Engagement
AI Asset & Shadow-AI Audit
A fast, focused audit for businesses that have already started building AI.
Published by Crucible Studio
Who this is for
You're past the pilot stage. You have AI tools in use, projects in flight, and data flowing into models in ways you may not fully map. That's a good thing. It's also where the risk quietly compounds: nobody can say with confidence what AI is running in your organisation, what data it touches, or what you'd find if the regulator, the board, or a breach asked.
This audit is for organisations like yours. It is not a "should we do AI?" assessment. You've already answered that.
It answers a harder question: what is actually running, what data is it touching, and where are the gaps you can't yet see?
The problem you're sitting on
Most started organisations don't have a missing-capability problem. They have a visibility and control problem, and it shows up in three ways:
- Unmapped AI.Teams have stood up models, copilots, and LLM integrations across the business. No one holds a complete inventory of what's running, where it came from, or who owns it.
- Shadow AI.People are using consumer and third-party AI tools with company data, often without IT or risk knowing. Data may already have left the building.
- Mismatched controls.The governance you have was built for traditional models. It's either too heavy for the low-risk tools (slowing the business) or missing the AI-specific risks entirely (no output logging, no drift monitoring, no incident path).
You may not be able to answer three basic questions today: What AI are we running? What data does it touch? What would we find if it were audited or breached? This audit makes those answers available.
What the audit covers
1 AI Asset Inventory
A complete map of every AI system, model, LLM integration, and copilot in use, with its owner, purpose, data dependencies, and vendor. The single source of truth you currently don't have.
2 Shadow-AI & Data-Exposure Assessment
Where consumer and unapproved AI tools are being used, what data is exposed, and how significant the leakage is. A quantified view of the confidentiality and data-protection risk, not a guess.
3 Governance & Control Gap Analysis
Your current model-risk and data controls assessed against the AI-specific risk profile. Where they're adequate, where they're over-applied, and where the AI-specific gaps are (output logging, monitoring, incident response, provenance).
4 Risk Tiering
Every asset classified on the two-axis tier model (consequence × controllability) so you can see, at a glance, which of your systems are actually high-risk and which of your controls are aimed at the wrong things.
5 Priority Remediation Plan
A sequenced list of the highest-leverage fixes, starting with the live risks (shadow AI, data exposure) and ending with the structural ones (governance architecture). Each item is scoped so you can act on it without a full program.
The deliverables
- AI Asset & Shadow-AI ReportThe complete inventory and data-exposure findings.
- Risk-Tiered Asset MapEvery system classified, with the risk posture made visible.
- Control Gap AssessmentWhere your governance is adequate, over-applied, or missing.
- Board-Ready Risk SummaryA short, decision-ready view of AI risk and the top asks.
- Priority Remediation RoadmapWhat to fix first, and why.
Why this, why now
You don't have a "do AI or not" question anymore. You have a "what's actually running, and can we stand behind it" question. The organisations that get ahead in the AI era are the ones that can answer that question to their board and their regulators with confidence.
This audit is the fastest, lowest-commitment way to get that answer.