모델 컨텍스트 프로토콜 보안
MCP는 엔터프라이즈 AI 워크플로 전반에 걸쳐 정책 시행을 표준화하는 범용 보안 컨트롤 플레인 역할을 합니다.
This cheat sheet is designed for:
SOC Managers and Security Analysts looking to scale triage capabilities and slash investigation times without losing operational control.
Security Architects and Engineers deploying agentic AI workflows and connecting LLMs to live production infrastructure.
DevSecOps Professionals auditing code repositories and safeguarding the CI/CD pipelines powering AI integrations.
Incident Responders wanting to leverage AI as a secure "copilot" to aggregate data, map to frameworks like MITRE ATT&CK, and accelerate time-to-remediation.
What's included?
Threats to look out for when using MCP: A breakdown of critical high-impact risks, including a deep dive into CVE-2025-49596 and the pathways attackers use to compromise AI workflows.
Risk mitigation practices: Core technical controls, auditing guidelines, and policy-as-code strategies to enforce least privilege and keep humans in the loop for critical actions.
Anatomy of a strong SOC prompt: The 6 essential structural building blocks (Role, Action, Input, Constraints, Workflow, and Output) needed to keep AI models secure and predictable.
Common pitfalls to avoid: Critical warnings against vague formatting, over-permissioning, silent execution, and placing complete trust in unverified external data logs.
MCP’s security use cases + prompt examples: Production-ready prompt blueprints for automated alert triage, deep incident investigation modeling, and code repository vulnerability analysis.
MCP는 엔터프라이즈 AI 워크플로 전반에 걸쳐 정책 시행을 표준화하는 범용 보안 컨트롤 플레인 역할을 합니다.
This guide breaks down the most pressing risks and offers practical steps to secure MCP as it evolves.
Bring Wiz cloud security insights into your Notion workspace with Custom Agents — enabling automated reporting, investigation, and security workflows where teams already work.
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