Raposa Aval: Authorization and Audit Controls for AI AgentsRaposa Aval: Authorization and Audit Controls for AI Agents
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Darius Ceponas
• Nov 2
🦊 Raposa Aval — The Trust Layer for AI Agents
AI agents can automate complex workflows, but sensitive actions require more than autonomous execution. They need explicit authorization, enforceable policies, and verifiable audit trails.
I developed Raposa Aval as a human-approval and execution-control layer for AI agents and MCP-based systems.
Key capabilities:
Human-in-the-loop authorization for sensitive operations
Policy-driven approval and execution controls
Protection against replayed, expired, or modified approvals
Traceable decisions and verifiable audit evidence
Integration with multi-agent workflows and external tools
Raposa Aval is part of the DC ESCRYPT architecture for building accountable, secure, and reliable autonomous AI systems.
The goal: Give AI agents the freedom to operate within clearly defined, independently verifiable boundaries.
#AIAgents #MCP #Cybersecurity #AIAutomation #SoftwareArchitecture
Data EngineeringSoftware ArchitectureSoftware DevelopmentClaudeOpenAI
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