AI usage visibility
Assess where AI tools are actually being used across teams and workflows, including usage that has never been formally approved.
Executive AI governance visibility
AI Readiness.Measured. Managed. Mastered.
NetSentinel delivers the AI governance visibility your business needs to adopt AI confidently and stay ahead of risk.
AI adoption usually starts before governance does. This service closes that gap with visibility, priority, ownership, and practical next steps.
What the service does
What the service does
The question leadership needs answered is not “is AI useful”. It is “what are we already exposed to, and who owns it”.
This service produces a structured, defensible answer that supports policy, approvals, vendor decisions, and board conversation.
For the full landing overview, see the AI readiness & governance assessment. For the wider risk picture, see technology risk assessment services.
It helps leadership understand:
- where AI is already being used, approved or not
- what business data may be entering AI tools
- which policies, approvals, and owners are missing
- which AI vendors create concentration or contractual risk
- what leadership should decide before adoption expands further
Who it is for
Who it is for
This service suits organizations where AI usage is moving faster than governance.
It is a strong fit for:
- executives whose teams are already using AI without a formal policy
- leadership groups being asked to approve AI tools or budget
- internal IT and security leaders who need governance language for the business
- MSPs and consultants advising clients on safe AI adoption
- organizations preparing for client, insurer, or board questions about AI use
What we assess
Seven AI readiness domains
Each domain is assessed on its own, then read together so leadership sees where governance is weakest relative to actual usage.
Governance and policy
Assess acceptable-use policy, approval paths, exceptions, ownership, and review discipline against how AI is really being adopted.
Data exposure
Assess where business, client, or regulated data may be entering AI systems, and whether classification and handling rules exist.
Vendor and third-party risk
Assess AI vendors, contractual terms, data handling commitments, and concentration risk in the platforms the business depends on.
Operational readiness
Assess whether processes, accountability, escalation, and quality control are ready for AI to be used in real work.
Identity and access readiness
Assess whether permissions, identity design, and integrations control what AI systems can reach inside the environment.
Reporting and leadership oversight
Assess whether leadership receives a consistent, usable view of AI usage, risk, and accountability over time.
What you receive
What you receive
Output written for decision-makers, not for a model evaluation team.
Depending on scope, the engagement supports:
- an AI readiness score with domain-level detail
- a shadow-AI and data-exposure findings summary
- prioritized governance gaps with business consequence
- vendor and dependency risk observations
- a practical policy and ownership starting point
- executive- and board-ready reporting
AssessSeven domains reviewedEvidence
ScoreReadiness 72 / 100Score
Identify exposureShadow AI + data pathsFindings
PrioritizeConsequence rankedPriority
Assign ownershipNamed accountabilityActions
Executive reportingBoard-ready outputReport
Leadership decisionGovern, approve, or pauseDecision
Raw findings → executive outputs → a decision leadership can defend
Engagement
How the engagement works
- 01
Establish adoption context
Understand what the business wants from AI, what is already happening, and where leadership feels uncertain.
- 02
Assess the readiness domains
Work through usage visibility, governance, data exposure, vendor risk, operations, access, and oversight.
- 03
Translate findings into governance decisions
Turn scattered AI activity into consequence, priority, ownership, and a defensible position for leadership.
- 04
Agree the next steps
Use the outputs for policy work, approval design, vendor review, or a deliberate pause on expansion.
This is an assessment and governance decision-support engagement. It is not an AI build or managed service.
Scope
What NetSentinel is — and is not
What NetSentinel is
NetSentinel is the executive layer above IT. For AI, that means showing where usage, data, and vendors create risk, and what leadership should govern first.
What NetSentinel is not
NetSentinel does not:
- penetration test AI models
- audit or certify model behavior
- provide legal or regulatory opinion
- build or operate AI systems for you
- monitor AI usage continuously
- replace internal IT or your MSP
Why this matters now
Adoption is already happening. Governance usually is not.
By the time leadership starts asking questions, AI use is often widespread and undocumented.
The practical consequences:
- AI usage spreads faster than policy, approvals, or ownership
- sensitive data enters tools nobody formally reviewed
- vendor commitments are assumed rather than read
- leadership cannot answer basic questions from clients or insurers
- adoption decisions get made without a shared view of the risk
Right tool, right problem
Right tool, right problem
AI model evaluation
Use model evaluation when the goal is testing accuracy, bias, or behavior of a specific model.
Legal or regulatory review
Use counsel when the goal is a formal opinion on regulation, contracts, or liability.
Implementation partner
Use an implementation partner when the goal is building, integrating, or operating AI systems.
NetSentinel
Use NetSentinel when the goal is executive visibility into AI risk, governance maturity, ownership, and safe next steps.
Related reading: cybersecurity risk assessment services, the six-pillar framework, and engagement pricing.
FAQ
Common questions
Bring AI use out of the shadows before it becomes an incident.
If leadership needs a clear view of AI usage, data exposure, and governance maturity, this is where the conversation should start.