The recent ABC reporting on VIQ Solutions is a useful case study for any organisation thinking about AI governance, data risk and third-party services.
30 Second Take
The issue is not only about one supplier.
It is about visibility. And it is just as relevant for government and non-government entities alike.
Multiple government agencies had used VIQ Solutions for transcription-related services, including agencies dealing with sensitive legal, tax, defence and public service matters. This includes concerns about sensitive court files being accessed offshore through a transcription service arrangement, with key questions being asked about privacy, confidentiality, national security and supplier oversight.
This is exactly the kind of scenario all organisations need to pay attention to and be asking the really hard questions of management of their ability to know their AI governance controls/protocols and risk assessment process.
“Overall, this issue demonstrates there’s a weakness in our processes and protocols … How can one company so expose all these departments to a security risk?”
The risk is not always obvious
Not every service is an AI governance issue.
But modern services increasingly contain technology, automation, analytics or AI-enabled capability that may not be obvious to the organisation buying the service.
This risk does not only apply to government. It applies to corporates, universities, insurers, health providers, professional services firms, not-for-profits and medium-sized enterprises.
AI and automation can sit inside vendor platforms, outsourced processes, document workflows, recruitment systems, customer service tools, cyber tools, analytics platforms and employee productivity applications.
The organisation may not have built the tool. But it may still be relying on it.
Too often I have heard senior people within organisations talk about embracing AI, and that others are doing it without a problem.
Very often this takes the form of language of:
“Why are we setting the bar so high, they are already using it elsewhere”
“I seem the only one tying to make this happen“
“It seems like we are trying to find a way to say ‘no'”
This type of sentiment does not come with any evidence of the AI governance, just observational comments.
The real governance question
The key governance question is no longer:
“Are we building AI?”
The better question is:
“Where are we or will be rely on AI, automation or data-processing technology — directly or through a third party?”
That distinction matters.
Accountability does not disappear when a service is outsourced:
If sensitive information is processed offshore, the organisation still has a governance issue.
If a vendor uses AI or automated processing, the organisation still needs to understand the risk.
If data is accessed by subcontractors, the organisation still needs assurance.
If customers, citizens, employees, legal matters or business-critical decisions are affected, the organisation still needs evidence that controls are working.
Where organisations are exposed
This is where many organisations may be weaker than they realise, relying on traditional policies and processes and not recognising AI is different, such as:
Procurement processes, but not AI-specific questions.
Privacy policies, but not a clear AI posture and inventory.
Vendor contracts, but limited visibility of subcontractors, data flows or automated processing, or specific AI terms and conditions.
Risk framework, but no consistent way to assess AI-enabled services before they become embedded in operations.
AI governance has to reach beyond policy documents and technology approvals.
It needs to connect procurement, privacy, cyber security, legal, risk, operations, data governance, business ownership and assurance.
Two assessments are needed
Organisations need to assess AI risk at two levels.
First, they need to assess their organisation-wide AI governance maturity.
This means looking at leadership, accountability, policy, inventory, data governance, privacy, cyber security, third-party management, training, monitoring and assurance.
Second, they need to assess each AI initiative or AI-enabled service.
This means understanding the purpose, data, users, affected stakeholders, level of automation, human oversight, vendor reliance, compliance obligations and potential impact if something goes wrong.
Both views matter.
A mature organisation can still approve a poorly controlled AI use case.
The questions leaders should be asking
A practical AI governance approach should help leaders answer simple questions:
Where is AI, automation or advanced data processing being used?
What data is involved?
Who can access it?
Where is it stored or processed?
Are third parties or subcontractors involved?
What decisions, services or records does it support?
What could go wrong?
Who is accountable?
What evidence shows the risk is being managed?
These questions should not wait until after a problem appears.
They should be built into governance, procurement, project approval, vendor management, risk assessment and assurance.
Lesson for every organisation
The VIQ Solutions case is a reminder that AI and technology risk may not arrive through a major transformation program.
It may already be sitting inside a service your organisation uses every day.
The lesson is simple.
Do not only assess the AI tools you build. Assess the services you use, the vendors you rely on, the data flows you create, and the maturity of the whole organisation to govern AI and technology-enabled risk.
Because in modern organisations, AI risk is not always visible at the surface.
Good governance starts by knowing where to look.
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Strategy & governance
AI use-case ownership, accountability and board or executive visibility.
Strategy & ownership · Q1
AI use cases are identified, documented and owned by the business.
Strategy & ownership · Q8
Accountability is clear across business, risk, compliance, technology and executive teams.
Human oversight · Q10
Board or executive reporting includes AI risk, maturity and responsible-use progress.
EmergingAd hoc or not yet consistent
DevelopingSome practices exist but are uneven
ManagedDefined and mostly embedded
AdvancedMature, monitored and improving
Risk, data & third parties
Risk assessment, escalation, data/privacy/security review and third-party AI oversight.
Assessment & escalation · Q2
AI risks are assessed before pilots, procurement, deployment or material change.
Assessment & escalation · Q3
High-risk AI use cases are escalated for senior approval before they go live.
Data, privacy & security · Q4
Data, privacy, cyber and information-security risks are reviewed before AI tools are used.
Third-party AI · Q6
Third-party AI tools, vendors and embedded AI features are assessed before use.
EmergingAd hoc or not yet consistent
DevelopingSome practices exist but are uneven
ManagedDefined and mostly embedded
AdvancedMature, monitored and improving
Oversight, monitoring & controls
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Human oversight · Q5
Human oversight is defined for AI-supported decisions or outputs that matter to customers, staff or operations.
Monitoring & controls · Q7
AI controls are monitored after implementation, not only checked at launch.
Monitoring & controls · Q9
AI incidents, errors, complaints or unintended outcomes are captured and reviewed.
EmergingAd hoc or not yet consistent
DevelopingSome practices exist but are uneven
ManagedDefined and mostly embedded
AdvancedMature, monitored and improving
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Managed, with clear gaps
Governance and monitoring are forming, but third-party AI and data/privacy review need stronger consistency.
Capable but informal
Responsible AI maturity
Uncontrolled experimentation
Policy theatre risk
Responsible-use behaviour ↑
Formal governance / controls →
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Assessment & escalation55%
Data, privacy & security48%
Human oversight58%
Monitoring & controls72%
Third-party AI38%
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Human oversight and responsible decisioning4 / 5 • 2 plans
Monitoring, incidents and control review3 / 5 • 1 plan
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Governance foundations and decision rights
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