Make ownership explicit
Define who can approve, operate, challenge and retire AI systems instead of leaving accountability between functions.
AI governance advisory in Australia
AI governance that enables responsible progress
Practical operating models, risk decisions and lifecycle controls for Australian organisations using AI, with support for global operations where required.
The objective
Governance people can operate, not a policy people avoid
See the AI estate
Create an inventory and classification method that covers internally built systems, embedded features and third-party services.
Apply controls by risk
Match assessment, testing, human oversight and monitoring requirements to the context and potential impact of each use case.
Keep evidence current
Build records, review triggers, incidents and change management into the lifecycle so governance continues after approval.
Two levels
Govern the organisation and assure the individual system
Portfolio-level accountability and system-level controls need to connect, but they solve different governance problems.
G / 01
Organisation
Create the governance operating model
For organisations that need clear accountability, decision rights, policies, inventory, risk tiers and portfolio oversight.
- Accountability, forums and decision rights
- AI inventory, classification and risk appetite
- Policy, standards, exceptions and reporting
G / 02
AI system
Assure individual use cases through their lifecycle
For teams that need a practical assessment and evidence pattern from design and procurement through deployment and review.
- Impact, risk and stakeholder assessment
- Data, security, testing and human oversight controls
- Monitoring, incidents, change and retirement evidence
Built for
The functions that share accountability for AI outcomes
Executives and risk leaders
Set accountability, risk appetite and decision forums that connect AI ambition with existing enterprise governance.
Product and technology teams
Translate governance expectations into usable design, testing, release and monitoring practices.
Security, privacy and assurance teams
Create a shared evidence model across data, cyber, privacy, procurement, compliance and responsible AI concerns.
Advisory scope
From accountability to evidence across the lifecycle
The work can focus on an enterprise operating model, a specific AI system or the interface between both.
Accountability and operating model
Place AI decisions inside a governance structure people can actually use.
- Executive accountability and delegated responsibilities
- Decision forums, escalation and challenge mechanisms
- Roles across business, technology, risk and assurance
Inventory and classification
Create visibility across the AI systems the organisation builds, buys and enables.
- AI system and use-case inventory
- Risk tiers, materiality and prohibited or restricted uses
- Ownership, purpose, users, data and supplier records
Impact and risk assessment
Assess each use case in its real context rather than through a generic technology checklist.
- Affected stakeholders and potential impacts
- Risk identification, treatment and acceptance criteria
- Framework and applicable-obligation mapping
Lifecycle controls
Define controls that can be implemented and evidenced by delivery teams.
- Data governance, privacy and cybersecurity
- Testing, evaluation, transparency and human oversight
- Release gates, monitoring and intervention mechanisms
Third-party and procurement governance
Treat supplier AI capability as part of the organisation’s risk surface.
- Supplier due diligence and responsibility boundaries
- Model, data, security and service evidence requests
- Contract, change and exit considerations with legal input
Evidence and continuous review
Keep the governance system useful as AI capability, use and risk change.
- Records, reporting and assurance evidence
- Incident, complaint and contestability processes
- Review triggers, change management and improvement cycles
Engagement brief
Designed around the decision the organisation needs to make
Scoping starts with the AI estate, stakeholders, current governance foundations and the decision or assurance gap that is creating risk.
- Entry point
- Enterprise operating model, governance uplift or system review
- Audience
- Executive, risk, technology, product, security, privacy and assurance teams
- Alignment
- Relevant frameworks and applicable organisational obligations
- Outputs
- Operating model, inventory, assessments, controls, evidence patterns or roadmap
- Boundary
- Advisory is not legal advice or formal management-system certification
Practice-informed
Governance grounded in how AI systems are actually built
Fedorai founder Arafat Tehsin combines solution architecture, applied AI leadership and hands-on delivery experience. That technical grounding helps translate governance expectations into controls, evidence and operating decisions delivery teams can apply.
About Arafat and Fedorai Questions
What organisations ask before engaging
Which AI governance frameworks can the advisory align with?
The work can map to relevant organisational and jurisdictional needs, including concepts from NIST AI RMF, ISO/IEC 42001 and Australia’s Guidance for AI Adoption. The final framework set is confirmed during scoping rather than imposed as a universal checklist.
Is AI governance advisory the same as legal advice or compliance certification?
No. Fedorai helps organisations design governance operating models, technical controls and evidence practices. Legal interpretation, regulatory opinions and formal certification remain with appropriately qualified legal, compliance and certification professionals.
Can you work with our existing policies and risk processes?
Yes. The preferred approach is to connect AI governance to existing enterprise risk, security, privacy, procurement, model risk, product and change-management practices where those foundations are effective.
Does the service cover generative AI only?
No. The operating model can cover predictive, decision-support, generative and agentic AI systems. Specific controls are tailored to the system type, context, users and potential impacts.
Can the advisory assess a specific AI system or project?
Yes. A system-level engagement can examine the use case, stakeholders, data, suppliers, model behaviour, human oversight, security, testing, monitoring and evidence before a major release or governance decision.
How long does an AI governance engagement take?
A focused system review or governance design sprint can be relatively short. An enterprise operating model involving multiple functions, policies and portfolio processes takes longer. Scope and sequence are agreed after the current state and decision need are understood.