Research on AI safety frameworks, Australia's Office of AI, safe AI adoption, and the implications of accelerating AI capability for organisational governance and board accountability.
AI safety is the discipline concerned with ensuring that AI systems behave as intended, remain under appropriate human control, and do not produce harmful outcomes — whether through technical failure, misuse, bias, or emergent behaviour that developers did not anticipate. At the organisational level, AI safety encompasses the policies, technical controls, testing practices, incident response protocols, and governance structures that ensure AI systems operate within acceptable boundaries. AI safety is distinct from but closely related to AI governance: governance provides the accountability and oversight structures that make safety possible, while safety requires specific technical and operational controls that governance frameworks alone do not specify.
The Australian regulatory environment for AI safety is developing rapidly. The Government's Office of AI, established under the Prime Minister, has made safety a central pillar of Australia's national AI strategy. Australia's voluntary AI Ethics Framework — eight principles covering human welfare, fairness, privacy, reliability, safety, transparency, contestability, and accountability — provides the current baseline. Sector regulators including APRA, ASIC, and the OAIC are progressively incorporating AI safety expectations into existing frameworks. Internationally, researchers including Daniel Kokotajlo (ai-2027.com, ai-2040.com) and AI safety practitioners including Jeffrey Ladish have raised substantive questions about the pace of AI capability development relative to safety infrastructure — questions that shape the policy and regulatory environment in which Australian organisations are making AI decisions. Strategen AI's safety research examines what these global developments mean for Australian boards and executives in practical terms: what frameworks to build, what boards need to know, and how to prepare for a regulatory environment that is moving towards greater safety requirements.
In the APIG framework, AI safety considerations cut across all four dimensions. Actors must be clear on who is responsible for safety decisions and oversight of high-stakes AI — including who exercises meaningful human control when AI systems make consequential decisions. Practices must embed safety reviews, incident reporting, human oversight checkpoints, and testing protocols into how work is done, not as isolated compliance activities. Infrastructure encompasses safety testing tools, model inventories, monitoring systems, and the control architecture that makes safety measurable. Governance provides the accountability structures — use-case approval processes with safety requirements, escalation protocols for safety failures, board reporting on AI safety posture — that ensure safety operates at the organisational level rather than resting on individual good intentions. Research in this hub examines how safety controls can be designed into each APIG dimension in ways that are appropriate to the organisation's AI maturity and regulatory context.
AI shifts destination management from reactive promotion to predictive intelligence, governance, and proactive visitor stewardship.
Category: Destination Intelligence | Year:
Australia needs sovereign AI infrastructure and models to reduce foreign dependency and retain strategic value.
Category: AI Sovereignty, National Capability | Year:
Agency as a Service reframes AI adoption as outsourcing organisational judgement, decision rights, and accountability.
Category: AI Governance | Year:
AI leadership appointments are governance decisions requiring better mandates, panels, and assessment criteria.
Category: Recruitment, AI Strategy, Operating Model | Year:
Agentic AI exhausts PPT by redistributing agency across humans, systems, practices, infrastructure and governance.
Category: AI Transformation Frameworks, Sociotechnical AI Governance | Year: