Data, AI, and Technology Advisory for Australian Businesses
Make your business data safe, governed, and ready for AI.
Data Factorial helps Australian businesses make their data safe, governed, and ready for AI. That means mapping sensitive data and access, testing governance against real workflows, and naming vendor and AI risks before spend locks in.
Before you widen Copilot use, add automation, or plug another SaaS into client or regulated data, leadership needs a straight answer on visibility, access, and whether use is defensible.
Data & AI confidence
Signals we clarify
Structured visibility across exposure, control, vendors, AI use, and reporting. No synthetic scores or automated maturity badges.
Sensitive data visibility
Critical data, flows, and storage locations understood.
Access control confidence
Permissions and ownership aligned to business risk.
Vendor exposure
SaaS and supplier touchpoints mapped to sensitive data.
AI readiness
Guardrails and data fitness for intended AI use.
Executive reporting
Clear artefacts boards and ELT can act on.
The gap
AI is moving faster than most data governance models.
Teams adopt Copilot, SaaS, automation, and analytics faster than most governance maps keep up, so exposure and access drift out of view.
What we see in market
The mismatch between adoption speed and governance clarity shows up in predictable ways.
- Staff may already be using AI tools with business or client data.
- Sensitive information may be spread across SaaS platforms, shared drives, email, and cloud systems.
- Access controls may not reflect actual business risk.
- Customer due diligence and board questions are becoming harder to answer.
- Data ownership and quality issues can block AI adoption.
When to engage
You may need a data and AI risk review if...
You are rolling out Microsoft Copilot, ChatGPT Enterprise, AI agents, or workflow automation.
A customer, board, investor, insurer, or auditor is asking harder questions.
You do not know where all sensitive data is stored or shared.
You rely on many SaaS vendors but lack a clear data exposure view.
Your leadership team needs a practical 90-day roadmap, not a generic maturity report.
Who we serve
Organisations where sensitive data and AI decisions collide.
Leadership teams balancing customer diligence, board scrutiny, SaaS sprawl, and AI pilots without a single coherent risk narrative.
SaaS companies selling to enterprise
Answer enterprise questionnaires with coherent narratives before rework blows up the sales cycle.
- Customer due diligence and enterprise procurement cycles
- Trust and assurance questions from larger buyers
- Need a controlled narrative backed by how data is actually handled
Fintech, payments, lending, and financial services-adjacent businesses
Keep lending, payments, and identity workflows defensible while AI pilot work presses against confidentiality duties.
- Material data across transactions, identity, and operations
- Vendor and subprocessor chains that need explicit oversight
- Board and regulator-backed expectations on resilience and conduct
Professional services firms
Protect client material across documents, mail, collaboration tools, and pilot AI workflows without slowing legitimate delivery.
- Client confidentiality spanning many systems and channels
- Partner and matter-level accountability for data handling
- AI experimentation without clear enterprise guardrails
Health, care, education, and training providers
Digitise care or education journeys while keeping personal and sensitive information controlled across legacy and cloud systems.
- Personal and sensitive information in longitudinal records
- Mixed legacy and cloud environments
- Pressure to adopt AI and automation with duty-of-care expectations
Offers
Advisory offers with named outputs.
Data & AI Risk Discovery Workshop
A structured leadership session to align on sensitive data, AI use, vendor exposure, and what “safe and governed” means for your business.
Typical outcome: Shared priorities and a clear view of what to validate next before deeper assessment work.
Flagship offer
AI & Sensitive Data Risk Assessment
A focused assessment of sensitive data exposure, governance gaps, vendor touchpoints, and AI readiness. Outputs are formatted for executive use.
Typical outcome: A prioritised view of risk and a practical path to improve confidence before AI scales.
Enterprise Customer Trust Readiness Review
Prepare for enterprise procurement, security questionnaires, and customer diligence with evidence-backed clarity on data handling and controls.
Typical outcome: Clearer answers under customer and partner scrutiny without improvising in the sales cycle.
Vendor & SaaS Data Exposure Review
Map where SaaS and vendor relationships intersect sensitive data, access, and subprocessors, then spotlight blind spots that create exposure.
Typical outcome: A consolidated vendor touchpoint view leadership can govern and track over time.
Fractional Data & Technology Risk Advisor
Ongoing executive advisory to steer data governance, AI guardrails, vendor decisions, and risk reporting between major projects.
Typical outcome: Consistent decision support so data and AI risk does not drift between initiatives.
Compare scopes and outputs across workshops, assessments, and ongoing advisory.
Methodology
The D.A.T.A. Confidence Framework at a glance.
Discover, Assess, Transform, Assure: each phase produces artefact-backed visibility that boards and technical owners can share.
Discover
Identify critical data, systems, vendors, AI usage, stakeholders, and business goals.
Focus areas
- Critical data, systems, and vendors
- AI usage and stakeholders
- Business goals and engagement scope
Assess
Evaluate sensitive data exposure, governance maturity, access controls, architecture, vendor risk, and AI readiness.
Focus areas
- Sensitive data exposure and governance maturity
- Access controls and architecture
- Vendor risk and AI readiness
Transform
Build the roadmap, control uplift, governance model, data ownership model, architecture improvements, and operating cadence.
Focus areas
- Roadmap and governance model
- Ownership structure and control uplift
- Practical 90-day plan
Assure
Track progress, provide executive reporting, support board visibility, and maintain governance over time.
Focus areas
- Progress tracking and executive reporting
- Governance cadence
- Leadership visibility between milestones
Methodology page lists phase intent, activities, assessment lenses, and named deliverables.
Deliverables
Clear outputs your leadership team can act on.
Deliverables vary by scope. The aim is artefacts executives can use for decisions, not shelf-ware.
Executive risk summary
Data and AI readiness scorecard
Sensitive data exposure map
Vendor data exposure register
Governance gap analysis
Top 10 risk register
90-day remediation roadmap
Board or ELT decision pack
Experience
Senior advisory experience across data, AI, cloud, SaaS, cybersecurity, and regulated environments.
We tie data risk, architecture and vendor choices, AI readiness, and revenue blockers into language leadership teams can act on.
Executive-level technology and data advisory
Decisions where data, AI, platforms, and accountability meet; framed so directors can fund or halt work with clarity.
Practical architecture, governance, and risk experience
Clear-eyed view of how controls, vendors, and operating models behave day to day, not only how they read on paper.
Independent guidance above implementation delivery
Recommendations track leadership outcomes, not a reseller quota or fixed implementation roadmap.
Next step
Before you expose more data to AI, know what risk you are carrying.
Book a Data & AI Risk Discovery Call to clarify whether data is safe, governed, and ready for AI in your context.