Use cases

# Use Cases Parmana is used anywhere AI systems trigger real-world actions that require authorization, control, and auditability. --- ## 1. AI Payment Authorization ### Scenario An AI agent proposes a payment: - vendor payout - customer refund - subscription charge adjustment ### Problem Without verification: - payments may be triggered incorrectly - fraud risk increases - no deterministic approval trail exists ### With Parmana - AI generates payment request - Governance evaluates authorization policy - Execution only happens if approved - Every transaction is cryptographically signed --- ## 2. AI Infrastructure Changes ### Scenario AI systems manage cloud infrastructure: - scaling servers - deploying services - modifying production configs ### Problem - accidental deployments - lack of approval traceability - unsafe automation loops ### With Parmana - every change requires authorization decision - policy defines safe deployment boundaries - execution is enforced deterministically --- ## 3. Healthcare Workflow Authorization ### Scenario AI assists in: - patient escalation - diagnostic recommendations - treatment approvals ### Problem - compliance requirements - high-risk decisions - audit requirements ### With Parmana - every action requires policy-based authorization - decisions are fully traceable - audit trails are cryptographically verifiable --- ## 4. AI Agent Tool Execution ### Scenario AI agents call tools: - APIs - external services - internal systems ### Problem - agents can trigger unintended actions - no centralized authorization layer - unpredictable execution behavior ### With Parmana - every tool call is validated - execution requires Authorization Decision - unsafe actions are blocked deterministically --- ## 5. Enterprise Automation Systems ### Scenario Enterprises automate: - finance workflows - HR actions - internal approvals ### Problem - fragmented approval systems - inconsistent enforcement - audit complexity ### With Parmana - centralized deterministic authorization - unified policy enforcement - full execution traceability --- ## 6. AI Agent Platforms ### Scenario Companies building AI agents that: - take autonomous actions - interact with APIs - perform multi-step workflows ### Problem - lack of safety boundary - no execution governance - inability to prove correctness ### With Parmana - agents generate signals only - governance decides execution rights - runtime enforces strict boundaries --- ## Core insight Across all use cases:

AI proposes actions — Parmana decides whether they are allowed to execute.

--- ## Summary Parmana is required wherever: - AI systems trigger real-world effects - execution must be controlled - compliance and auditability matter - deterministic authorization is required