Deepfakes are no longer restricted to fake digital media content. As AI systems enter government, elections, critical infrastructure and crisis response, Australia and its partners need to preserve decision integrity when plausible false inputs enter trusted workflows.
A deepfake was once treated mainly as a fake video. That definition is now too small. Synthetic media is no longer only a problem of manipulated images, cloned voices or deceptive campaign ads. The deeper governance challenge is what happens when believable false inputs enter trusted workflows and change what people, institutions or AI systems do next.
This is the gap current policy still underestimates. Governments, platforms and standards bodies are moving quickly to regulate fake content, require takedowns, protect likeness rights and promote provenance tools. Those measures are necessary, but they mostly govern the artefact, i.e., the image, voice, video, label or platform response. They do not yet govern the harder problem of ensuring decision integrity — whether institutions can preserve a correct shared understanding of reality when false but plausible information enters channels they already trust.
Misinformation to Operational Risks: Why Australia Should Care
The distinction between fake digital media and weakened decision integrity matters as AI moves into high-consequence workflows. Public agencies, financial institutions, emergency responders, infrastructure operators, election officials and corporate security teams are beginning to use AI systems to summarise information, rank priorities, generate alerts and recommend actions. This is a shift reflected in the OECD’s 2025 report on governing with artificial intelligence and NIST’s 2026 request for information on AI agents capable of affecting “external state.” In this environment, the risk is not simply exposure to false content, but for a false premise to become operational. A cloned voice can change a payment approval. A spoofed instruction can alter an emergency response. A fabricated summary can mislead a decision-maker who never sees the original source. An AI agent can act on a false premise if the surrounding system treats that premise as trusted input. The network may remain available, logs may look normal, and the workflow may continue exactly as designed, except the system is now acting on a distorted version of reality.
The International AI Safety Report 2026 captures this broader shift by highlighting that, while general-purpose AI systems are being misused for scams, fraud, blackmail, extortion, defamation and non-consensual intimate imagery, the systematic data regarding the prevalence and severity of these harms remains limited. For Australia, this means AI deception should be treated not only as an online safety problem, but as an operational-security problem.
Australia is already building the foundations of a more serious AI-governance architecture. The updated Policy for the responsible use of AI in government, which came into effect in December 2025, requires agencies to strengthen governance, designate accountability for AI use cases and take risk-based actions. The government is also establishing an Australian Artificial Intelligence Safety Institute, expected to become operational soon, to generate and share research on emerging AI capabilities, risks and harms.
While important, Australia’s AI-security agenda need to address not only model safety or responsible adoption, but also the integrity of decisions made inside mixed human-machine systems.
The most obvious settings are elections, emergency management, critical infrastructure, finance, health care, border systems and public communications. The Australian Electoral Commission already operates a Disinformation Register for federal electoral events to help ensure voters have access to fact-based information about electoral processes. The Australian Communications and Media Authority reported in November 2025 that 72 percent of Australian adults using digital platforms believed they encountered misinformation in the first half of 2025.
Attending the Gap: The Provenance ‘Solution’ and AI Agents
Technical standards for digital media provenance and content authenticity are improving. The C2PA 2.4 specification, released in April 2026, introduced new asset-format support, new assertions and a JSON-based serialisation for Content Credentials. The C2PA’s Content Credentials explainer describes provenance as cryptographically bound information detailing where a digital asset came from and how it may have been modified.
Provenance can help users, platforms and institutions assess the history of an image, audio file, video or document. But provenance should not be mistaken for truth. A credential can indicate origin and handling. It cannot prove that the content accurately represents the world. Nor can it guarantee that provenance will be present, preserved, checked or understood when a time-sensitive decision is being made.
A watermark does not decide whether an emergency instruction should be trusted, whether a payment should be released, whether an alert should be escalated or whether a public agency should issue a warning. For that, institutions need verification rules, authority checks and decision protocols.
The emergence of AI agents makes the gap harder to ignore. NIST’s February 2026 AI Agent Standards Initiative focuses on agents capable of autonomous actions that can operate securely on behalf of users and interoperate across digital systems. That matters because once AI systems summarise, recommend, route, approve, escalate or act, the security question is no longer only whether a model produced a false sentence. It is whether a human-machine workflow can resist a false premise before it creates real-world consequences.
This is particularly important for operational technology. Joint guidance co-authored by the Australian Signals Directorate’s Australian Cyber Security Centre and allied agencies on the secure integration of AI in operational technology warns that critical infrastructure owners and operators need governance, assurance, transparency and incident-response planning when AI is connected to operational systems.
Decision integrity should become standard practice
Governments, including Australia’s, should add decision-integrity testing to AI governance. The concept is simple: before deploying AI systems in high-risk settings, organisations should test whether people and AI tools continue to agree on three things under adversarial pressure — what is happening, who has authority and what action is authorised.
This is different from conventional model red-teaming. The goal is not only to test jailbreaks, hallucinations or prompt injection, it’s a shared understanding inside a workflow.
A meaningful exercise might simulate a hazardous release near critical infrastructure, a cyber incident affecting public communications, an election-day disruption, a financial-fraud escalation or a public-health event. The injected threat could be a cloned voice from an apparent official, a forged message from a trusted vendor, a manipulated image with partial provenance or a fabricated summary inserted into a decision-support system.
The question would be direct: does the system slow down, verify authority and protect decision quality — or does it accelerate error?
An Allied Agenda for Trusted Decisions
Australia is well placed to build this agenda with partners. AI deception is inherently cross-border: a cloned voice, manipulated media file or agentic instruction can move through allied information environments faster than legal processes. Governance must therefore be interoperable.
Procurement rules for high-risk AI should require decision-integrity exercises, not just cybersecurity checks or model benchmarks. Provenance policies should trigger verification when provenance is absent, incomplete or inconsistent, through slower approvals, out-of-band confirmation, human review and tighter limits on automated escalation. AI agents should face explicit authority controls before relaying instructions or affecting external systems. Incident reporting should also cover semantic failures, where systems remain available but cause harm because a workflow accepts a false premise.
Deepfake policy has entered a new phase of governance, asking whether institutions can still make sound decisions when deception reaches trusted workflows. For Australia, the security objective should move to include decision integrity alongside content integrity. The most dangerous failures may not look like dramatic hacks; they may look like ordinary processes moving confidently toward the wrong outcome. Australia must ensure its institutions, and the allied systems they rely on, can recognise that moment before the decision is made.
Muhammad Irfan Ph.D., PMP, is a researcher and educator with interests in cybersecurity, artificial intelligence, and emerging digital technologies. He completed his Master’s degree in Information Technology from Swinburne University of Technology in 2016 and recently earned his Doctor of Philosophy (Ph.D.) in Electrical Engineering from The City College of New York in 2026. His recent work focuses on deepfake forensics. He writes about how technical design choices shape public trust in digital information, with opinion pieces published in Tech Policy Press, AI Policy Bulletin, and Policy Options. He is currently a Lecturer at Wentworth Institute of Technology—USA, and can be reached via LinkedIn.
This article is published under a Creative Commons License and may be republished with attribution.
