The hidden legal risk in your AI workflow

On 10 June 2025, Australia introduced a new statutory tort for serious invasion of privacy that enhances an individual’s rights if their privacy is intentionally or recklessly breached, even if no harm has occurred. It’s a landmark shift; but for organisations undergoing digital transformation, it’s also a legal trap hidden inside their own tech stack.

Here’s the simple version: if your organisation collects personal data and something goes wrong, even unintentionally, you could now be held accountable if that data use is considered a ‘serious’ invasion of privacy. And in practice, the term ‘serious’ is not precisely defined, leaving its interpretation to the courts. According to one law firm, factors that may influence this determination include the degree of offence, distress or harm caused, and whether the defendant knew or ought to have known that their actions would likely cause such effects.

But the danger isn’t just legal; it’s also structural. The way most organisations have built digital processes over the past decade has created what I’d call a ‘governance gap’. Personal data flows invisibly between SaaS platforms, automation tools, and now AI models, often without clear oversight or auditability. No one’s malicious, but no one’s entirely sure who’s responsible, either.

Nowhere is this risk more acute than in the rise of autonomous AI agents.

These agents — self-directed programs that complete tasks by reasoning, chaining actions, and retrieving data on their own — are being rapidly embedded into organisations’ systems. In theory, they boost productivity. In reality, they introduce a level of unpredictability that existing governance frameworks simply weren’t built for.

It’s created what some call the ‘lethal trifecta’; AI agents operating across three technical areas: tools (like the ability to send emails or make purchases), memory (recalling previous instructions or context), and self-improvement (rewriting their own code or refining goals over time). When these come together, the result isn’t just automation, its autonomy.

From a privacy standpoint, this creates serious risk. An AI agent that pulls personal data into a task it wasn’t intended for — or that stores sensitive information in an unapproved location — may not feel like a breach in the traditional sense, but under the new tort it could well be. You can’t claim you didn’t know. You can’t claim you didn’t mean to. You have to prove the system was designed to act reasonably.

That’s a high bar. And it’s one that most AI deployments, especially those built around speed, novelty or experimentation, are unlikely to clear.

The challenge is that many of these tools operate across different systems: your CRM talks to your marketing engine, your AI assistant talks to your document store, your ERP feeds data into an analytics dashboard. What looks seamless from a user perspective is actually a tangled web of integrations that often lack proper access controls, data handling policies, or escalation logic.

So what can organisations do?

First, treat explainability as a core design principle, not a compliance afterthought. If you can’t trace what your agent did, when it did it and what data it touched, you’re exposed. Second, reframe AI and automation as balanced conversations, considering both governance and innovation. That means involving legal, compliance and security stakeholders from the start, not looping them in once a project is already live.

And third, pressure test your data architecture. Where is personal data flowing? What assumptions have your systems made about user consent, retention and classification? If those assumptions are wrong — or worse, invisible — the legal consequences are now real, not theoretical.

This tort won’t stop AI adoption. But it will force a reckoning. Organisations are rapidly embedding intelligent systems into everything from call centres to HR to supply chains. The opportunity is huge, but so is the risk of treating privacy as a checkbox instead of a foundational design element. It’s not the AI. It’s the architecture. And right now, too many organisations are layering automation on top of ambiguity, with no visibility, no audit trail and no plan for when it all goes wrong. *Tony Butler is the Managing Director of data and analytics consultancy Decision Inc. Australia.

Top image credit: iStock.com/da-kuk