Trust Evidence

Every proof here has a source. See for yourself.

Cases, certifications, patents, and papers, gathered in one place. Each certification names its issuing body, each patent shows its registration or application number, and each paper names its venue. Open any of them yourself.

0.12s Per-page processing 2,200-character page · excluding answer generation · self‑tested
0.94 PII detection F1 automated PII detection · self‑tested
Blocked → Enabled External LLM use on data previously off-limits
Case Records

Operational case records

Blocked workflows, unblocked.
What changed, and how to reproduce it for audit.

01 Insurance LLM-assisted claims processing: sensitive-data substitution
Before ExposedSensitive data in prompts

Claims documents containing policyholder names, ID numbers, and medical details were sent directly to an external LLM API. The compliance team blocked the workflow.

After SubstitutedSensitive fields PreservedOutput usability

LLM Capsule substituted sensitive fields with restorable stand-ins before submission. Outputs returned and were restored locally for downstream system use.

Trust boundary: raw policyholder & medical data stays inside
What changed

The LLM Capsule layer was inserted into the workflow. Substitution covered names, IDs, dates, and medical field patterns. Sensitive raw values stayed in the local restore mapping.

How it works
  1. 1 Detection names, IDs, dates, medical field patterns
  2. 2 Substitution restorable stand-ins before submission
  3. 3 Model request only substituted values reach the LLM
  4. 4 Restore outputs restored locally for downstream systems
Reproduce

Each substitution run is recorded in the audit log and usage records, so the run can be traced for audit.

Evidence
Substitution Log Restore Mapping Record Audit Log Usage Record
Before BlockedLLM on internal docs (before)

Internal documents (contracts, HR records, customer files holding personal and confidential information) could not be sent to ChatGPT, Claude, or Gemini for summarization or search. The security team blocked it.

After EnabledSensitive LLM use (after) LocalPII detect & restore

LLM Capsule detects and substitutes personal and confidential fields locally before the request reaches an external LLM, then restores the original values in the response, so staff can summarize and query internal documents without sending raw data to the external LLM.

Trust boundary: raw internal documents stay inside; only substituted values reach the external LLM vendor
What changed

The LLM Capsule layer was placed between internal tools and the external LLM. Detection covered names, contacts, IDs, and account/contract identifiers; raw values stayed in the local restore mapping.

How it works
  1. 1 Detection names, contacts, IDs, account/contract identifiers
  2. 2 Substitution restorable stand-ins before the request leaves
  3. 3 Layer only the substituted request reaches ChatGPT / Claude / Gemini
  4. 4 Restore original values rehydrated locally in the response
Reproduce

Each request's substitution is recorded in the audit log and usage records, so a given document-Q&A run can be traced for audit.

Evidence
Substitution Log Restore Mapping Record Audit Log
Before RestrictedRaw OT data (before)

OT/ICS network data carried sensitive operational details, so it could not be sent to an external AI for automated threat analysis.

After EnabledAI threat analysis (after) Structure-preservingSubstitution

Structure-preserving substitution lets an AI agent analyze the network data and answer threat questions. Sensitive values are replaced with stand-ins while relationships are preserved. (Integrated with a global OT security platform's detection solution.)

Trust boundary: sensitive operational values stay inside; only substituted values leave
What changed

Network-data sensitive fields are substituted while topology and relationships are kept intact, so the agent can reason over realistic context.

How it works
  1. 1 Restricted input OT/ICS network data with operational detail
  2. 2 Substitution structure-preserving (topology kept intact)
  3. 3 Analysis AI agent reasons over realistic, substituted context
  4. 4 Integration works with a global OT security detection platform
Reproduce

The substituted dataset and the agent's analysis are recorded in the audit log, so the run can be traced.

Evidence
Substituted Dataset Agent Analysis Log Structure Map
Before ManualRelease screening (before)

Users exporting sensitive aggregate statistics required manual, per-desk substitution and release review, which was inconsistent and hard to audit.

After AutomatedScreening (after) TracedRelease audit trail

An automated substitution module plus a multi-agent pipeline detects, traces, and substitutes personal information in aggregate data, automating and standardizing the release-review process.

Trust boundary: personal information is substituted and traced before any release
What changed

Each substitution is recorded in the audit log, so which records were transformed, and how, is traceable for audit.

How it works
  1. 1 Before manual, per-desk substitution (hard to audit)
  2. 2 Detection PII detection across aggregate data
  3. 3 Pipeline detect, trace, substitute, standardize
  4. 4 Audit each substitution recorded in the audit log
Reproduce

For regulatory inspection, the audit log and usage records of a prior release show how it was screened.

Evidence
Substitution Report Release Audit Log Detection Trace Usage Record
Certifications · Patents · Research

Independent validation

Certifications, patents, and peer-reviewed research. Each checkable at the source.

Certification
ISO 27001: Information Security Management
ISO/IEC 27001:2022 · Certified by Korea Foundation for Quality (KFQ) · 2026

International standard for information security management. Demonstrates a systematic approach to managing sensitive information.

Certification
ISO 42001: AI Management System
ISO/IEC 42001:2023 · Certified by Korea Foundation for Quality (KFQ) · 2026

International standard for AI management systems. Demonstrates responsible AI governance and risk management.

Certification · GS Grade 1
GS Certification (Grade 1): LLM Capsule
Korean SW Quality Certification (Good Software), Grade 1 · LLM Capsule v1.0 (2024) · v2.0 (2026)

Top grade (Grade 1) of the Korean software quality certification (Good Software), awarded to LLM Capsule v1.0 in 2024 and to v2.0 in 2026.

Certification
KISA Fast Track (2024)
Korea Internet & Security Agency (KISA) · 2024

LLM Capsule received the KISA information security system Fast Track confirmation (2024).

Certification
AI Product & Service Verification (2026)
Telecommunications Technology Association (TTA) · 2026

LLM Capsule received AI Product & Service Verification from the Telecommunications Technology Association (TTA) in 2026.

View full details in the Trust Center
Patent · KR + US Registered
AI-Based Service Providing Method Without Leaking Private Information and Client Apparatus
KR Reg. No. 10-2757651 (App. 10-2023-0133086, Registered 2025-01-16) · US Pat. No. 12,737,496 B2 (App. 18/908,054, Filed 2024-10-07, Registered 2026-09-15)

Core LLM Capsule patent. Method and client apparatus for AI services without exposing private information. Registered in Korea (2025-01) and the United States (2026-09).

Patent · Pending · KR + US
Method for Providing Security for On-Device Artificial Intelligence Models
KR App. No. 10-2025-0003223 (Filed 2025-01-09) / 10-2026-0000037 (priority, Filed 2026-01-02) · US App. (Ref. PO25-025-US)

Security provisioning method for AI models running on-device, supporting the on-prem / air-gapped execution path. Korean priority applications with a corresponding US filing.

Patent · Pending · KR · Expedited
Data Management Method and System for AI Execution Control
KR App. No. 10-2026-0053050 · Filed 2026-03-24 · Expedited examination granted 2026-04-08

Method and system for controlling and managing data state within AI execution environments. Expedited examination granted.

Patent · Pending · KR
Method and Inference Apparatus for Building Deep Learning Models Robust to Private Information Exposure
KR App. No. 10-2023-0074745 · Filed 2023-06-12

Deep-learning model construction robust to private-information exposure. Applicant: Ewha Womans University (co-research).

Patent · Pending · KR
Method and Analysis Apparatus for Building AI Models that Process Heterogeneous Datasets
KR App. No. 10-2023-0013029 · Filed 2023-01-31 · Under examination

AI model construction method for heterogeneous datasets. Applicant: Ewha Womans University (co-research).

Substitution & structure preservation
IEEE BIBM 2023
Privacy-Preserving Publishing of Individual-Level Medical Data for Cloud Services
Ho Bae, Heonseok Ha, Siwon Kim · IEEE BIBM · Istanbul, Dec 2023

Formalizes how individual-level medical records can be released to cloud services under strict privacy constraints while staying useful. It frames the exact problem LLM Capsule addresses: letting an organization use sensitive data with an external service without handing over the raw records.

IEEE/ACM TCBB 2022
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks
Ho Bae, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon · IEEE/ACM Transactions on Computational Biology and Bioinformatics · 2022

A deep-learning approach to security and privacy on genomic sequences, among the most tightly regulated data classes there is. It shows how far the founding team's privacy research reaches, well beyond the document and tabular data an AI data layer handles day to day.

PSB 2020
AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data
Ho Bae, Dahuin Jung, Hyun-Soo Choi, Sungroh Yoon · Pacific Symposium on Biocomputing · 2020

Anonymizes sensitive medical records with a GAN while preserving the statistical structure downstream models rely on. LLM Capsule works from the same structure-preserving principle: it substitutes fields before they reach an external LLM and restores them afterward, so the data stays usable without being exposed.

Membership & inference-attack defense
BMVC 2022
MPGAN: Membership Privacy-Preserving GAN
Heonseok Ha, Uiwon Hwang, Jaehee Jang, Ho Bae, Sungroh Yoon · BMVC · London, Nov 2022

Trains generative models that resist membership-inference attacks, so an adversary can't tell whether a given record was in the training data. It reflects the founding team's long-running research on keeping sensitive records out of what external models see.

ACM AsiaCCS 2022
Membership Feature Disentanglement Network
Heonseok Ha, J. Jang, Y. Jeong, S. Yoon · ACM Asia Conference on Computer and Communications Security · 2022

Disentangles membership-revealing features from model representations, lowering exposure to inference attacks. It addresses the inference-attack questions enterprise buyers raise before sensitive data reaches a model.

Distributed-learning security & foundations
ESORICS 2024
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
Yungi Cho, Woorim Han, Miseon Yu, Younghan Lee, Ho Bae, Yunheung Paek · ESORICS · 2024

A backdoor defense designed for Vertical Federated Learning. It identifies and purifies backdoor-triggered embeddings at inference.

ESORICS 2023
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
Younghan Lee, Yungi Cho, Woorim Han, Ho Bae, Yunheung Paek · ESORICS · 2023

Byzantine-robust federated learning that detects malicious clients via an ensemble of contrastive models, strong under non-IID data.

JMLR 2025
Regularizing Hard Examples Improves Adversarial Robustness
Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon · Journal of Machine Learning Research · 2025

Adversarial robustness method that regularizes hard examples to improve robust generalization.

Sensors 2024
Evaluation of Malware Classification Models for Heterogeneous Data
Ho Bae · Sensors (MDPI) · 2024

Study of malware-classifier explainability on heterogeneous data. Existing explanations fall short, and high accuracy can give a misleading sense of security.

Selected publications by the CUBIG founding team and collaborators. Full publication list and citation counts available on request.

Awards · Partners · Press

Market recognition

Awards, partnerships, and press, with dates and links.

Industry Award
Deutsche Telekom T-Challenge 2026: 2nd Place
T-Mobile / Deutsche Telekom · 2026

Placed 2nd in the global open-innovation program with substitution and local restoration technology.

Government Award
Information Security Product Innovation Award: Minister of Science and ICT Prize
Ministry of Science and ICT · 2024-11-26

Minister of Science and ICT Prize for LLM Capsule at the Information Security Product Innovation Awards (first half, 2024).

Recognition
2026 Emerging AI+X Top 100
Korea AI Industry Association · 2026

Named to the Emerging AI+X Top 100 by the Korea AI Industry Association (AIIA), January 2026.

Government Program
Selected Supplier: 2026 AI Voucher Program
Ministry of Science and ICT · NIPA · 2026

Selected as a supplier for the 2026 AI (Cloud) Voucher program, letting institutions adopt CUBIG solutions, including LLM Capsule, via government vouchers.

Public Procurement
Innovative Product Designation: LLM Capsule
Public Procurement Service · 2025

Designated an Innovative Product by the Public Procurement Service in May 2025, so LLM Capsule is listed on the Innovation Procurement Marketplace for public-sector buyers.

Government Program
Ultra-Gap Startup 1000+
Ministry of SMEs and Startups · 2025

Selected as a deep-tech startup (AI / big data) in the Ultra-Gap Startup 1000+ project.

Government Program
Global ICT Future Unicorn
Ministry of Science and ICT · NIPA · 2025

Selected by the Ministry of Science and ICT as a Global ICT Future Unicorn company in May 2025.

Startup Recognition
SKT × Hana Bank AI Accelerator
2024

Selected for the joint SKT and Hana Bank AI startup accelerator.

Recognition
Startup World Cup Finalist
Startup World Cup · 2025

Reached the finals of the Startup World Cup global startup competition in October 2025.

Recognition
NextRise Award Global Innovator
NextRise · 2024

Named a Global Innovator at the NextRise Award in June 2024.

Marketplace
AWS Marketplace
LLM Capsule listed · procurement-ready

Available for direct purchase and private-offer deployment through AWS, simplifying enterprise procurement.

Press Coverage
CUBIG Validates LLM Capsule's Technical Efficacy Across Global Industrial Sites ↗
Korea Economic Daily Business · April 22, 2026

Coverage of LLM Capsule's context-preserving data layer architecture being validated in production alongside global industrial cybersecurity firm Claroty, with original data staying inside the customer environment.

Press Coverage
CUBIG Named a Representative Vendor in Gartner® Hyper-Synthetic Data Report ↗
Electronic Times (etnews) · July 30, 2025

Coverage of Gartner® Emerging Tech: Provider Differentiation Strategy-Trends for Hyper-Synthetic Data (2025), in which CUBIG is named a Representative Vendor.

Press Coverage
LLM Capsule Emerges as Essential Tool for Safe Enterprise AI Adoption ↗
Korea Economic Daily · April 1, 2025

Coverage of LLM Capsule as enterprises and public institutions look for a way to use ChatGPT, Claude and Gemini on sensitive operational data.

Interview
[VivaTech 2025] CUBIG: "Designing the Future of AI Data Security" ↗
NewsPim · June 4, 2025

CEO Bae Ho interviewed at VivaTech 2025 in Paris on CUBIG's AI data technology.

Press Coverage
LLM Capsule Selected for South Korea's Government Tech Pilot Purchase Program ↗
TechWorld News · June 2, 2025

LLM Capsule was selected as a pilot purchase product by the Korea SMEs and Startups Agency for Distribution in May 2025.

Press Coverage
CUBIG to Unveil Next-Generation Synthetic Data Technology at World IT Show 2025 ↗
AVING News · April 22, 2025

CUBIG showcased LLM Capsule at WIS 2025 at COEX Seoul.

FAQ

Common questions

A context-preserving data layer for AI lets AI work on operational data that cannot be sent in its original form. Sensitive terms are substituted while the structure, context and relationships the task needs are kept, and LLM Capsule restores the original names, codes and values in your workflow.
Operational evidence is concrete, verifiable documentation that an AI system behaves as claimed in real production conditions: what was blocked before, what changed after deployment, the deployment reference, and the certifications, patents, and peer-reviewed research that back it. It is the difference between a marketing claim and a record a procurement or audit team can verify.
Through third-party certifications (ISO/IEC 27001, ISO/IEC 42001, KISA Information Security System Fast Track, GS Certification, TTA AI Product & Service Verification), a registered patent on AI services without exposing private information (KR and US) plus pending applications including on-device AI model security, peer-reviewed publications by the founding team, and deployment references across regulated industries.
Certifications show an independent body has audited the security and AI-management practices; patents show the method is novel and documented; peer-reviewed research shows the underlying techniques withstand academic scrutiny. Together they let a buyer rely on more than the vendor's own word.
In the substitution workflows on this page, the external AI receives substituted values, not the original sensitive values. Substitution happens inside your environment and keeps the structure the task needs, and outputs are restored locally through your internal mapping.
The certifications, patents, research papers, and press items on this page each cite a primary source you can check independently, without taking our word for it. Each certification names its issuing body, with full details in the Trust Center; the patents list their KIPO and USPTO numbers, lookupable in the national registries or Google Patents; the papers cite venue and authors traceable through DBLP; and every press item links to its original article.
Credible proof is evidence a third party can verify without relying on the vendor's word, not a testimonial or a marketing line. For an AI data layer that means several signals together: operational records of what was blocked and what changed, independent certifications, registered patents in a public registry, and peer-reviewed publications by the founding team. Any one alone is weak; together they let a buyer rely on more than a promise.

Now see it on your own data.

One workflow blocked by data exposure is enough to start. We will show the same evidence on your data, your systems, and your constraints.

See the Architecture Try it on your data