Generation AI (GENAI) and Agent AI are revolutionizing how companies leverage data, increase innovation, streamline operations, and leverage supercharge efficiency. But here’s the catch. When sensitive data is fed into AI models, such as customer records, employee data, financial details, even security credentials do not only optimize workflows. You may be handing over the key to the kingdom and turning a powerful tool into a potential security nightmare.
The scope of the question is incredible. A recent survey revealed that 45.77% of sensitive AI prompts contain customer data, while 27% included records of internal employee. Though not often exposed, legal and financial data still accounted for 14.88%, while security-related information accounted for almost 13% of all leaked data. The risks are clear. Trained with sensitive inputs, AI models can inadvertently disclose confidential information and put businesses at risk of regulatory fines, reputational damage and competitive disadvantage.
However, this solution does not ban AI tools or limit innovation. This is to protect and disinfect your data before you enter the AI workflow in the first place. Companies need to harness the full potential of AI and balance it with protecting sensitive data at every stage. Let’s dig deeper:
Where can you find your data? The first step to AI security
To ensure AI use, first identify where sensitive data lives, who has access to it, and how it flows into the AI model. Without clear visibility, sensitive data can be exposed during AI interactions or embedded during model training. Before interacting with AI, start by mapping, security, and managing data access.
1. Map data access
AI models thrive with data, but not all data is freely available for AI processing. Organizations need to identify all structured, unstructured data sources that are fed to AI platforms, including databases, SAAS applications, collaboration tools, and cloud storage. This requires data classification and organizational baselines. Establishing strict access controls ensures that only certified users interact with AI-sensitive datasets.
2. Make sure your sensitive data policy is in place
AI does not automatically know which data is off limits. They rely on organizations to pre-defined these boundaries. Therefore, businesses need to implement clear policies regarding the way AI handles sensitive information. Defining acceptable data entry, classification frameworks, and AI-specific security rules prevents employees from being unable to share their own business strategies with PII, financial records, health information, or AI systems. A strong, acceptable usage policy (AUP) ensures that AI-powered insights are generated safely and within compliance guidelines.
3. Clarify your data security responsibility
Adopting AI is not just a security issue, it is a company-wide responsibility. Security teams need to visualize how AI interacts with data to ensure regulatory compliance. Meanwhile, business units and IT leaders need to implement appropriate data governance policies. As AI end users, employees need ongoing education on the risks of false data, data leaks, AI-generated inaccuracies, and security blind spots to avoid unintended exposures.
4. Enable safe AI use – not just blocking it
AI can accelerate business efficiency. In other words, blocking is not always sustainable. When used for applications such as automated customer interaction and account management, security should focus on enabling AI adoption by implementing disinfection and obfuscation. Rather than limiting its use, businesses need to eliminate security risks at the data level, allowing employees to safely leverage AI without fear of data leakage or non-compliance.
Understanding where your data lives, how it moves and who accesses it is the first step to ensuring AI-driven operations. However, awareness alone is not enough. Organizations need to actively disinfect and protect their data before entering the AI model. When data is ingested in LLM, involving accidental exposure is a long and sometimes impossible task.
Prepare data before taking AI
Before integrating your data into an AI model, perform proactive steps to protect, sanitize and escape risky susceptibility information. Use the right controls to avoid creating compliance and security challenges.
Visualize the data source
While not all datasets need to be used in AI workflows, many organizations lack the visibility of the repository that the AI model is retracting. Without a clear understanding of data access, Shadow IT and unauthorized AI usage create blind spots and make it difficult to track how sensitive information is handled. Security teams must ensure that AI platforms interact only with approved and properly gabundized data sources to prevent uncontrolled data exposure.
Identify and delete sensitive data before using it
Instead of trying to reduce exposure after the fact, companies need to actively filter sensitive data before reaching the AI model. Active data masking tools automatically discover, identify and mask data under the umbrella of Personally Identifiable Information (PII), Payment Card Information (PCI), and Protected Health Information (PHI), and edit each entry to prevent unauthorized exposure. This allows AI tools to still generate insights while protecting against misuse of sensitive data.
Disinfect files and remove malicious inclusions
AI models process huge amounts of data, but organizations rarely check whether it is safe or not. However, feeding data to an AI model is more dangerous than employees downloading unknown documents, clicking on unverified URLs, or handing over sensitive information during a phishing attack. Malicious actors can compromise on AI-driven workflows and embed threats in documents, images, and spreadsheets. Hidden malware, embedded scripts, or steganography-based exploits can contaminate AI models, leading to data corruption and unauthorized access. Advanced content disarming and reconstructing (CDR) technology neutralizes these threats at the file level, ensuring only clean and safe data enters the AI intake pipeline.
Daily business without blocking
Security teams cannot realistically prevent employees from using AI tools. However, the challenge is clear. How can security teams allow AI adoption while ensuring sensitive data for their organizations?
Accept that sensitive data is used
Employees inevitably interact with AI in a way that includes sensitive data. Instead of responding after a violation, organizations should actively delete sensitive data before they are shared. It’s like offering bumpers at a bowling alley. Continue the game, ensuring that Guardrail is in place. By integrating solutions such as data detection and response (DDR) at the time of entry, organizations can prevent the exposure of regulated data without adding friction to their workflows.
Security needs to cooperate rather than oppose business operations.
Traditional Data Loss Prevention (DLP) solutions often completely block AI tools and irritate employees who rely on them for efficiency. This causes employees to use unauthorized AI services without security supervision. Instead of creating obstacles, security teams should enable safe AI use through aggressive risk mitigation. Detailed solutions like Votiro offer alternatives by sanitizing files before reaching AI models, masking sensitive data, and allowing employees to work freely while ensuring ongoing compliance with complex and evolving data regulations.
How to protect your votes
Security cannot be an afterthought for businesses to fully embrace AI. While traditional security strategies focus on responses to violations, at Genai, prevention is the only viable approach. Sensitive data is already being fed into AI models. In many cases, the entire organization is not aware of the risk. This requires a transition from reactive defense to aggressive security to prevent data leakage.
Active Data Masking: Votiro automatically discovers, identifies and masks unstructured data while still moving. This approach allows security teams to prevent unintended exposure of customer records, employee data, and unique business information across multiple channels. All this is done using fine-grained security controls from each organization.
Advanced CDR: At the same time, Potiro actively neutralizes file-mediated threats using its proprietary file sanitization technology. This eliminates zero-day malware, embedded scripts, and exploits before the AI model handles it. Votiro’s intelligent reconfiguration process also leaves the file functionality intact, preventing users from losing productivity along the way.
When security moves from reactive to proactive, AI changes from responsibility to business accelerator. Organizations that secure AI workflows by voting can confidently unlock the lock on AI-driven innovation without fear of regulatory violations, reputational damage, or unintended exposure to data.
Key business benefits of data preparation using Votiro
AI stays here, but businesses cannot afford to ignore the security risks they implement. As employees increasingly use Genai tools for customer interaction, employee workflows and sensitive business processes, organizations need to prepare their data before entering the AI model.
Minimize the risk of data leakage – Masking data during movement prevents sensitive data from being exposed to AI prompts and limits potential misuse. Protected AI Workflow – CDR ensures that all files you enter into your AI model are free of malware, ransomware, or zero-day threats. Uninterrupted Productivity – Employees are free to use AI tools without fear of being blocked by sensitive data leaks or security restrictions. Regulatory Compliance – Maintain compliance with GDPR, CCPA and industry-specific data security standards while enabling AI adoption. More Powerful AI Trusts – Executives and security leaders can confidently deploy AI, knowing that data privacy and security concerns are fully addressed.
Security should not be a barrier to innovation. Votiro allows businesses to leverage the power of AI without revealing sensitive data. Schedule a demo today to discover how Votiro can make AI data preparation a seamless experience.
