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RaonSecure、エージェント AI 向けの 5 つの統合セキュリティ ソリューションを発表

RaonSecure は、自律型 AI エージェントとロボットの保護を目的とし、アイデンティティ、動作、データ、防御、攻撃の軽減をカバーする 5 つのセキュリティ ツール スイートを発表しました。

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Source-provided image accompanying RaonSecure launches five integrated security solutions for agentic AI
出典参照記録されたソース
出版社
mk.co.kr
ソースリンク
mk.co.krhttps://www.mk.co.kr/news/english/12165752
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リンクされたソース — プライマリ ソースのステータスが確立されていません。
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ここから始めましょう

重要な用語

人工知能 (AI)
パターン認識、推論、言語、意思決定を必要とするタスクを実行するシステムを構築する広範な分野。
大規模言語モデル (LLM)
テキストを生成および分析するために大規模なテキスト コーパスでトレーニングされた言語モデル。
即時注入
悪意のある命令がモデルの入力または取得されたコンテンツに挿入される攻撃パターン。
自分自身をテストしてくださいAI エージェント クイズ

何が起こったのか

RaonSecure Co. announced five new security solutions designed specifically for agentic artificial intelligence systems at its 2026 Security IQ Up (SQUP) event in Seoul. The suite—named OneAccess, OneHacker, OneTag, OneSOAR and OneShield—targets identity and permission management, attack‑path detection, document classification, automated threat response via natural‑language commands, and real‑time blocking of malicious prompts. The company demonstrated a humanoid robot receiving a digital employee ID through a Web3‑based identity system, illustrating how AI agents can be managed similarly to human staff. Solutions can be purchased individually, allowing firms to tailor the framework to their AI maturity and security needs.

At the SQUP conference, RaonSecure’s CEO Lee Soon‑hyung emphasized that AI security must move beyond external safeguards, noting that "if you cannot identify it, you cannot control it." The five solutions were presented as an integrated framework rather than stand‑alone products, according to CTO Kim Tae‑jin.

OneAccess provides Web3‑based digital identities for AI agents and robots, enabling granular permission management and traceability of instruction sources. OneHacker leverages AI to map potential attack vectors within an AI ecosystem. OneTag automatically tags documents by security level, while OneSOAR automates account and threat response actions through natural‑language commands. OneShield focuses on real‑time mitigation of malicious prompts and other active threats.

The demonstration featured a humanoid robot completing an authentication process to receive a digital employee ID, then verifying visitors’ mobile IDs, showcasing how AI entities can be managed similarly to human employees.

ソースの詳細: mk.co.kr ↗

なぜそれが重要なのか

As AI systems gain the ability to make autonomous decisions and interact with external services, traditional perimeter‑focused security models become insufficient. RaonSecure’s framework addresses this gap by extending identity and access controls to AI agents, a capability that could become a baseline for enterprise AI governance. By integrating behavior monitoring, data protection, and automated response, the suite offers a holistic approach that may reduce the risk of AI‑driven breaches, such as attacks or unauthorized data access. The use of Web3 for digital identities also introduces a novel method for tracking provenance and delegating authority, potentially influencing future standards for AI accountability and trust. If adopted widely, these tools could shape how organizations secure autonomous AI workloads, prompting competitors to develop comparable solutions and encouraging regulators to consider AI‑specific security requirements.

The suite directly tackles the emerging risk surface of autonomous AI agents, which traditional IT security tools are not designed to monitor or control. By providing identity, behavior, and data safeguards in a single package, RaonSecure offers a practical pathway for enterprises to achieve compliance with nascent AI governance expectations.

Web3‑based identity management could set a precedent for immutable, auditable AI provenance, supporting both internal governance and external regulatory audits. This approach may also influence future standards bodies that are currently debating how to certify AI behavior and accountability.

If the solutions prove effective, they could reduce the incidence of AI‑related security incidents, such as attacks that have plagued large language model deployments. This would lower operational risk for organizations deploying agentic AI at scale.

Interactive Mechanism

インタラクティブなメカニズム: 実際にどのように機能するか

この開発の背後にある基盤となるテクノロジーをインタラクティブに探索します。

Agent Lifecycle Stage:
1
User Intent & Planning: "Audit customer refund request #4092 and settle payment."
2
Tool Calling: Emits structured JSON call crm_get_transaction(id='4092').
3
Guardrail & Verification:🛡️ Paused: High-value action requires human operator sign-off.
4
Final Settlement: Refund recorded, email receipt dispatched, and audit log stored.
Core takeaway: An AI agent is not just a language model—it is a closed loop of planning, tool invocation, and environment feedback. Production systems require self-healing retries and strict human approval guardrails.
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What most distinguishes an AI agent from a basic chatbot?

次に見るべきもの

Key indicators to monitor include: (1) early adopters’ deployment timelines and any disclosed pricing or licensing models; (2) industry response, especially from sectors with high‑risk AI use cases such as finance, healthcare and critical infrastructure; (3) emergence of standards or regulatory guidance referencing AI identity and permission frameworks; and (4) any reported incidents that test the effectiveness of the OneShield real‑time threat‑blocking capability.

Pricing and licensing details have not been disclosed; monitoring RaonSecure’s announcements for commercial terms will indicate market positioning.

Adoption by large enterprises, especially those in regulated industries, will signal the perceived value of AI‑specific security controls.

Regulatory bodies may reference RaonSecure’s framework when drafting AI security guidelines, especially in jurisdictions focusing on AI accountability.

Performance data from real‑world deployments, particularly regarding OneShield’s ability to block malicious prompts, will be critical to assess the suite’s efficacy.

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