AI Safety & Governance Diagnostic

A 30–45 day assessment of how AI/ML systems introduce risk in your environment, how that risk is managed today, and what must change.

AI introduces distinct operational and governance risks: opaque model behavior, emergent failure modes, data provenance and drift, and unclear accountability for decisions made or influenced by automated systems. This diagnostic produces an executive-ready view of current controls, gaps, and priority actions—grounded in your actual use cases and operating reality.

At a glance
  • Designed for: Organizations with meaningful AI/automation exposure and unclear control maturity
  • Typical duration: 30–45 days
  • Engagement level: 1–2 days/week (variable by scope)
  • Primary sponsor: CISO, CTO, Head of Risk, Head of Data/AI
  • Primary outcome: Executive-ready findings, control gaps, and prioritized actions
AI Safety & Governance Diagnostic graphic

AI Safety & Governance Diagnostic – Clarify how decisions, risk, and AI safety are governed.

Ideal Fit

This engagement is designed for leaders who need clarity on AI risk posture, control maturity, and ownership.

  • AI/ML is in production or moving quickly toward production without a clear governance model
  • Risk ownership across product, engineering, security, legal, and compliance is unclear
  • Leadership needs a defensible view of current controls and gaps before scaling usage
  • AI vendors or internal teams are moving fast, but auditability and oversight are limited
  • Safety, privacy, and security concerns exist without a concrete action plan

Diagnostic Scope

The diagnostic focuses on real use cases, real data flows, and the controls that determine safety and accountability.

  • Inventory of key AI/ML use cases (internal and customer-facing)
  • Mapping of data inputs, outputs, decision points, and human oversight
  • Control review: access, logging, monitoring, change management, and incident response
  • Risk review: privacy, security, misuse/abuse scenarios, drift, and quality degradation
  • Governance review: decision rights, RACI, escalation paths, and documentation standards
  • Vendor review (if applicable): responsibilities, evidence, and contractual control alignment

Delivery Approach

The work progresses from scoping to control assessment to an actionable governance plan.

Define Scope and Priority Use Cases

Confirm the AI/ML landscape that matters: systems, data, and high-impact use cases. Align on what ‘acceptable risk’ means and which decisions must be governed..

Map Data Flows and Control Points

Document how inputs, outputs, and decisions move through the system. Identify the control points that determine auditability, accountability, and safe operation.

Assess Controls and Gaps

Evaluate current practices against practical control requirements, not theoretical frameworks. Identify gaps that create meaningful risk or prevent defensible oversight.

Deliver Governance Actions and Ownership

Produce prioritized actions with clear ownership, sequencing, and implementation guidance. Define governance structure, decision forums, and evidence required for ongoing oversight.

The practical outcome: a clear map of AI risk, current controls, and the few concrete steps that matter most in the next quarter.

Engagement Shape

  • Typical duration: 30–45 days
  • Engagement level: 1–2 days per week (depending on scope)
  • Mode: Rapid assessment + actionable governance design

Deliverables

Deliverables are designed to be used by executives, security/risk owners, and implementation teams.

Executive summary of risk posture, control maturity, and key gaps
AI use-case inventory with risk classification and ownership
Data-flow and control-point maps for priority systems
Data-flow and control-point maps for priority systems
Governance model: decision rights, RACI, forums, and required evidence
Implementation-ready next steps (30/60/90 day plan)

Engagement Outcomes

Leaders gain a defensible view of AI risk and a practical plan to improve oversight.

Clear ownership and decision rights across AI systems and use cases
Improved auditability and visibility into AI behavior and operational risk
A prioritized control roadmap tied to real systems and constraints
Reduced exposure to unmanaged drift, misuse, and silent failure modes
A governance cadence that can scale with adoption

Example scenario

Context

A product organisation has integrated both third-party and in-house models into customer-facing features and internal tooling. Different teams are using different providers and patterns. There is no central view of where sensitive data might be flowing, which models are in use, or how AI-driven behaviour is monitored. Board questions about “AI risk” are increasing, and a major customer has asked for details on AI governance.

Engagement

Nova Inizio runs a focused diagnostic: builds an inventory of key AI/ML use cases and data flows, identifies realistic threat scenarios, reviews current controls and policies, and consolidates findings into an executive-ready scorecard, risk view, and prioritized hardening actions.

Result

Leadership gains a clear, shared view of AI usage and risk, with a concrete 30-day hardening plan and a small number of structural changes agreed upon. Risk, security, and product teams have a common language for AI risk, and the organization can respond credibly to board and customer questions.

Relationship to other services

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A short working session to clarify current state, constraints, and the fastest credible path forward.

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