AI governance is the set of controls a company puts around how its employees use AI tools like ChatGPT, Claude, Gemini, and GitHub Copilot: redacting sensitive data before it reaches the AI provider, seeing how AI is actually being used, and owning the accounts the company pays for. It sits alongside - not instead of - the AI provider's own terms and policies.
Why the existing security stack doesn't see it
Traditional security controls inspect data at known egress points - email gateways, USB ports, SaaS upload scanners - using keyword and pattern matching. They were built for a world where exfiltration meant attaching a CSV to an email or uploading a folder to cloud storage.
AI usage looks completely different. The same employee who would never email a customer list to a personal address will happily paste 50 rows of it into ChatGPT to "draft a summary". The payload is in the request body sent to the AI provider, encrypted with TLS, and looks identical on the wire to any other HTTPS request. None of the existing stack was built to look inside the prompt box.
How an AI governance agent works
A governance layer for AI usage has three building blocks:
- Device-level interception. A small agent runs on each employee's laptop and watches outbound HTTPS traffic to a known list of AI providers. It only looks at AI traffic - the rest of the network keeps flowing untouched.
- Provider-aware prompt extraction. Every AI tool shapes its request differently. The agent understands each tool's request format well enough to find the user-authored prompt text, and ignore the surrounding plumbing (model name, tool definitions, message identifiers).
- Hybrid detection and redaction. A fast pattern layer catches high-confidence strings (emails, credit cards, API keys, JWTs, private-key blocks). A contextual layer catches the harder cases - "our Q4 revenue was", layoff plans, customer names mentioned in passing. Detected spans are replaced with placeholders before the request reaches the AI provider.
The agent intercepts on the device; the redaction itself runs in NexusNest's service (or the customer's own deployment) in flight, before the request reaches the AI provider. The employee keeps using their AI tool exactly as before, and the provider never sees the sensitive original.
What AI governance should not do
- Block. Hard-blocks push people to personal devices and unmanaged accounts where a company has zero visibility. Redact and log instead.
- Retain original prompts. Wherever redaction happens, the original sensitive text should never be stored - it should be discarded the moment the redacted version is computed. Insist on a no-retention guarantee.
- Slow the person down by more than a few hundred milliseconds. Anything else and people will route around it.
What to look for in a product
- Coverage of the AI tools your team actually uses today.
- A redaction layer where the redacted prompt is what reaches the AI provider, and the original is discarded, not retained.
- Categories that map to your real data - personal information, financial and health data, credentials, source code.
- An audit log your team can actually use during a review - which person, which tool, which categories, when.
- A self-hosted option if you're regulated.
Rolling out AI governance at your company? NexusNest's device agent routes ChatGPT, Claude, Gemini, and Copilot traffic through redaction before it reaches the provider, with no policy work to set up. See how it works →
Sources & further reading
- OWASP - Top 10 for LLM Applications (LLM06: Sensitive Information Disclosure)