AI Security in 2025: Why Data Protection Must Be Built In, Not Bolted On

Generative AI (GenAI) and agentic AI are revolutionizing how businesses harness data, boost innovation, streamline operations, and supercharge efficiency. But here’s the catch: when sensitive data is fed into an AI model, such as customer records, employee data, financial details, and yes, even security credentials, you’re not just optimizing workflows; you might be handing over the keys to the kingdom, turning a powerful tool into a potential security nightmare.

The scope of the problem is staggering. A recent study revealed that 45.77% of sensitive AI prompts contained customer data, while 27% included internal employee records. Although less frequently exposed, legal and financial data still accounted for 14.88%, while security-related information comprised nearly 13% of all leaked data. The risks are clear: AI models trained on sensitive inputs can inadvertently expose confidential information, putting businesses at risk of regulatory fines, reputational damage, and competitive disadvantage.

But the solution isn’t to ban AI tools or restrict innovation—it’s to secure and sanitize data before it enters AI workflows in the first place. Businesses must strike a balance between leveraging AI’s full potential and ensuring sensitive data is protected at every stage. Let’s dive deeper:

Where Is Your Data? The First Step to AI Security

To secure AI use, first identify where sensitive data lives, who has access to it, and how it flows into AI models. Without clear visibility, confidential data can be exposed during AI interactions or incorporated during model training. Begin with mapping, securing, and managing data access before it interacts with AI.

1. Map Out Data Access

AI models thrive on data, but not all data should be freely available for AI processing. Organizations must identify all structured and unstructured data sources feeding into AI platforms, whether from databases, SaaS applications, collaboration tools, or cloud storage. This requires a baseline of data classification and organization. Establishing strict access controls ensures that only authorized users interact with AI-sensitive datasets.

2. Ensure Sensitive Data Policies Are in Place

AI doesn’t automatically know which data is off-limits—it relies on organizations to define those boundaries ahead of time. That’s why businesses must implement clear policies on how AI handles sensitive information. Defining acceptable data inputs, categorization frameworks, and AI-specific security rules helps prevent employees from unintentionally sharing PII, financial records, healthcare information, or proprietary business strategies with AI systems. A strong Acceptable Use Policy (AUP) ensures AI-powered insights are generated securely and within compliance guidelines.

3. Clarify Data Security Responsibilities

AI adoption isn’t just a security issue—it’s a company-wide responsibility. Security teams must have visibility into how AI interacts with data to ensure compliance with regulations. Meanwhile, business units and IT leaders must enforce proper data governance policies. As AI’s end-users, employees require ongoing education on the risks of mishandled data, data leaks, AI-generated inaccuracies, and security blind spots to avoid unintentional exposure.

4. Enable Secure AI Use—Don’t Just Block It

AI can accelerate business efficiency, which means blocking it isn’t always sustainable. If it’s to be used for applications such as automated customer interactions and account management, security should focus on enabling AI adoption by implementing sanitization and obfuscation. Rather than restricting its use, businesses must eliminate security risks at the data level so employees can leverage AI securely without fear of data leakage or compliance violations.

Understanding where data lives, how it moves, and who accesses it is the first step toward securing AI-driven operations. But awareness alone isn’t enough. Organizations must actively sanitize and protect data before it ever enters an AI model. Once data has been ingested into an LLM, containing accidental exposure is a long, sometimes impossible, task. 

Preparing Your Data Before AI Ingestion

Before integrating data into AI models, take proactive steps to secure, sanitize, and de-risk sensitive information. Use proper controls to avoid creating compliance and security challenges.

Gain Visibility into Data Sources

Not all datasets should be used in AI workflows, yet many organizations lack visibility into which repositories AI models are pulling from. Without a clear understanding of data access, shadow IT and unsanctioned AI usage create blind spots, making it difficult to track how sensitive information is handled. Security teams must ensure AI platforms only interact with approved, well-governed data sources to prevent uncontrolled data exposure.

Identify and Remove Sensitive Data Before It’s Used

Instead of attempting to mitigate exposures after the fact, businesses must proactively filter sensitive data before it reaches an AI model. Active Data Masking tools automatically discover, identify, and mask data that falls under the umbrella of personally identifiable information (PII), payment card information (PCI), and protected health information (PHI), redacting each entry to prevent unauthorized exposure. This ensures AI tools can still generate insights while protecting sensitive data from being misused.

Sanitize Files to Remove Malicious Inclusions

AI models process vast amounts of data, but organizations rarely verify whether that data is safe. Yet, feeding data into an AI model is no less risky than employees downloading unknown documents, clicking on unverified URLs, or handing over sensitive information during a phishing attack. Malicious actors can embed threats in documents, images, and spreadsheets, compromising AI-driven workflows. Hidden malware, embedded scripts, or steganography-based exploits can contaminate AI models, potentially leading to data corruption or unauthorized access. Advanced Content Disarm and Reconstruction (CDR) technology neutralizes these threats at the file level, ensuring that only clean, secure data enters AI ingestion pipelines. 

Day-to-Day 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 an organization’s worth of sensitive data remains protected?

Accept That Sensitive Data Will Be Used

Employees will inevitably interact with AI in ways that include sensitive data, whether they realize it or not. Instead of reacting after a breach, organizations must proactively remove sensitive data before it is shared. It’s like providing bumpers at a bowling alley; you keep the game going while ensuring guardrails are in place. By integrating solutions like Data Detection & Response (DDR) at the point of entry, organizations can prevent the exposure of regulated data without adding friction to workflows.

Security Must Work With, Not Against, Business Operations

Traditional Data Loss Prevention (DLP) solutions often block AI tools entirely, frustrating employees who rely on them for efficiency. This leads to shadow IT, where employees use unauthorized AI services without security oversight. Instead of creating roadblocks, security teams must enable safe AI use through proactive risk mitigation. A defense-in-depth solution like Votiro provides an alternative by sanitizing files and masking sensitive data before it reaches an AI model, allowing employees to work freely while ensuring continuous compliance with complex and ever-evolving data regulations.

How Votiro Keeps AI Secure

For businesses to fully embrace AI, security cannot be an afterthought. Traditional security strategies focus on reacting to breaches, but with GenAI, prevention is the only viable approach. Sensitive data is already being fed into AI models—often with entire organizations unaware of the risks. This demands a shift from reactive defense to proactive security to prevent data leaks.

Active Data Masking: Votiro automatically discovers, identifies, and masks unstructured data while it is still in motion. This approach allows security teams to prevent the unintended exposure of customer records, employee data, and proprietary business information across multiple channels as well as before it can be ingested into AI models. This is all done using fine-grain security controls for each organization.

Advanced CDR: Simultaneously, Votiro proactively neutralizes file-borne threats using its proprietary file sanitization technology. This eliminates zero-day malware, embedded scripts, and exploits before they are processed by AI models. Votiro’s intelligent reconstruction process also leaves file functionality intact to ensure user productivity is not lost along the way.

When security moves from reactive to proactive, AI transforms from a liability into a business accelerator. Organizations that secure AI workflows with Votiro can confidently unlock AI-driven innovation without fear of regulatory violations, reputational damage, or unintended data exposure.

Key Business Benefits of Data Preparation with Votiro

AI is here to stay, but businesses cannot afford to ignore the security risks it introduces. As employees increasingly use GenAI tools for customer interactions, employee workflows, and sensitive business processes, organizations must prepare their data before it enters an AI model.

• Minimized Risk of Data Leaks – Masking data while it’s in motion prevents sensitive data from being exposed in AI prompts, limiting potential misuse.
• Protected AI Workflows – CDR ensures that all files entering AI models are free from malware, ransomware and zero-day threats. 
• Uninterrupted Productivity – Employees can use AI tools freely without fear of leaking confidential data or being blocked by security restrictions.
• Regulatory Compliance – Maintain compliance with GDPR, CCPA, and industry-specific data security standards while enabling AI adoption.
• Stronger AI Trust – Executives and security leaders can confidently deploy AI, knowing that data privacy and security concerns are fully addressed.

Security shouldn’t be a barrier to innovation. With Votiro, businesses can harness AI’s power without exposing sensitive data. Schedule a demo today and discover how Votiro makes data preparation for AI a seamless experience.