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We all agree that public AI tools are fantastic for general tasks such as brainstorming ideas and working with non-sensitive customer data. They help us draft quick emails, write marketing copy, and even summarise complex reports in seconds. However, despite the efficiency gains, these digital assistants pose serious risks to businesses handling customer Personally Identifiable Information (PII). Most public AI tools use the data you provide to train and improve their models. This means every prompt entered into a tool like ChatGPT or Gemini could become part of their training data. A single mistake by an employee could expose client information, internal strategies, or proprietary code and processes. As a business owner or manager, it’s essential to prevent data leakage before it turns into a serious liability. Financial and Reputational Protection Integrating AI into your business workflows is essential for staying competitive, but doing it safely is your top priority. The cost of a data leak resulting from careless AI use far outweighs the cost of preventative measures. A single mistake by an employee could expose internal strategies, proprietary code, or sensitive client information. This can lead to devastating financial losses from regulatory fines, loss of competitive advantage, and the long-term damage to your company's reputation. Consider the real-world example of Samsung in 2023 . Multiple employees at the company's semiconductor division, in a rush for efficiency, accidentally leaked confidential data by pasting it into ChatGPT. The leaks included source code for new semiconductors and confidential meeting recordings, which were then retained by the public AI model for training. This wasn't a sophisticated cyberattack, it was human error resulting from a lack of clear policy and technical guardrails. As a result was that Samsung had to implement a company-wide ban on generative AI tools to prevent future breaches. 6 Prevention Strategies Here are six practical strategies to secure your interactions with AI tools and build a culture of security awareness. 1. Establish a Clear AI Security Policy When it comes to something this critical, guesswork won’t cut it. Your first line of defence is a formal policy that clearly outlines how public AI tools should be used. This policy must define what counts as confidential information and specify which data should never be entered into a public AI model, such as social security numbers, financial records, merger discussions, or product roadmaps. Educate your team on this policy during onboarding and reinforce it with quarterly refresher sessions to ensure everyone understands the serious consequences of non-compliance. A clear policy removes ambiguity and establishes firm security standards. 2. Mandate the Use of Dedicated Business Accounts Free, public AI tools often include hidden data-handling terms because their primary goal is improving the model. Upgrading to business tiers such as ChatGPT Team or Enterprise , Google Workspace , or Microsoft Copilot for Microsoft 365 is essential. These commercial agreements explicitly state that customer data is not used to train models. By contrast, free or Plus versions of ChatGPT use customer data for model training by default, though users can adjust settings to limit this. The data privacy guarantees provided by commercial AI vendors, which ensure that your business inputs will not be used to train public models, establish a critical technical and legal barrier between your sensitive information and the open internet. With these business-tier agreements, you’re not just purchasing features; you’re securing robust AI privacy and compliance assurances from the vendor. 3. Implement Data Loss Prevention Solutions with AI Prompt Protection Human error and intentional misuse are unavoidable. An employee might accidentally paste confidential information into a public AI chat or attempt to upload a document containing sensitive client PII. You can prevent this by implementing data loss prevention (DLP) solutions that stop data leakage at the source. Tools like Cloudflare DLP and Microsoft Purview offer advanced browser-level context analysis, scanning prompts and file uploads in real time before they ever reach the AI platform. These DLP solutions automatically block data flagged as sensitive or confidential. For unclassified data, they use contextual analysis to redact information that matches predefined patterns, like credit card numbers, project code names, or internal file paths. Together, these safeguards create a safety net that detects, logs, and reports errors before they escalate into serious data breaches. 4. Conduct Continuous Employee Training Even the most airtight AI use policy is useless if all it does is sit in a shared folder. Security is a living practice that evolves as the threats advance, and memos or basic compliance lectures are never enough. Conduct interactive workshops where employees practice crafting safe and effective prompts using real-world scenarios from their daily tasks. This hands-on training teaches them to de-identify sensitive data before analysis, turning staff into active participants in data security while still leveraging AI for efficiency. 5. Conduct Regular Audits of AI Tool Usage and Logs Any security program only works if it’s actively monitored. You need clear visibility into how your teams are using public AI tools. Business-grade tiers provide admin dashboards, make it a habit to review these weekly or monthly. Watch for unusual activity, patterns, or alerts that could signal potential policy violations before they become a problem. Audits are never about assigning blame, but identifying gaps in training or weaknesses in your technology stack. Reviewing logs might help you discover which team or department needs extra guidance or indicate areas to refine and close loopholes. 6. Cultivate a Culture of Security Mindfulness Even the best policies and technical controls can fail without a culture that supports them. Business leaders must lead by example, promoting secure AI practices and encouraging employees to ask questions without fear of reprimand. This cultural shift turns security into everyone’s responsibility, creating collective vigilance that outperforms any single tool. Your team becomes your strongest line of defence in protecting your data. Make AI Safety a Core Business Practice Integrating AI into your business workflows is no longer optional, it’s essential for staying competitive and boosting efficiency. That makes doing it safely and responsibly your top priority. The six strategies we’ve outlined provide a strong foundation to harness AI’s potential while protecting your most valuable data.  Take the next step toward secure AI adoption, contact us today to formalise your approach and safeguard your business.

The cloud makes it easy to create virtual machines, databases, and storage accounts with just a few clicks. The problem is, these resources are often left running long after they’re needed. This “cloud sprawl,” the unmanaged growth of cloud resources, can quietly drain your budget every month. According to Hashi Corp’s State of Cloud Strategy Survey 2024 , the top reasons for this waste are lack of skills, idle or underused resources, and overprovisioning, which together drive up costs for businesses of all sizes. Why Should I Care About Cloud Resources? The business benefit is tangible and dramatic. While organisations struggle with cloud budgets exceeding limits by an estimated 17%, automation offers a clear path to control. For example, a VLink saved a significant amount of money on its non-production cloud spend by implementing a rigorous cloud shutdown automation policy. This policy automatically powered down all development and test environments that were not explicitly tagged as 'Production' outside of normal business hours (8 AM to 6 PM). The savings from just this single automated action accounted for 40% off their non-production cloud spend, freeing up that budget for new growth initiatives. 3 Power Automate Workflows Finding these unused cloud resources feels like hunting for ghosts. But what if you could automate the hunt? Microsoft Power Automate is a powerful tool for this exact task. Let’s look at three straightforward workflows to identify and terminate waste automatically. 1. Automate the Shutdown of Development VMs Development and test environments are the worst offenders for cloud waste. A team needs a virtual machine for a short-term project. The project ends, but the VM continues to run, costing money. You can build a workflow that stops this waste. Create a Power Automate flow that triggers daily and queries Azure for all virtual machines with a specific tag, like “Environment: Dev.” The flow then checks the machine’s performance metrics. If the CPU utilisation has been below 5% for the last 72 hours, it executes a command to shut down the VM. This simple Azure automation does not delete anything, it simply turns off the power, slashing costs immediately. Your developers can still start it if needed, but you are no longer paying for idle time. 2. Identify and Report Orphaned Storage Disks When you delete an Azure virtual machine, you are often given an option to delete its associated storage disk. This step is frequently missed, and the orphaned disks continue to incur storage charges month after month. You can create a flow to find them. Build a Power Automate schedule that runs weekly. The flow will list all unattached managed disks in your subscription and will then compose a detailed email report that lists the disk names, their sizes, and the estimated monthly cost. The report acts as a clear, actionable list that could be used for cleanup purposes, and you can send it using the “Send an email” action to your IT manager or finance team for further evaluation on whether to keep or delete the disks. 3. Terminate Expired Temporary Resources Some business projects require temporary cloud resources, like a blob storage container for a file transfer or a temporary database for data analysis. Since these resources have a finite lifespan, you need to directly integrate build expiration dates into your deployment process. For this, you can use a Power Automate flow that is triggered by a custom date field. This means that whenever you create a temporary resource, you add a descriptive tag such as “Deletion Date.” After implementing this best practice, i.e., adding descriptive tags to cloud resources, set the flow to run daily and check for all resources that bear the “Deletion Date” tag. For each resource the flow finds, it should check whether the current date matches or is later than the “Deletion Date” property. If this condition is met, the flow deletes the resource automatically. This hands-off cleanup ensures that temporary items do not become permanent expenses. This approach not only eliminates the risk of human oversight but also uses automation to enforce financial discipline. Troubleshoot Your Automated Workflows Using Power Automate to build these workflows is a great start, but you also need to implement them safely. Automations that delete resources are powerful and need controls in place. To be safe, always launch these flows in report-only mode, which lets you test and simulate automations without enforcing them. For example, you can modify the “Terminate Expired Temporary Resources” flow to send an email alert instead of deleting resources for the first couple of weeks as you observe. This helps validate whether your flow logic is sound and gives you an opportunity to fix errors and oversights. You can also consider adding a manual approval requirement for certain high-risk actions, such as the deletion of very large storage disks. This ensures that your automations work to your benefit and not against you. Take Control of Your Cloud Spend These three Power Automate workflows are a good starting point for businesses using Microsoft Azure. They help you shift from a reactive to a proactive position, ensuring you only pay for the resources you actively use.  Stop overspending on idle cloud resources. To take control of your cloud environment and start saving, contact us today to implement these Power Automate workflows and optimise your Azure spend.