Finally, a Practical RFP Template for AI Usage Control & Governance
Saw the piece on HackerNews regarding the new RFP template for AI Governance. It couldn't come at a better time. We finally have the green light and the budget to secure our AI initiatives, but as the article points out, the boardroom is struggling with the actual requirements. We know we need "AI Governance," but translating that into technical procurement requirements is a nightmare.
We need to move beyond vendor marketing buzzwords like "responsible AI" and enforce specific usage controls. One thing I'm insisting on in our RFP is granular egress control and shadow AI detection. If we can't see where the data is going or what models are being accessed, we can't approve the vendor.
I've started running detection queries internally to identify current shadow AI usage before we finalize our governance stack. Here is a KQL snippet I'm using to hunt for Generative AI traffic in our proxy logs:
DeviceNetworkEvents
| where Timestamp > ago(7d)
| where RemoteUrl has_any ("openai.com", "anthropic.com", "api.openai.com", "bard.google.com")
| project Timestamp, DeviceName, InitiatingProcessAccountName, RemoteUrl, BytesSent, BytesReceived
| summarize Count=count() by DeviceName, RemoteUrl
Additionally, we are scanning our codebases for hardcoded API keys that indicate developers are bypassing official channels. This Python script helps us flag potential secrets in repos:
import re
import sys
# Regex for common AI Provider Keys
patterns = {
"OpenAI": r"sk-[a-zA-Z0-9]{48}",
"Anthropic": r"sk-ant-[a-zA-Z0-9_-]{95}",
"HuggingFace": r"hf_[a-zA-Z0-9]{34}"
}
def scan_file(filepath):
with open(filepath, 'r', errors='ignore') as f:
content = f.read()
for provider, pattern in patterns.items():
if re.search(pattern, content):
print(f"[!] Potential {provider} key found in: {filepath}")
I'm curious—what are you all flagging as non-negotiable technical controls in your AI governance RFPs? Are you focusing on DLP for prompts, or is model lineage/auditability your priority?
Great timing on this post. We just went through this exact procurement cycle. The biggest hurdle was defining 'auditability.' We required vendors to provide full logging of prompt inputs and outputs, not just metadata. Some vendors balked at this due to privacy concerns, which immediately disqualified them. We also integrated a local LLM gateway (using OpenWebUI) to inspect traffic before it hits external APIs.
From a pentester's perspective, I'd recommend adding 'Prompt Injection Resistance' to your RFP technical requirements. Ask vendors how they handle untrusted data being injected into system prompts or retrieval-augmented generation (RAG) contexts. We've found that while DLP catches the obvious data leaks, indirect prompt injection is often overlooked until it's too late.
Solid Python snippet, OP. We expanded on that by integrating it into our pre-commit hooks. It's amazing how many devs try to 'borrow' a corporate OpenAI key for personal projects. On the RFP side, we added a requirement for 'Local Inference Support'—we don't want all our proprietary code sent to the cloud for processing, so we need vendors that support on-prem or VPC-deployed models.
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