Your team shares data with AI every day. Can you see it?
Visibility, data protection, and privacy aren't just compliance requirements. They're how you protect your business, your clients, and your reputation.
Every team using ChatGPT, Claude, or Perplexity is sending data outside the organisation. Some of it's fine. Some of it contains client names, financial details, internal strategies, or personal information that should never leave your network.
Managing this comes down to three things: seeing what's happening, protecting what matters, and being able to prove you're in control. Whether a regulation requires it or not, that's just good practice.
It so happens that regulators agree. The EU AI Act, ISO 42001, and Australia's POLA Act all require exactly this. So if you get the fundamentals right, compliance comes along for the ride.
EU AI Act
Europe now requires what you should be doing anyway: transparency about how your organisation uses AI.
What it actually asks for
If your team uses AI tools like ChatGPT or Claude, the EU classifies you as a "deployer" of general-purpose AI. For most businesses, that means limited risk obligations: keep a record of what tools you use, what data goes in, and what oversight you have in place.
Article 52 specifically requires transparency about AI use. In practice, that looks a lot like the visibility you'd want regardless: an inventory of AI tools, documentation of data flows, and evidence that someone's paying attention.
If you already have visibility into your team's AI usage and controls protecting sensitive data, you're most of the way there.
ISO/IEC 42001
The international benchmark for managing AI responsibly. Not required. Increasingly expected.
Why would a 50-person company care?
Think of ISO 42001 as the AI version of ISO 27001 for information security. Nobody forced you to get ISO 27001 either, until a client's procurement team asked for it.
The standard covers risk assessment, data protection, transparency, and continuous improvement. Full certification costs real money and involves external audits. But you don't need the certificate to follow the framework. Having an AI systems inventory, documented risk controls, and monitoring evidence already puts you ahead of most businesses your size.
And when a client asks "how do you manage AI risk?", being able to show them actual data beats a policy document every time.
POLA Act (Australia)
Your team pasting client data into ChatGPT is already a cross-border disclosure under Australian law. This isn't a future risk.
This one's already live
The Privacy and Other Legislation Amendment Act 2024 took effect in December 2024. Since June 2025, individuals can sue organisations for up to $478,550 for serious privacy breaches. That's not a regulatory fine. That's a personal lawsuit from someone whose data your team shared with an AI platform.
The AI-specific deadline is December 2026: any organisation covered by the Australian Privacy Principles must disclose when automated processes are used in decisions affecting individuals. If your team uses AI to draft client advice, assess applications, or generate recommendations, this applies.
But here's the bit most people miss: existing obligations under APP 1.2, APP 5, and APP 8 already cover data shared with overseas AI platforms. The privacy risk isn't coming. It's already here.
Three things every business using AI should manage
Forget the regulations for a moment. If your team uses AI tools, these are the fundamentals. Get them right and the regulatory requirements take care of themselves.
Visibility
Which AI tools does your team use? How often? What kind of work goes into them? You can't protect what you can't see, and most businesses have no idea what's actually happening.
Data protection
Stop sensitive information from reaching AI platforms. Client names, financial data, credentials, personal details. Catch it at the point of entry, before it becomes a problem.
Evidence
Audit trails, usage reports, and documented controls that prove your protections work. Not a policy document collecting dust. Living evidence from day-to-day operations.
The EU AI Act, ISO 42001, and POLA all require exactly these three things. The frameworks are catching up to what good businesses already know they need to do.
Why your business needs this, regardless of regulation
Regulations aside, these are real risks happening right now inside your organisation.
Data is leaving your network every day
Your team is pasting client information, project details, and internal strategies into AI tools. Every prompt to ChatGPT or Claude sends data to a server outside your control. Without visibility, you have no idea what's going out the door.
Clients will ask, and you need an answer
Professional services firms, agencies, and tech companies are already getting asked by clients: "How does your team handle AI?" Saying "we have a policy" isn't enough anymore. They want to see data. The businesses that can show it win the work.
One incident can damage trust permanently
It only takes one incident: client financials pasted into ChatGPT, patient details shared in Claude, proprietary code dropped into Perplexity. That kind of data leak can destroy the trust you've spent years building. Preventing it is always cheaper than cleaning it up.
Regulators are catching up to these risks. The EU AI Act (enforcement from August 2026), ISO 42001 (increasingly expected in procurement), and Australia's POLA Act (already in force, with automated decision rules by December 2026) all require the same fundamentals: visibility, data protection, and evidence of controls. But even without a single regulation, managing these three things is just good business.
How Vireo Sentinel maps to all three
Built for visibility and protection. The regulatory mapping is a bonus.
Vireo wasn't built to tick compliance boxes. It was built to give you visibility into how your team uses AI and protect your data in real time. But when you have genuine visibility and real controls, the regulatory boxes get ticked as a side effect.
AI Systems Inventory
Automatically built from real usage data. Every AI tool, every user, usage categories, and risk classifications. Satisfies EU AI Act Article 52, ISO 42001 Clause 4.3, and POLA APP 1.2.
Data Leakage Prevention
50+ detection patterns identify sensitive data in real time, before it reaches AI platforms. Intervention workflows give your team options rather than blocks. Documented risk controls with measurable effectiveness rates.
Compliance Reports
One-click reports mapping your data to all three frameworks. AI systems inventory, risk assessment, control effectiveness, and residual risk analysis. Export as PDF. Hand it to whoever's asking.
One set of tools, three frameworks covered
The requirements overlap more than they differ. If you have visibility into AI usage, controls protecting sensitive data, and audit trails proving it works, you're covering the core of EU AI Act, ISO 42001, and POLA with one platform and one set of evidence.
Common questions
Over 50 detection patterns covering personal information (names, emails, phone numbers, national IDs), financial data (credit cards, bank accounts, tax file numbers), technical credentials (API keys, passwords, access tokens), and confidential business information (client names, project codes). Detection happens in the browser before anything reaches the AI platform, so the data never leaves if your team chooses to remove it.
That's where most data leakage actually happens. The prompt might look harmless: "Draft a reply to John Smith about the Anderson account." But it just sent a real person's name and a client relationship to an external AI platform. Multiply that across every person on your team, every day, and the exposure adds up fast. The risk isn't the tool. It's what goes into it.
Absolutely. Most of our users don't start with a regulatory requirement. They start because they want to know what their team is actually doing with AI and make sure sensitive data isn't leaking out. That's a business risk question, not a legal one. The reporting tools are there if you ever need them for an audit or client request, but the core value is visibility and protection.
Setup takes about 10 minutes. Install the browser extension, invite your team, and usage data starts flowing immediately. Most teams see a full picture of their AI activity within the first week. The dashboard shows which tools are being used, how often, what categories of work, and where sensitive data has been detected.
Probably not yet. Full certification involves external audits and real investment. But aligning with the framework, having documented AI policies, risk controls, and monitoring evidence, gives you most of the benefit without the cost. When a client asks "how do you manage AI risk?", showing them actual usage data and intervention reports beats a policy document every time.
See what your team shares with AI. Protect what matters.
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