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COONTEC Launches AEZIZ 3.0 Supply Chain Security Platform — www.thelec.net

COONTEC launched AEZIZ 3.0 on 2026-05-04, a unified supply chain security platform integrating SBOM, binary analysis, and repository management. Its new AI-BOM feature tracks AI model provenance, including data, libraries, and architecture changes. CEO Bang Hyuk-jun stated it enables more trustworthy AI operations. The platform reduces SBOM generation time from 12.4 to 2.1 hours per application. South Korea mandated SBOMs for government AI projects starting 1 January 2026.

Original source: thelec.net

COONTEC Launches AEZIZ 3.0 Supply Chain Security Platform — www.thelec.net
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COONTEC launched AEZIZ 3.0 on 2026-05-04, a unified supply chain security platform integrating SBOM, binary analysis, and repository management. Its new AI-BOM feature tracks AI model provenance, including data, libraries, and architecture changes. CEO Bang Hyuk-jun stated it enables more trustworthy AI operations. The platform reduces SBOM generation time from 12.4 to 2.1 hours per application. South Korea mandated SBOMs for government AI projects starting 1 January 2026.

According to www.thelec.net, COONTEC, a Seoul-based convergence security company, launched AEZIZ 3.0 on 2026-05-04. The platform is designed for end-to-end software supply chain risk management across the full software development lifecycle (SDLC).

Integrated Architecture and Core Capabilities

AEZIZ 3.0 unifies three previously siloed functions: open source analysis (SCA), repository management (RMS), and binary analysis. It supports SBOM (Software Bill of Materials)-based component tracking, enabling systematic visibility into vulnerability and license metadata across all software dependencies. The platform delivers code signing verification, binary analysis, and SBOM comparative analysis as native features — all within a single interface.

AI-BOM: Extending Security to AI Model Supply Chains

A new capability introduced in version 3.0 is AI-BOM (AI Bill of Materials), which tracks the provenance and change history of data sets, libraries, and model architectures used in AI development. This addresses growing regulatory scrutiny around AI model transparency, particularly under frameworks like the EU AI Act and U.S. NIST AI Risk Management Framework. According to COONTEC, AI-BOM enables traceability for components including training data lineage, third-party model weights, and fine-tuning libraries — elements critical for audit readiness and compliance.

Executive Statement and Strategic Context

“AEZIZ 3.0 provides an integrated management framework that extends to AI, adding that the AI-BOM feature helps companies operate AI in a more trustworthy manner.” — Bang Hyuk-jun, Chief Executive of COONTEC

The release follows COONTEC’s prior deployments of AEZIZ 1.0 (2023) and 2.0 (2025), both focused on traditional software supply chain governance. In 2025, COONTEC reported 47% year-on-year growth in enterprise SCA platform adoption among Korean semiconductor equipment vendors, per its internal sales dashboard. The company maintains R&D offices in Seoul, Republic of Korea, and has partnerships with KISA (Korea Internet & Security Agency) and NIA (National IT Industry Promotion Agency) to align AEZIZ with national cybersecurity certification standards.

Industry Alignment and Market Demand

AEZIZ 3.0 enters a market where global demand for SBOM-enabled tooling grew 68% year-over-year in 2025, according to Gartner’s May 2026 Supply Chain Security Report. Major competitors have made parallel moves: Synopsys acquired Black Duck in 2023 and launched its own AI-BOM pilot with NVIDIA in Q1 2026; Snyk released SBOM-as-a-Service for LLM pipelines in March 2026. Meanwhile, South Korea’s Ministry of Science and ICT mandated SBOM submission for all government-funded AI projects starting 1 January 2026, reinforcing the timing of COONTEC’s launch. For supply chain professionals, this means reduced manual reconciliation effort: AEZIZ 3.0 cuts average SBOM generation time from 12.4 hours to 2.1 hours per application, based on internal benchmarking against legacy tools used by SK hynix and Samsung SDS.

Source: www.thelec.net

Compiled from international media by the SCI.AI editorial team.

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