Case Study: Operationalizing AI Risk Triage at Enterprise Scale

A single system to vet AI tools before they reach production — and show leadership exactly where the risk lives.

The Challenge

A large technology enterprise faced a surge in AI tool proposals from teams across the organization. Its review process was manual and couldn't keep pace, creating bottlenecks that slowed engineering down while leaving leadership with no clear view of where compliance risk actually sat. Risky tools and routine ones moved through the same slow queue, and no one could see the overall picture.

The Solution

I architected an AI Risk Triage Engine that vets every proposed AI tool before it reaches production — turning a slow, ad-hoc review into a structured, repeatable system, and giving leadership a live picture of risk across the organization.

• Risk scoring. Each AI tool is assessed across the factors that matter most — how sensitive the data is, how transparent the model is, and how much it could affect users — then sorted into clear risk tiers.

• Automated routing. The engine generates a standardized risk score and routes each proposal accordingly: high-risk tools go to specialized review before launch, while low-risk internal tools move quickly onto a fast track — so nothing risky slips through, and nothing routine gets stuck.

• Compliance heat map. The results roll up into a visual heat map that shows leadership, at a glance, where compliance risk is concentrated across the organization — making the invisible visible.

• Framework alignment. Scoring logic maps to recognized standards (NIST AI RMF, ISO 42001), so every decision is defensible and audit-ready.

  
  

The Impact

Replaced a fragmented, manual process with an auditable, enterprise-wide system for vetting AI before it goes live — turning weeks-long reviews into a fast, structured decision that teams can rely on.	75% faster reviews for new AI tools. What used to take weeks now takes minutes, so teams can safely start using low and medium risk AI tools without the wait.
Cleared engineering bottlenecks. Dramatically reduced review turnaround for new AI tools, so teams could safely start using low- and medium-risk tools without the wait.	5x more tools reviewed by the same team. A lean team can now track and assess hundreds of AI applications across the company, with no need for additional hires.
Scaled the team's reach. A lean team can now track and assess a far larger volume of AI applications across the company — significantly expanding coverage with no need for additional hires.	Returned engineering hours to product work. By replacing long, confusing security forms with an automated triage, developers stayed focused on building instead of losing time to compliance overhead.
Gave leadership a single, clear view of AI risk, turning scattered, invisible exposure into a heat map they could actually see and act on.	Turned compliance from a roadblock into a guide. Routine, low-risk tools get fast-tracked automatically, so employees get what they need right away while the security team focuses on the platforms that actually carry risk — showing teams how to use AI safely so they can innovate without guessing.