AI portfolio risk
Use-case map, sponsor, expected value, dependencies, criticality and criteria to stop, reframe or move to production.
ENTERPRISE AI AUDIT
Decide which AI initiatives deserve production
An executive audit to qualify AI portfolio risk, data maturity, governance, security and production readiness before committing budget, compliance exposure or teams.
AI maturity map
Business value
Risk
Data
Production
Pilot
Frame
Secure
Industrialize
Why audit now
Large organizations do not need more prototypes. They need a decision filter.
Identify AI initiatives that expose core systems, compliance obligations or sensitive data.
Separate opportunities ready for industrialization from ideas that still need evidence.
Give business, data, IT and security leaders a shared basis for decisions.
Accountability frame
A decision audit, not a regulatory certification
NeuroVista provides an independent, structured and actionable assessment. The findings support technical and executive decisions; they do not replace legal, financial, regulatory or certification audits.
Method: targeted interviews, document review, mapping of use cases, data, risks, vendors and production constraints.
Scope: findings, assumptions, unverified areas, ownership and expected evidence are made explicit in the final package.
Accountability: a NeuroVista lead owns the synthesis, audit limitations and prioritized action plan.
Audit scope
The assessment covers portfolio, data, controls and production architecture. The goal is not to promise ROI: it is to reduce uncertainty before an executive decision.
Governance and accountability
Decision roles, human validation, model policies, traceability, AI Act/GDPR requirements and legal alignment.
Data readiness
Availability, quality, freshness, lineage, access rights, sensitive data and real preparation effort before modeling.
Security and vendors
Prompt exposure, access, secrets, retention, AI vendors, data residency and integration risk.
MLOps and production
Observability, drift, evaluations, rollback, monitoring, inference costs and transition from pilot to operated service.
Investment due diligence
Executive view of risk, technical debt, critical dependencies, security effort and 30/60/90-day path.
Expected outputs
A decision package for executive, IT, data and security leadership
01
Value / risk / maturity matrix for each AI initiative
02
Risk register across data, model, security, compliance and production
03
Readiness scorecards for priority use cases
04
Limitations note: assumptions, missing evidence and associated ownership
05
Decision paths: stop, reframe, secure, industrialize
06
30/60/90-day plan with expected evidence and dependencies
Method
01
Inventory
AI portfolio, involved systems, teams, data, vendors, costs and security constraints.
02
Qualify
Risk level, evidence level, data maturity, production feasibility and defensible business value.
03
Arbitrate
Prioritized workstreams, go/no-go decisions and minimum conditions to move into production.
04
Secure
Short execution plan, ownership, compliance evidence and operational guardrails.
Built for contexts where mistakes are expensive
AI portfolio already started but difficult to read
Executive committee must prioritize AI budgets
CIO or CISO concerned about industrialization
Due diligence before investment, acquisition or partnership
Regulated organization or high sensitive-data exposure
Need to know which AI initiatives should move forward?
Share the context, constraints and decision level expected. We scope a short, readable and actionable audit.
Contact NeuroVista