Securing AI in Your Business
Why This Matters Now
Every previous technology wave – email in the 90s, smartphones in the 2000s, cloud computing in the 2010s – gave businesses years to develop security controls and best practices.
AI is different. The pace of change is unprecedented.
New AI capabilities emerge monthly, sometimes weekly. Your competitors are already using AI to work faster, serve customers better, and cut costs. You must keep pace – but security threats have multiplied just as fast.
Gartner warned in December 2025 that AI tools built into web browsers are automatically sending sensitive user data – browsing history, open tabs, active content – to vendors' cloud servers, often without users realising it. Australia's Cyber Security Centre's 2024-25 Annual Cyber Threat Report warns that businesses must ensure a "safe and secure approach" when integrating AI technologies. The Australian Government's Critical Infrastructure Annual Risk Review states clearly: "Realising the opportunities of AI will require mitigating the new threats it introduces."
The risks are real: data leaks to competitors, unauthorised access to company information, customer data exposed through AI queries, and new entry points for cyber attacks.
This isn't the time to wait for mature controls to emerge. Your business needs a framework NOW that protects you while allowing you to compete.
The good news: we can adapt proven security frameworks to address AI across three critical areas where your business operates every day.
1. Online and Web Browsing Security
What this covers: How your team accesses cloud applications (e.g. Microsoft 365, finance systems like SAP or Dynamics 365) and browses the internet.
Traditional security focus: Employees accessing malicious websites, downloading viruses, or using unauthorised cloud applications.
How AI has changed the game: Your employees now use dozens of AI tools daily – ChatGPT or Copilot for writing emails and proposals, AI research assistants, AI presentation generators, AI meeting summarisers. Each interaction could expose sensitive business data.
Here's what most employees don't realise: When they paste a customer contract into ChatGPT to "make it sound more professional," or ask an AI tool to "analyse this pricing spreadsheet," that data is being sent to the AI vendor's servers. Gartner's research shows these AI tools capture and transmit your browsing data, active content, and whatever information employees input – creating exposure risks that simply didn't exist two years ago.
Real-world risk: An employee uses a free AI tool to help draft a proposal, pasting in your pricing strategy, customer names, and competitive positioning. That information now sits on the AI vendor's servers. Under many free AI services' terms, they can use this data to train their models – meaning your confidential business information could potentially be exposed to other users or even competitors using the same AI tool.
What we can control:
Discover unauthorised AI use – Identify what AI tools your team is using without IT approval. Many employees install AI browser extensions or sign up for AI services without realising the security implications. Detection tools can help find these unauthorised AI applications (often called "shadow AI" because they're operating in the shadows) so you can make informed decisions about what should be allowed. (by Shadow AI detection and reporting, AI usage monitoring and controls)
Block sensitive data in AI queries – Implement filters designed to detect and help prevent employees from accidentally pasting customer information, financial data, pricing strategies, employee records, or other confidential details into AI tools. These systems can recognise patterns of sensitive information before it leaves your network. (by AI prompt protection, Data Loss Prevention for AI interactions, Inline inspection of AI tool traffic)
Track AI usage – Gain visibility into who's using which AI tools, how often, and for what purposes. This helps you understand productivity gains while monitoring for appropriate use. (by AI usage monitoring and controls, API-based monitoring of AI tool usage)
Identify risky AI tools – Assess each AI application your team wants to use, scoring them based on their security practices, data handling policies, and reputation. Tools can flag high-risk applications for review before they're approved. (by Risk scoring for AI applications, Application Confidence Scorecards for AI/online apps, AI Security Posture Management – AISPM)
Business impact: Your team can use powerful AI productivity tools more safely – with controls designed to reduce the risk of leaking customer data, pricing strategies, competitive intelligence, or confidential information to outside vendors or competitors.
2. Company Servers (Private Network, Internal AI Tools)
What this covers: Your internal network – file servers, databases, document management systems, time tracking software, customer relationship management (CRM) systems, and any AI tools or services your organisation runs internally.
Traditional security focus: Preventing employees from accessing internal systems they shouldn't, and keeping external attackers from breaching your network to steal data.
How AI has changed the game: Many businesses are now deploying internal AI tools – like a private ChatGPT instance for your company, or AI assistants that help employees find information faster. These AI tools need to access your internal systems to be useful. An AI assistant helping your sales team might need to search your document management system, pull data from your CRM, or analyse files on shared drives. Each connection point creates potential security concerns.
Real-world risk: You deploy an AI assistant to help your sales team work more efficiently. It's configured to access customer contracts and proposals. But if not properly controlled, that same AI tool might also be able to access HR salary data, financial projections, or M&A documents it has no business seeing. An employee asks the AI a simple question, and it pulls information from restricted areas of your network.
Consider this scenario: A sales representative asks your internal AI assistant, "What's our typical discount for enterprise customers?" The AI, if not properly restricted, could search through everything – including the confidential pricing proposal you're preparing for your biggest prospect, or the board-level financial forecasts that should only be accessible to executives.
What we can control:
'Need-to-know-only' access for AI tools – Implement access controls so AI applications only get access to the specific data they're configured to need, nothing more. Just like you wouldn't give every employee access to every file in the office, AI tools can be configured without blanket access to your entire network. Your sales AI can be set to read customer contracts but not HR files. Your document AI can search approved folders but be blocked from financial planning documents. (by Zero Trust access for internal AI tools, Secure access controls for AI agents)
Define precise AI system connections – Specify exactly which internal systems each AI agent can connect to, and monitor for configuration changes to limit risk of unapproved changes. This helps ensure AI tools stay within their designated boundaries. (by Secure access controls for AI agents, MCP Model Context Protocol security)
Connection monitoring – Track how internal AI tools are connecting to and using your data. Monitoring tools can help detect unusual access patterns – like an AI tool suddenly accessing far more files than normal, or attempting to reach restricted areas. (by API-based monitoring of AI tool usage)
Business impact: You can deploy AI tools that access your internal data to boost productivity and help employees work smarter – with controls designed to limit AI tools' access to only what they need. Your project management AI can be configured to read project files without accessing financial records. Your document assistant can search contracts without seeing employee personal information. Your team works faster, with layers of protection for your sensitive data.
3. User Desktops (Installed Applications, Local AI Agents)
What this covers: The software installed on each employee's computer or laptop – from Microsoft Office to specialised business tools, and now increasingly, AI assistants and agents that run directly on their machines.
Traditional security focus: Preventing employees from installing unauthorised software that could contain viruses, consume excessive computer resources, or create security vulnerabilities.
How AI has changed the game: AI agents installed on desktop computers can be extraordinarily powerful – and risky. Unlike cloud AI tools where you can control what data leaves your network, desktop AI agents operate locally and can directly access everything on that computer.
AI assistants like Claude Desktop, GitHub Copilot, or Microsoft Copilot can read files, modify documents, write and execute code, access applications, and interact with anything stored on the computer – all based on how they're configured and what permissions they're given.
The acceleration problem: New AI desktop tools launch every month. Your technical staff may be installing them to work faster without going through IT approval, creating unauthorised AI installations on devices throughout your organisation.
Real-world risk: A developer installs an AI coding assistant to help write software faster – a legitimate productivity tool. With proper controls, it only accesses the project folders it needs. Without controls, it can access the entire hard drive.
Here's what could go wrong: The developer asks the AI to "help me find that configuration file." The AI searches everywhere – and stumbles across an HR folder containing everyone's salary information that happens to be saved on that laptop. Or it finds database credentials saved in a config file. One poorly worded question ("show me all the sensitive files on this computer") could expose everything.
The licensing risk most businesses miss: An employee uses a free AI tool to analyse a spreadsheet containing customer data – names, emails, purchase history. They think they're just getting help organising information. What they don't know: the tool's terms of service (buried in the fine print they didn't read) allows the vendor to use that data to improve their AI model.
Your customer data is now potentially part of that vendor's training dataset – possibly exposed to other users, potentially violating your privacy commitments to customers, and definitely beyond your control. Business-grade licenses typically prevent this, but only if you're actually enforcing approved software.
What we can control:
Approved software only – Maintain and enforce a list of AI applications that have been reviewed and approved for installation. Device management tools can help detect and flag unauthorised AI tools. This doesn't mean saying "no" to everything – it means evaluating each tool properly before it's deployed across your organisation. (by Install only approved AI software)
Proper business licensing – Verify that AI tools are properly licensed so your data is intended only to provide services to you – not to train vendor AI models. This helps ensure that business-grade licenses are in place and that free consumer tools aren't being used with company data. (by Licensing of installed AI agents so Licensee Data is only used to provide Services to the Licensee)
Standardised setup rules – Establish standard configurations for approved AI tools across every computer. This includes defining what folders they can access (project files, yes; entire hard drive, no), what actions they can take (read files, yes; delete files, requires approval), and what data they can process (public information, yes; customer database, no). Configuration management helps maintain these standards. (by AI Agent Access Control – configuration management, MCP Server Security – configuration management)
AI system access inventory – Maintain a register of which computers have AI agents with direct access to files and systems, and regularly review what they're allowed to access. If an employee leaves or changes roles, you know exactly what AI tools they had configured and can revoke access appropriately. (by Setup and maintain a register of MCP servers)
Keep AI software current – Regularly update AI tools to address newly discovered security weaknesses. AI software is evolving rapidly, and vendors frequently release security patches. Staying current helps reduce exposure to known vulnerabilities. (by Standard software update and patch management practices)
Business impact: Your employees can access AI productivity tools that help them work faster and smarter – with controls designed to reduce the risk of accidental data loss, unauthorised data sharing with AI vendors, or security breaches from misconfigured AI agents. You get the potential competitive advantage of AI with multiple layers of protection against nightmare scenarios.
The reality check: Desktop AI is the least mature area of AI security. Tools are evolving rapidly, standards are still being developed, and many businesses don't even know what AI software is installed on employee devices right now.
Our approach: Start with visibility (audit what's out there), standardise on approved tools, implement basic configuration controls, and build from there. You don't need perfect security on day one – you need a framework that can improve continuously while letting your business move forward.
How These Three Areas Work Together
These three security areas create "defence in depth" – multiple lines of protection working together.
An employee might try to use an unauthorised cloud AI tool → controls at the web browsing layer can detect and block it before data leaves your network.
Or they might install an unapproved desktop AI agent → device management tools can detect and flag it for removal.
Or an internal AI tool might try to access data it shouldn't → server-level access controls can prevent unauthorised access.
The framework is designed to catch risks at multiple levels. If one control is bypassed or misconfigured, others are still working to protect your data.
The bottom line: You can't wait for perfect AI security solutions for years. But you can implement practical frameworks today that let you use AI competitively while managing the risks more intelligently. That's what this three-area approach is designed to deliver.
If you would like some help, please contact us using the details below.
Note: the scope of this article was about security of how employees use AI tools, but a business might also be interested in defending public-facing AI apps from abuse or helping developers build AI services securely.
