기업 LLM, RAG, MCP, Agentic Workflow를 위한 AI 보안
AI 보안은 프롬프트 필터링을 넘어 운영 통제로 이동하고 있습니다. 모델 입력, 검색 컨텍스트, 도구 호출, 신원, 권한, 런타임 행동, 증거 전반의 가시성과 집행이 필요
AI 보안이란?
AI 보안은 AI-enabled system을 오남용, 조작, 데이터 노출, 모델 및 공급망 위험, 안전하지 않은 도구 실행, 거버넌스 실패로부터 보호하는 실무입니다. 생성형 AI에서는 LLM 앱 보안, RAG 보안, MCP 보안, AI 에이전트 보안, 런타임 모니터링, 컴플라이언스 증거까지 포함
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생성형 AI와 에이전트 워크플로우를 보안 아키텍처에 포함하려는 팀의 검색 의도
- AI 보안은 무엇을 포함하는가?
- LLM, RAG, MCP, AI 에이전트를 어떻게 함께 보호하는가?
- AI 보안 플랫폼은 LLM firewall과 어떻게 다른가?
- AI 보안 통제를 컴플라이언스 증거와 어떻게 연결하는가?
위험 영역
AI security spans application, data, model, identity, tool, and governance risk.
| 위험 | 중요한 이유 | Rutile 대응 |
|---|---|---|
| Prompt and context manipulation | Attackers influence model behavior through direct prompts, indirect content, or poisoned context. | Policy proxy and tool-call checks reduce operational impact. |
| Sensitive data exposure | Agents and LLM apps may expose PII, secrets, prompts, documents, or regulated records. | Scoped permissions, resource boundaries, and audit logs. |
| Uncontrolled autonomy | AI systems act without sufficient identity, approvals, or rollback paths. | Agent registry, JIT/JEA, approvals, monitoring, and kill switch. |
| Governance gaps | Teams cannot prove risk ownership, control coverage, or evidence against AI governance frameworks. | Framework mapping and reporting across agent actions. |
통제 계층
A practical AI security program needs multiple layers rather than a single prompt filter.
| 통제 | 구현 패턴 | Rutile 기능 |
|---|---|---|
| Inventory | Find AI apps, agents, MCP servers, data flows, and connected tools. | Discovery and Registry. |
| Access control | Limit who or what can invoke tools and resources, and for how long. | JIT/JEA Permission Broker. |
| Runtime policy | Evaluate requests, retrieved context, tool calls, and destinations before execution. | Policy Proxy. |
| Evidence | Record decisions, owners, permissions, outcomes, and exceptions. | Audit and Compliance Reporting. |
주요 1차 출처
These references anchor the page in recognized AI security guidance.
OWASP Top 10 for Large Language Model Applications
Defines critical LLM application risks including prompt injection, sensitive information disclosure, excessive agency, and vector or embedding weaknesses.
NIST AI RMF Generative AI Profile
Applies NIST AI RMF concepts to generative AI risks and mitigation practices.
Google's Secure AI Framework
Frames AI development, deployment, and operation through a security lens.
MITRE ATLAS
Documents adversary tactics and techniques against AI-enabled systems.
관련 AI 보안 토픽
AI 보안 FAQ
Is AI security only about prompt injection?+
No. Prompt injection is important, but enterprise AI security also covers data access, tool execution, identity, supply chain, retrieval, runtime control, monitoring, and governance.
Where does Rutile fit in an AI security stack?+
Rutile focuses on the identity, access, runtime enforcement, and audit layer for AI agents and tool-using LLM systems.