchpad: use the one‑year transition to 22 January 2026 to map which services may be “high‑impact,” run focused impact assessments, and align procurement and human‑in‑the‑loop plans so legal duties (transparency, labeling and lifecycle risk controls) are baked in from day one - see the FPF analysis of South Korea's AI Framework Act for a concise run‑down.

At the same time, follow PIPC guidance on legally safe data practices (including the new publicly available data rules) to avoid personal‑data pitfalls and inspections: the PIPC guidelines for data protection and AI use in South Korea lay out legitimate‑interests tests, pseudonymization and technical safeguards.

Practical skills matter: non‑technical staff can gain usable prompt‑writing and workflow skills in Nucamp AI Essentials for Work bootcamp (AI skills for the workplace), which pairs policy awareness with hands‑on prompts and automation patterns - one vivid payoff: a simple, well‑documented impact assessment can be the difference between a smooth procurement win and an MSIT inspection.

Start with narrow pilots, document decisions for explainability and human review, and scale only after audits and privacy controls are proven.

Immediate step	Why it matters
Map high‑impact services	Triggers Article 34 obligations for risk management and human oversight
Run impact assessments	Incentivised for procurement and clarifies rights/risks under the Act
Follow PIPC data rules	Ensures lawful use of training data and fewer privacy inspections
Upskill on prompts & workflows	Practical skills reduce compliance gaps and speed safe pilots

“it is part of our endeavors to meet halfway between protecting personal data and encouraging AI-driven innovation. This will be a great guidance material for the development and usage of trustworthy AI.”