Aeon AI Risk Management

What Controlled AI Delivery Looks Like

Three anonymized examples show what Aeon helped decide, the evidence produced, and the measures used. Client-specific financial, security, and procurement metrics remain confidential.

AI workflow ROI and private-stack decision

Decision outcomes include a ranked build backlog and cloud, private, or hybrid workload routing. Evidence includes an ROI backlog, decision matrix, and owner-based 90-day plan.

LLM and MCP security before customer review

Decision outcomes include prioritized remediation and buyer-ready closure evidence. Measures include finding severity, trust boundary, time to remediation, and retest status.

Board-ready AI governance evidence

Decision outcomes include owned evidence gaps and a reusable control story. Measures include evidence completeness, open executive decisions, artifact reuse, and board-pack preparation effort.