Proof · Trust Evidence

Proof,
not promises.

Data that is usable for AI execution, works without sending the original out, and stays stable across runs. The case records, certifications, patents, awards, and marketplace listings that back it, in one place.

< 4 hrs Root cause identification 21 days → 4 hrs · 99% shorter
88.55% F1-score (DTS augmentation) 58.55% → 88.55% · +30pp
1 day Model time-to-deploy 4 weeks → 1 day · 95% shorter
Customers

Across banking, insurance, legal, the public sector, and telecom.

ISO/IEC 27001:2022 Information SecurityISO/IEC 42001:2023 AI ManagementGS Certified Grade 1, DTS 2025GS Certified Grade 1, LLM Capsule 2024KISA Fast Track 2024Information Security Product Innovation Award 2024 H2, DTSInformation Security Product Innovation Award 2024 H1, LLM CapsuleEmerging AI+X Top 100 2026 (AIIA)NextRise Global Innovator 2024Deutsche Telekom T Challenge 2026 FinalistIntellyx Digital Innovator Award 2026Startup World Cup Finalist 2025AI Medical Innovation Award, AI EXPO KOREA 2025Human Technology Award 2025, Excellence Prize (Hankyoreh)Global TIPS 2026 (Ministry of SMEs and Startups)Innovative Product, Public Procurement Service 2026, DTSInnovative Product, Public Procurement Service 2025, LLM CapsulePilot Purchase Program 2025, LLM CapsuleISO/IEC 27001:2022 Information SecurityISO/IEC 42001:2023 AI ManagementGS Certified Grade 1, DTS 2025GS Certified Grade 1, LLM Capsule 2024KISA Fast Track 2024Information Security Product Innovation Award 2024 H2, DTSInformation Security Product Innovation Award 2024 H1, LLM CapsuleEmerging AI+X Top 100 2026 (AIIA)NextRise Global Innovator 2024Deutsche Telekom T Challenge 2026 FinalistIntellyx Digital Innovator Award 2026Startup World Cup Finalist 2025AI Medical Innovation Award, AI EXPO KOREA 2025Human Technology Award 2025, Excellence Prize (Hankyoreh)Global TIPS 2026 (Ministry of SMEs and Startups)Innovative Product, Public Procurement Service 2026, DTSInnovative Product, Public Procurement Service 2025, LLM CapsulePilot Purchase Program 2025, LLM Capsule
Certifications & Awards

Backed by international certifications and industry awards.

Gartner® “Emerging Tech: AI Vendor Race: Tech Innovators in Agentic AI — Solution Accelerators” (2026)

Gartner® “Emerging Tech: AI Vendor Race: Most Prominent Use Cases in Agentic AI by Industry” (2026)

Gartner® “Emerging Tech: Provider Differentiation Strategy—Trends for Hyper-Synthetic Data” (2025)

Named a Representative Vendor

Operational Evidence

Same conditions,
only the data state changed.

Results checked by changing only the data state, with the use case, model, evaluation set and runtime held constant. Cases where the result moved and cases where it did not are both recorded as they are. Actual records measured in customer environments and representative examples that show the mechanism are labeled apart.

See whether your data is in a state to start AI at all.

Actual recordFinancial Services

Retraining pipeline: schema change caught before the next run

AI: Retraining pipeline model Data: Upstream feature tables

Held constantUse caseRetraining pipeline operationModelSame model and pipeline, version fixedEvaluationSame validation procedureEnvironmentSame runtimeReproducePre-change Release State re-run
Data ReadinessProblemWhere the data stands today
An upstream schema change caused silent degradation. Root cause took 21 days and downstream decisions were affected in the meantime.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanInput data versioned as a Release State with a schema diff at ingestion. Two missing feature columns and one type change surfaced there.
  • Inputs versioned as a Release State
  • Schema diff checked at ingestion
Review Validation ResultsResultUsable for this use case, or not
Data qualified forthe retraining pipelineRoot causeBefore21 daysAfterUnder 4 hoursChangedInput data state before and after the schema changeThe change surfaced before the next training run, so no degraded model reached production. Root cause in under 4 hours.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The pre-change Release State was re-run on the same pipeline and model. Every later run records which data version it used.State Card · Change Log · Re-run Record · Schema Diff

Representative exampleTelecom

Real-time inference: score drift traced and rolled back within 2 hours

AI: Real-time inference model Data: Preprocessed feature stream

Held constantUse caseReal-time inference serviceModelSame inference modelEvaluationSame production score metricEnvironmentSame serving environmentReproducePre-change state re-run
Data ReadinessProblemWhere the data stands today
Production scores became erratic after a preprocessing update. There was no way to trace which version the drift started from.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanEvery run bound to its Release State (Run Binding). The diff pointed to a normalization change and a 12% shift in feature scaling range.
  • Scores bound to a Release State per run
  • Change located with the Release State diff
Review Validation ResultsResultUsable for this use case, or not
Data qualified forreal-time inferenceTrace and rollbackBeforeNot possibleAfterUnder 2 hoursChangedData state before and after the preprocessing updateRolled back to the pre-drift Release State within 2 hours, with a record of which run used which data state.
Operational AssuranceOperate and recordRecorded and revalidated in operation
Scores stay bound to their Release State per run, so the next update shows at once which version changed the result.State Card · Change Log · Re-run Record

Actual recordManufacturing

Quality inspection model: three missing defect classes reconstructed

AI: Defect detection vision model Data: Inspection images and labels, three rare defect classes

Held constantUse caseDefect detectionModelSame inspection modelEvaluationSame evaluation on unseen dataEnvironmentSame runtimeReproduceRe-run verified
Data ReadinessProblemWhere the data stands today
Rare defect classes were underrepresented and F1-score sat at 58.55%. The model missed edge cases in production.
Use Case ValidationWhat we applyPrepared and validated for the use case
DTSThe three classes were augmented with DTS reconstruction under DP and the class distribution rebalanced.
  • Three defect classes augmented with DP reconstruction
  • Class distribution rebalanced and versioned
Review Validation ResultsResultUsable for this use case, or not
Data qualified fordefect detectionF1-score+30ppBefore58.55%After88.55%ChangedTraining data state before and after augmenting three defect classesF1-score moved to 88.55% and the coverage gap closed before the next training cycle.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The augmented set was validated on unseen data. Connected to Syntitan, the next training cycle can re-check it under the same conditions.Dataset Version · Re-run Record · Class Dist. Log

Representative exampleTelecom

Churn prediction model: the data checked before swapping the model

AI: Churn prediction model (XGBoost v1.3) Data: Subscription and usage history, too few churn cases

Held constantUse caseChurn predictionModelSame model (XGBoost v1.3)EvaluationSame evaluation set, F1/Recall/PrecisionEnvironmentSame runtimeReproduceRe-run verified
Data ReadinessProblemWhere the data stands today
The churn model was missing its recall target. The team wanted to know whether the data, not only the model, was involved.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanAdded the churn cases that were too few and restored the missing tenure context. Everything else untouched, re-run under the same conditions.
  • Missing churn cases added
  • Missing tenure context restored
Review Validation ResultsResultUsable for this use case, or not
Data qualified forchurn predictionF1+0.07Before0.75After0.82ChangedData state before and after adding churn casesRecall 0.71 to 0.84, Precision 0.79 to 0.80. Improved, and the criteria (F1 at least 0.80, Recall at least 0.82) were met. Improvement and criteria are judged separately.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The validated data is released as a version and every run records which version it used. When churn patterns shift, only that scope is revalidated.State Card · Dataset Version · Re-run Record

Representative exampleHealthcare

Clinical AI validation: back on track without the patient records it could not access

AI: Clinical prediction model, validation stage Data: Patient records, not accessible

Held constantUse caseClinical model validationModelSame validation pipeline and modelEvaluationSame validation metricsEnvironmentSame validation environmentReproduceReproducible from the Release State version
Data ReadinessProblemWhere the data stands today
Regulatory constraints blocked access to the patient records needed for validation, so the pipeline stalled.
Use Case ValidationWhat we applyPrepared and validated for the use case
DTSThe inaccessible records were replaced with DTS reconstruction under DP. Distributional properties are preserved, with no mapping back to individuals.
  • Inaccessible records replaced with DP reconstruction
  • Distribution kept, never mapped back
Review Validation ResultsResultUsable for this use case, or not
Data qualified forclinical model validationValidation pipelineBeforeHaltedAfterRunningChangedReconstructed data in place of the patient recordsThe validation pipeline resumed without modification, with a DP audit log kept for review.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The reconstructed data version and DP audit log stay together, so the same validation pipeline can run again at any time.State Card · DP Audit Log · Dataset Version

Representative exampleFinancial Services

Claims processing LLM: the same task, without sending the original out

AI: Claims assistance LLM, external API Data: Claims documents with names, IDs and medical details

Held constantUse caseClaims processing assistanceModelSame external LLM and promptEvaluationSame output comparisonEnvironmentMapping layer inside the customer environmentReproduceLogged per run, Release State binding
Data ReadinessProblemWhere the data stands today
Claims documents with policyholder names, IDs and medical details were going to an external LLM API as they were. Compliance halted the workflow.
Use Case ValidationWhat we applyPrepared and validated for the use case
LLM CapsuleLLM Capsule substitutes identifiers such as names and IDs and keeps the context the claim decision needs. Originals and the mapping stay in a protected mapping layer inside the customer environment.
  • Identifiers substituted, claim context kept
  • Outputs restored through Business-Ready Reconstruction
Review Validation ResultsResultUsable for this use case, or not
Data qualified forclaims processing assistanceExternal LLM pathBeforeHaltedAfterRunning on substitutesChangedA substituted working version sent instead of the originalThe external LLM path reopened. Outputs came back to the original names, codes and values through Business-Ready Reconstruction and fed downstream as they were.
Operational AssuranceOperate and recordRecorded and revalidated in operation
Substitution and reconstruction logs are kept per run and bound to a Release State. When the substitution policy changes, only that scope is requalified.State Card · Substitution Log · Mapping Record · Re-run Record

Representative exampleRetail

Recommendation engine: pre-upgrade state reproduced within 3 hours

AI: Recommendation model Data: User and item embeddings

Held constantUse caseRecommendation model operationModelSame recommendation modelEvaluationSame evaluation dataEnvironmentRuntime before and after the upgradeReproducePre-upgrade state re-run
Data ReadinessProblemWhere the data stands today
Recommendation scores dropped after a routine infrastructure upgrade and engineers could not reproduce the pre-upgrade behavior.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanRuntime parameters captured as a Release State and each run bound to it. The diff pointed to a library version change and a floating-point handling difference.
  • Runtime parameters captured in the Release State
  • Runs bound to that state with Run Binding
Review Validation ResultsResultUsable for this use case, or not
Reproducedthe pre-upgrade stateRoot causeBeforeDaysAfterUnder 3 hoursChangedRuntime only. The data state stayed the sameThe pre-upgrade Release State was re-run within 3 hours and the score gap confirmed.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The runtime snapshot is part of the Release State, so the previous state can be re-run as it was at the next upgrade.State Card · Runtime Snapshot · Change Log · Re-run Record

Representative examplePublic Sector

LLM adoption under N2SF: started without sending confidential context out as it was

AI: External LLM for air-gapped work Data: Work documents with confidential context

Held constantUse caseAir-gapped LLM tasksModelSame external LLMEvaluationPer-request output verificationEnvironmentMapping inside the boundary, local logsReproduceRe-verifiable per request
Data ReadinessProblemWhere the data stands today
In the segregated network the original context could not leave, so bringing an external LLM into the work had stalled.
Use Case ValidationWhat we applyPrepared and validated for the use case
LLM CapsuleLLM Capsule substitutes sensitive context with context-preserving values before processing. Only the substituted version reaches the external LLM. Configured against the N2SF guideline.
  • Sensitive context substituted with context-preserving values
  • Workflow configured against the N2SF guideline
Review Validation ResultsResultUsable for this use case, or not
Data qualified forair-gapped LLM tasksLLM adoptionBeforeHaltedAfterIn progressChangedA substituted working version sent instead of the originalLLM adoption moved forward. Outputs return to the original values inside through Business-Ready Reconstruction.
Operational AssuranceOperate and recordRecorded and revalidated in operation
Local logs are kept per request for later verification. Originals and the mapping stay inside the boundary.Local Mapping Layer · Audit Log · N2SF Reference

Validation recordAviation · Public

Aviation safety documents: identifiers substituted, safety data kept, checked on an aviation safety authority test set

AI: Document review LLM, external API path Data: Aviation safety documents mixing personal data, aviation identifiers and safety data

Held constantUse caseAviation document substitution checkModelEngine output scored, not AI answersEvaluation1,061 answer items, all scoredEnvironmentDirect API call on synthetic dataReproduceSame input, same result, repro file attached
Data ReadinessProblemWhere the data stands today
Aviation documents put names and contacts, licence numbers, employee IDs, aircraft registrations, call signs and flight numbers in the same sentence as airport codes, runways and NOTAMs that must stay readable. Aviation has no separate protected category like PHI in healthcare. Regulation attaches to data types instead: PNR (Passenger Name Record), API/APIS (Advance Passenger Information), SFPD and PNRGOV, controlled through purpose limitation and retention rules (EU PNR Directive 2016/681, ICAO Doc 9944). No one had measured whether a substitution layer could tell these identifiers from safety data.
Use Case ValidationWhat we applyPrepared and validated for the use case
LLM CapsuleQA registered seven aviation types from the aviation safety authority guide as custom filters (6 patterns, 1 keyword list) on top of the 5 default filters. Safety data stayed outside the substitution scope.
  • Control run without aviation filters: 0 of 368 aviation items
  • Recommended set: boundary fixes plus a flight-level exclusion
Review Validation ResultsResultUsable for this use case, or not
Criteria met onthe authority test set, 300 sentences and 1,061 itemsAviation identifiers substituted, out of 368Before0%After100%ChangedRegistering the 7 aviation custom filters658 of 658 substitution targets and 403 of 403 preservation targets. Zero misses, zero over-substitutions. Conditions: synthetic data, API path only, and four identifier formats still pending the institution's confirmation, so the verdict is conditional.
Operational AssuranceOperate and recordRecorded and revalidated in operation
9 of 10 regression tests passed, including filter registration and removal restoring the original state, and dedicated filters winning on overlap. The UI path case runs in formal QA. When the institute adds a sensitive type, QA registers it the same way and re-measures.Test Cases 300 · Answer Key 1,061 · Filter Config 24 · Scoring Result

Representative exampleManufacturing

OT network data: usable for threat analysis AI with its structure intact

AI: Threat analysis AI Data: OT/ICS network data

Held constantUse caseThreat analysisModelSame analysis AIEvaluationSame threat question setEnvironmentExisting OT analysis platformReproduceFixed data state re-run
Data ReadinessProblemWhere the data stands today
OT/ICS network data carried sensitive operational detail and could not go to an external AI for automated threat analysis.
Use Case ValidationWhat we applyPrepared and validated for the use case
LLM CapsuleSensitive fields substituted while topology and relationships stay intact (structure-preserving substitution). Runs inside the existing OT analysis platform.
  • Sensitive fields substituted, topology and relations kept
  • Runs inside the existing OT analysis platform
Review Validation ResultsResultUsable for this use case, or not
Data qualified forthreat analysisAI threat analysisBeforeBlockedAfterIn progressChangedSensitive fields substituted, relationship structure keptThe analysis AI can now read the network data and answer threat questions. Sensitive values are substituted and the relationship structure is preserved.
Operational AssuranceOperate and recordRecorded and revalidated in operation
Data states before and after substitution are fixed, and the analysis was re-run to confirm the same result.Transformed Dataset · Analysis Log · Structure Map

Representative exampleTelecom

Network operations model: topology change confirmed as the cause by re-run

AI: NOC operations model Data: Network configuration and state logs

Held constantUse caseNOC operations modelModelSame NOC modelEvaluationSame output stability metricEnvironmentConfiguration before and after the changeReproducePre-change replay, re-verified after the fix
Data ReadinessProblemWhere the data stands today
NOC model outputs became unstable after a network configuration change. The model itself was unchanged, so the cause was unclear.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanNetwork state captured as a Release State per run. The diff against the previous run identified the changed configuration elements.
  • Network state captured as a Release State per run
  • Configuration change identified by diff
Review Validation ResultsResultUsable for this use case, or not
Data qualified forthe NOC operations modelDrift causeBeforeUnknownAfterIdentifiedChangedState before and after the network configuration changeRe-running the pre-change state confirmed the topology change as the cause. Verified again after the fix.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The pre-change state was replayed to confirm, and verified again after the fix. Every configuration change leaves a diff.State Card · Topology Diff · Change Log · Re-run Record

Representative exampleNo change after refinementRetail

Pricing recommendation model: refining the data did not reach the criteria

AI: Pricing recommendation model Data: Sales and price history with gaps and outliers

Held constantUse casePricing recommendationModelSame pricing modelEvaluationSame evaluation set and hit rateEnvironmentSame runtimeReproduceRe-run verified
Data ReadinessProblemWhere the data stands today
The hit rate was below target in production and data quality was the working hypothesis.
Use Case ValidationWhat we applyPrepared and validated for the use case
SyntitanThe same model was re-run on the same evaluation set with missing values and outliers fixed.
  • Re-run on a data state with gaps and outliers fixed
  • Model and evaluation set fixed
Review Validation ResultsResultUsable for this use case, or not
Not QualifiedThreshold not met. This refinement did not reach the 65% target.Hit rate+1ppBefore58%After59%ChangedData state before and after fixing missing values and outliersHit rate 58% to 59%, short of the 65% target. The remaining failure cases and evaluation evidence guide the next refinement or AI setup review.
Operational AssuranceOperate and recordRecorded and revalidated in operation
The re-run record with model and evaluation set fixed stays, so the next hypothesis starts from the same conditions.State Card · Re-run Record

Representative exampleRetail

Consolidated customer data: state checked before choosing any AI

AI: Not chosen yet Data: Customer table merged from three systems

Held constantUse caseNot set (before adoption)ModelNot chosenEvaluationSix-axis overall scoreEnvironmentSyntitan Data ReadinessReproduceSame file re-diagnosed before and after
Data ReadinessProblemWhere the data stands today
There was a plan to use customer data from three systems for AI, but no one could say in numbers what state it was in.
Improve Data ReadinessRefineImprove only what came out low
Syntitan Data ReadinessA six-axis diagnosis on one file. Consistency (mismatched code schemes) and Traceability (no source column) came out low, so only those two were fixed.
  • Six-axis diagnosis on one file
  • Only Consistency and Traceability fixed
Data Readiness ScoreResultShared readiness score
Data ReadinessUse case verdict comes next in Use Case ValidationOverall score · six axes+21Before58After79ChangedData state before and after refinementThis is shared readiness, not the success odds of a specific AI. Once the use case is set, Use Case Validation starts from this version.
Next · Use Case ValidationNext stepValidate once the use case is set
Assessment results and improvement history are kept as one version. When the use case is set, fit validation starts from it.Data Readiness Report · Dataset Version

Representative exampleManufacturing

Equipment sensor logs: readiness checked before picking a predictive maintenance AI

AI: Not chosen yet, predictive maintenance candidates under review Data: 18 months of line sensor logs

Held constantUse caseNot set (predictive maintenance candidates)ModelNot chosenEvaluationSix-axis overall scoreEnvironmentSyntitan Data ReadinessReproduceRe-diagnosed before and after
Data ReadinessProblemWhere the data stands today
Predictive maintenance AI was under review, but the sensor logs had missing stretches and series in mixed units.
Improve Data ReadinessRefineImprove only what came out low
Syntitan Data ReadinessIntegrity and Reproducibility came out low on the six-axis diagnosis. Only the gap markers and unit normalization were handled first.
  • Gap markers and unit normalization
  • Only Integrity and Reproducibility fixed
Data Readiness ScoreResultShared readiness score
Data ReadinessUse case verdict comes next in Use Case ValidationOverall score · six axes+23Before51After74ChangedData state before and after refinementA starting point where any vendor model can be compared from the same state. The score is shared readiness, not a performance prediction.
Next · Use Case ValidationNext stepValidate once the use case is set
The refined logs are fixed as one version. Once a candidate model is chosen, the same-condition comparison runs on it.Data Readiness Report · Dataset Version

Representative examplePublic Sector

Citizen inquiry records: text state diagnosed before reviewing an LLM

AI: Not chosen yet, inquiry-response LLM under review Data: Inquiry text records by department

Held constantUse caseNot set (inquiry-response LLM under review)ModelNot chosenEvaluationSix-axis overall scoreEnvironmentSyntitan Data ReadinessReproduceRe-diagnosed before and after
Data ReadinessProblemWhere the data stands today
Record formats differed by department and many entries lacked category codes and outcomes. There was no basis to judge whether an LLM could be attached.
Improve Data ReadinessRefineImprove only what came out low
Syntitan Data ReadinessContext and Usability came out low on the six-axis diagnosis. Only category codes and format normalization were handled first.
  • Category codes filled, formats normalized
  • Only Context and Usability fixed
Data Readiness ScoreResultShared readiness score
Data ReadinessUse case verdict comes next in Use Case ValidationOverall score · six axes+25Before46After71ChangedData state before and after refinementThis score is shared readiness. Whether the data works for inquiry response is judged separately in Use Case Validation.
Next · Use Case ValidationNext stepValidate once the use case is set
Assessment and improvement history are versioned. When the inquiry-response use case is confirmed, fit validation uses that version.Data Readiness Report · Dataset Version
Certifications

Standards,
third-party verified.

Third-party validated certifications across information security, privacy, and operational standards.

ISO 27001 · Information Security Management

Information Security Management

ISO/IEC 27001:2022 · 2026

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

ISO 42001 · AI Management System

AI Management System

ISO/IEC 42001:2023 · 2026

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

GS Certification Grade 1 · DTS (2025)

GS Grade 1 · DTS

GS Certification Grade 1 · 2025

Korean SW Quality Certification, Grade 1 (2025). Verified quality, eligible for public procurement.

GS Certification Grade 1 · LLM Capsule (2024)

GS Grade 1 · LLM Capsule

GS Certification Grade 1 · 2024

Korean SW Quality Certification, Grade 1 (2024). Listed on the public Innovation Marketplace for procurement.

KISA Fast Track 2024

KISA Fast Track

KISA · 2024

Selected for the KISA information-security industry Fast Track program.

Patents

The patents
behind the tech.

Registered patents and pending applications behind Syntitan, DTS, and LLM Capsule. As of July 2026: 17 patents (5 registered, 12 pending) and 2 registered software copyrights. Only published filings are listed below.

▸ 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 · Pub. US 2025/0117517 A1)

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

View patent →
▸ Patent · KR Registered

Method and Data Processing Apparatus for De-identifying Data While Preserving Target Characteristics

KR Reg. No. 10-2926046 · App. No. 10-2023-0167085 · Registered 2026-02-06

Core DTS patent. Method for transforming source data while preserving target characteristics such as statistical distributions and label structure.

View patent →
▸ Patent · KR Registered · US Pending

Synthetic Data Generation Method Without Leaking Target Information and Client Apparatus

KR Reg. No. 10-2818137 (App. 10-2024-0017564, Registered 2025-06-04) · US App. No. 19/039,319 · Pub. No. US 2025/0252156 A1 (under examination)

DTS synthesis patent. Client-server architecture for generating synthetic data without exposing target information.

View patent →
▸ Patent · KR Registered · US Pending

Method and Data Processing Apparatus for Generating a Synthetic Dataset Containing Multiple Attributes

KR Reg. No. 10-2818136 · App. No. 10-2024-0131551 · Registered 2025-06-04 · US Pub. No. US 2026/0017275 A1 (under examination)

DTS multi-attribute synthesis patent. Method for generating complex synthetic datasets that span multiple feature columns and attribute types.

View patent →
▸ Patent · KR Pending

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

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

▸ Patent · KR US Pending

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, via export-voucher)

Security provisioning method for AI models running on-device.

▸ Patent · KR US Pending

Method and Data Processing Apparatus for Validating Synthetic Datasets for Model Training

KR App. No. 10-2024-0174041 (Filed 2024-11-28) · US App. No. 19/400,665 (Filed 2025-11-25) · under examination

DTS patent application for validating synthetic datasets used to build training models.

▸ Patent · KR Pending

Method and Data Processing Apparatus for Filtering Synthetic Datasets for Model Training

KR App. No. 10-2024-0174042 · Filed 2024-11-28 · Under examination

DTS patent application. Method for filtering synthetic datasets prior to model training.

▸ Patent · KR Pending

Method and Inference Apparatus for Building Deep Learning Models Robust to Private Information Exposure

KR App. No. 10-2023-0074745 · Filed 2023-06-12 · Under examination (office action issued 2026-03)

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

▸ Patent · KR Pending

Method and Analysis Apparatus for Building Artificial Intelligence Models that Process Heterogeneous Datasets

KR App. No. 10-2023-0013029 · Filed 2023-01-31 · Under examination (response filed 2026-01-14)

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

Research

The research
behind the products.

Selected publications by CUBIG founders, from peer-reviewed venues to a survey preprint. The privacy and robustness research behind Syntitan, DTS, and LLM Capsule.

Publication · 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.

Publication · ICLR 2024

DAFA: Distance-Aware Fair Adversarial Training

Hyungyu Lee, Saehyung Lee, Hyemi Jang, Junsung Park, Ho Bae, Sungroh Yoon · ICLR · Vienna, May 2024

Adversarial training method that enforces fairness across subgroups via distance-aware margin adjustment.

Publication · 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.

Publication · 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

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

Publication · BIBM 2023

Privacy-Preserving Publishing of Individual-Level Medical Data for Cloud Services

Ho Bae, Heonseok Ha, Siwon Kim · IEEE BIBM · Istanbul, Dec 2023

Formal privacy-preserving framework for publishing patient-level medical records to cloud services, with emphasis on utility preservation under strict privacy constraints.

Publication · 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.

Publication · RAID 2023

Exploring Clustered Federated Learning's Vulnerability against Property Inference Attack

Hyunjun Kim, Yungi Cho, Younghan Lee, Ho Bae, Yunheung Paek · RAID · 2023

Reveals property-inference privacy risks in clustered federated learning.

Publication · 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

Deep-learning analysis of malicious DNA sequences for security and privacy in genomic data.

Publication · BMVC 2022

MPGAN: Membership Privacy-Preserving GAN

Heonseok Ha, Uiwon Hwang, Jaehee Jang, Ho Bae, Sungroh Yoon · BMVC · London, Nov 2022

GAN training method that prevents membership inference attacks on generated data, providing formal, provable privacy properties for synthetic outputs.

Publication · ACM AsiaCCS 2022

Membership Feature Disentanglement Network

Heonseok Ha, J Jang, Y Jeong, S Yoon · ACM Asia Conference on Computer and Communications Security · 2022

Network architecture that disentangles membership-sensitive features from model representations, reducing exposure to membership inference attacks.

Publication · IEEE Access 2021

Gradient Masking of Label Smoothing in Adversarial Robustness

Hyungyu Lee, Ho Bae, Sungroh Yoon · IEEE Access · 2021

Analysis of how label smoothing induces gradient masking, a false sense of robustness that does not transfer to true adversarial settings.

Publication · IEEE TAI 2021

Learn2Evade: Learning-based Generative Model for Evading PDF Malware Classifiers

Ho Bae, Younghan Lee, Yohan Kim, Uiwon Hwang, Sungroh Yoon, Yunheung Paek · IEEE Transactions on Artificial Intelligence · Aug 2021

Adversarial generative modeling of malware evasion: learning to produce feature-space perturbations that bypass PDF malware classifiers while preserving functionality.

Publication · IEEE Access 2020

Anomaly Detection by Learning Dynamics From a Graph

Jaekoo Lee, Ho Bae, Sungroh Yoon · IEEE Access · 2020

Graph-based anomaly detection that learns system dynamics to flag abnormal behavior.

Publication · PSB 2020

AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data

Ho Bae, Dahuin Jung, Hyun-Soo Choi, Sungroh Yoon · Pacific Symposium on Biocomputing · Hawaii, Jan 2020

GAN-based anonymization of private medical datasets while preserving statistical utility for downstream analysis.

Publication · PSB 2019

DNA Steganalysis Using Deep Recurrent Neural Networks

Ho Bae, Byunghan Lee, Sunyoung Kwon, Sungroh Yoon · Pacific Symposium on Biocomputing · Hawaii, Jan 2019

Deep recurrent-network method for detecting hidden messages embedded in DNA sequences (steganalysis), applied to genomic data.

Preprint · arXiv 2018

Security and Privacy Issues in Deep Learning

Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Sungroh Yoon · arXiv:1807.11655 · 2018

Comprehensive survey of attack surfaces and defenses in deep learning systems, covering adversarial examples, model extraction, and data poisoning.

Awards & Recognition

Recognized by government
and industry.

From government program selections to industry awards at home and abroad: third-party validation of our technology and business.

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

Deutsche Telekom T-Challenge 2026 · 2nd Place

T-Mobile / Deutsche Telekom · 2026

Placed 2nd in the T-Challenge global open-innovation program (T-Mobile / Deutsche Telekom), recognized for LLM Capsule's context-preserving substitution and local reconstruction.

2026 Emerging AI+X Top 100
Industry Recognition

2026 Emerging AI+X Top 100

Korea AI Industry Association · 2026

Selected for the 2026 Emerging AI+X Top 100 for its AI-Ready data technology.

Selected Supplier · 2026 AI Voucher Program
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.

Selected Supplier · 2026 Data Voucher Program
Government Program

Selected Supplier · 2026 Data Voucher Program

Korea Data Agency (K-DATA) · 2026

Selected as a Data Voucher supplier, rebuilding restricted enterprise data into AI-Ready data with DTS.

Global TIPS 2026
Government Program

Global TIPS 2026

Ministry of SMEs and Startups · 2026

Korea's flagship program linking private investment with government R&D for startups with proven global potential. Selected for Global TIPS 2026 (commercialization and R&D tracks) to advance Syntitan and expand overseas.

Innovative Product · DTS (Public Procurement Service, 2026)
Public Procurement

Innovative Product · DTS

Public Procurement Service · 2026

Designated an Innovative Product by the Public Procurement Service. Public institutions can adopt DTS directly through the Innovation Marketplace.

Ultra-Gap Startup 1000+ (DIPS 1000+)
Government Program

Ultra-Gap Startup 1000+ (DIPS 1000+)

Ministry of SMEs and Startups · KISED · 2025

Selected in 2025 as a top deep-tech startup (AI / big-data) in the Ultra-Gap Startup 1000+ project, and as a Global ICT Future Unicorn the same year.

Innovative Product · LLM Capsule (Public Procurement Service, 2025)
Public Procurement

Innovative Product · LLM Capsule

Public Procurement Service · 2025

Designated an Innovative Product by the Public Procurement Service. Public institutions can adopt LLM Capsule directly through the Innovation Marketplace.

Pilot Purchase Program · LLM Capsule (2025)
Government Program

Pilot Purchase Program · LLM Capsule

Ministry of SMEs and Startups · KOSMES Distribution Agency · 2025

Selected for the Ministry of SMEs and Startups' Technology Development Product Pilot Purchase Program, letting public institutions purchase and validate LLM Capsule directly.

Human Technology Award 2025 · Excellence Prize, User Category
Industry Award

Human Technology Award 2025 · Excellence Prize

Hankyoreh · User Category · 2025

Excellence Prize in the User Category at the Human Technology Awards, which recognize human-centered technology. Awarded for DTS.

Startup World Cup 2025 Finalist
Industry Award

Startup World Cup 2025 Finalist

Pegasus Tech Ventures · 2025

Won the Seoul regional of Startup World Cup 2025 (June) and advanced to the Grand Finale in San Francisco (October 2025).

NVIDIA Inception
Global Membership

NVIDIA Inception

2024–2025

Member of NVIDIA Inception, the global program for AI startups.

Information Security Product Innovation Award · DTS (Minister of Science and ICT Prize, 2024 H2)
Government Award

Information Security Product Innovation Award · DTS

Ministry of Science and ICT · 2024 H2

Grand Prize, Information & Physical Security category, at the 2024 H2 Information Security Product Innovation Awards (Minister of Science and ICT Prize).

Information Security Product Innovation Award · LLM Capsule (Minister of Science and ICT Prize, 2024 H1)
Government Award

Information Security Product Innovation Award · LLM Capsule

Ministry of Science and ICT · 2024 H1

Winner at the 2024 H1 Information Security Product Innovation Awards (Minister of Science and ICT Prize). CUBIG won in both halves of 2024.

NextRise Global Innovator (2024)
Industry Award

NextRise Global Innovator

2024

Selected as a NextRise Global Innovator.

SK Telecom × Hana Bank AI Accelerator
Accelerator

SK Telecom × Hana Bank AI Accelerator

SK Telecom · Hana Bank · 2024

Selected for the 2nd SK Telecom × Hana Bank AI startup accelerator (15 of 230 applicants).

Available on

Find us on
leading marketplaces.

Access CUBIG solutions through supported cloud marketplaces.

AWS Marketplace AWS Marketplace DTS · LLM Capsule
Naver Cloud Platform Naver Cloud Platform DTS
Microsoft Marketplace Microsoft Marketplace LLM Capsule
FAQ

Frequently asked
questions.

Common questions on operational evidence, reproducibility, and sensitive-data handling.

What is operational evidence in AI systems? +
Operational evidence in AI systems is concrete, verifiable documentation that shows how an AI system behaves in production. It shows what changed between runs, what caused the difference, and whether the same conditions can be replayed. It includes before/after outcomes, state comparisons, and re-run records.
What is reproducible AI execution? +
Reproducible AI execution means a run can be replayed and verified. The conditions include data state, schema, preprocessing logic, and runtime dependencies. Syntitan achieves this through Release State, Run Binding, and Reproduce.
Why is AI execution unstable in production? +
AI execution becomes unstable in production when execution conditions change after deployment: data schemas, preprocessing logic, dependencies, data windows. Any of these can cause AI results to drift without any change to the model itself. When execution state is not captured and fixed, it is hard to pinpoint which change caused a production issue.
How does CUBIG handle sensitive data in LLM workflows? +
CUBIG's LLM Capsule substitutes sensitive values with restorable stand-ins before the data crosses to an external LLM. The original values stay in the protected mapping layer inside the enterprise boundary. The LLM operates on the substituted version, and Business-Ready Reconstruction restores the output to the original names, codes, and values inside your environment for downstream use.
Can CUBIG be deployed on-premises or air-gapped? +
Yes. LLM Capsule can be installed on-premises or in air-gapped environments, with the setup proposed to fit your environment. Detailed architecture available on request for qualified evaluations.

The proof is in the execution.

Bring one workflow that isn't reproducible today. We'll show, on your data, what AI-Ready execution changes.

Book architecture review Try it on your data