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AI Agent Write-Path Security & Governance Survey
Where are your AI agents currently operating?
A
Read-only path (searching docs, internal QA)
B
Early write-path (updating non-critical tools like Jira or Slack)
C
Production write-path (mutating CRMs, databases, codebases, or financial systems)
D
Not deploying agents yet
What is your single biggest concern regarding AI agent security/compliance?
A
Lack of immutable audit logging for compliance
B
Risk of agents taking unauthorized write actions / hallucinations
C
No standard way to enforce Human-in-the-Loop (HITL) approvals
D
Vendor lock-in with native agent frameworks
Where should cross-boundary policy enforcement live in your architecture?
A
At the native agent framework level (e.g., Copilot Studio, LangChain)
B
At an independent, cross-platform policy gateway (like an MCP sidecar or proxy)
C
At the System of Record / API level (e.g., Salesforce, ERP backend)
How are you currently handling Human-in-the-Loop (HITL) approvals?
A
Asynchronous UI cards / approval webhooks (Teams, Slack)
B
Blocking step requiring manual database/CRM approval
C
Custom internal dashboard built by our platform team
D
Relying on system prompts / No formal approval workflow yet
For agents built outside .NET stacks (TypeScript/Python), what is your supported enforcement point for pre-execution policy checks before a tool call mutates an external system?
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