AI Risk Management Consulting
Treat LLM risk as four problems you can score — data, actions, vendors, and drift — then put gates on each before production traffic.
- Service
- Governance
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
AI risk management consulting identifies and controls the four LLM risk planes that actually halt programs — data exposure, tool actions, vendor terms, and quality drift — then implements traces, eval gates, and human checkpoints in your cloud, typically in four weeks for one system or 8–12 weeks under financial-services review.
Why teams pick this engagement
Governance × EnterpriseThreat model first
We write who can attack, what they want, and which tools they can reach before a red team or a launch date. Residual risk is a number with an owner.
Risk you can fail a release on
Golden sets and rubric judges turn leakage, tool abuse, and groundedness into CI gates. A risk register that cannot fail a build is a narrative.
Client cloud, client IP
Assessments, traces, and control configs stay in your VPC. Zero-retention, no shared training, SOC 2-aligned process. You keep the artifacts.
CIO, CISO, and second line
The package is written for the people who will be asked: data classification, vendor terms, action permissions, and monitoring — not a model-card dump.
Weeks, with a known FS path
One bounded system is four weeks. Financial-services engagements with heavier model-risk review typically take 8–12 weeks.
Reconstructable decision logs
Every in-scope run stores input, retrieval, tools, model version, and output so an incident can be replayed without guesswork.
Key takeaways
- 01
Most LLM programs fail risk review on data paths, write-capable tools, vendor training terms, or unmeasured drift — not on model brand.
- 02
A useful AI risk register scores each system on those four planes, names an owner, and points to a control that can fail a release.
- 03
Reconstructable traces are the evidence layer: without them, residual risk is an opinion.
- 04
Golden sets, rubric judges, and CI gates are how you notice a silent model update before a customer or a regulator does.
- 05
Work runs in the client cloud. Client owns IP. Zero-retention, SOC 2-aligned, no shared training.
What the engagement covers
How we work
- 01
Discover
Interview CIO, CISO, and system owners. Map data, tools, vendors, and existing monitors. List the questions that currently have no answer.
- 02
Design
Threat model, residual register, control pattern, and eval-gate criteria reviewed before implementation.
- 03
Build
Implement logging, permissions, and the first risk-linked eval suite in your cloud.
- 04
Validate
Reconstruct a simulated incident, confirm gates fail known-bad changes, and walk the evidence with second line.
- 05
Enable
Handover of register, runbooks, and a 30-day on-call window so risk operations continue without us in the loop.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
CIO LLM Risk Question Bank
The questions we use in discovery: data classes, tool blast radius, vendor training terms, drift monitors, and who can halt a system.
Get the questions ·AI Risk Register Worksheet
Score data, action, vendor, and drift risks per system, with residual rating, owner, and control mapping.
Get the worksheet ·