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Use AI to identify risks and generate assessment recommendations.
This MCP tool is from an official registry, open source, and recently updated, with no declared secrets or remote endpoints; no clear high-risk red flags are evident. The main concern is its local code-execution capability combined with minimal documentation and no declared license, so transparency is limited and isolated use is advisable.
The materials explicitly state that no keys or environment variables are required, and no API tokens, account credentials, or other sensitive authentication requirements are described, so credential exposure and abuse risk appears low.
No remote host endpoints are declared, and the README provides no indication of external connectivity; based on the available materials, there is no factual basis showing user data is sent to third-party services.
The system checks explicitly mark this tool as having executes-code capability, indicating it can execute code locally or spawn related processes; this is a standard sensitive MCP capability and warrants least-privilege, isolated execution.
The materials do not state that it reads or writes specific local files, databases, system directories, or cloud resources, and no data permissions beyond its stated purpose are described; no sign of overbroad access is evident from the current evidence.
Positive factors include an official registry listing, an auditable open-source repository, and updates within the last year; however, the README is effectively empty, no license is declared, and community adoption is 0 stars, limiting audit context and real-world validation.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.CSOAI-ORG/risk-assessment-ai-mcp" MCP server from askskill: Run: claude mcp add 'io-github-csoai-org-risk-assessment-ai-mcp' -- npx -y risk-assessment-ai-mcp
Based on the following system change description, identify deployment risks, rate them as high/medium/low, and provide mitigation suggestions: We are migrating the payment service to a new Kubernetes cluster and upgrading the database version.
A structured risk assessment with key risks, severity ratings, impact scope, and mitigation actions.
Assess the main risks in this project plan: Launch a new membership feature in two weeks with 2 frontend engineers, 1 backend engineer, and 1 QA, while requirements may still change. List schedule, resource, and quality risks.
A project risk list with causes, possible consequences, and recommended priorities.
Review the possible compliance and security risks in this data handling process and suggest improvements: After users upload identity information, the files are stored for 180 days and processed by an external OCR service.
A compliance and security risk analysis highlighting sensitive points and safer process recommendations.
Helps users perform AI security governance and NIST RMF compliance analysis.
Use AI to analyze data privacy risks and support compliance tasks.
Use AI-powered ops tools for monitoring, troubleshooting, and automation.
Predict customer churn risk from business data and support retention planning.
An AI MCP server for recruitment-related workflows.
Helps teams check EU AI Act requirements and compliance guidance.