AI GOVERNANCE
AI governance for enterprise proposals
When proposal, sales, and InfoSec teams put generative AI to work on RFPs, security questionnaires, and DDQs, the hard question is whether you can trust the answer, explain where it came from, and defend it later.
Make AI speed safe to use
• Ground answers in approved content to prevent most errors at the source
• TRACE Score makes each answer explainable
• Human review keeps a person accountable
• Approval workflows and permissions produce the audit trail buyers expect
The first governance control is the source of the answer. General large language models are trained on broad data, which is where unsupported claims creep in.
Responsive's hallucination-prevention approach starts with retrieval-augmented generation against your curated content library. When answers come from approved content, a wrong answer usually means the library is out of date — a fixable content problem rather than an invented fact.
Configurable review workflows move responses through legal, technical, or executive checkpoints based on question type or sensitivity.
When someone asks who approved a security claim and on what basis, the answer lives in the system rather than an email thread.
