Every organization understands that AI is central to competing and thriving in the future of work. But with that opportunity comes risk. For every gain in speed, efficiency, and output, AI introduces new vulnerabilities: data leakage, regulatory exposure, ethical concerns, and the potential misuse of sensitive information.
Governing this technology is critical to long-term success. However, governing AI effectively is its own challenge. Some organizations have leaned into written policies and one-off training to tick the box—but this isn’t enough to guarantee secure usage.
Here, we’ll look at why.
AI policies: Only the foundation
In an effort to establish some control over workplace AI, many organizations have introduced written AI acceptable usage policies that outline what’s acceptable and what isn’t. These documents typically cover which tools are allowed, what types of data can be shared, and general do’s and don’ts for interacting with AI systems.
It’s a logical starting point—but it’s not enough.
Policies, by design, are static. They exist as reference points, not living systems. Most employees will skim them once, acknowledge the rules, and move on. Without reinforcement, retention is short-lived—research shows employees forget over half of what they’ve learned within just 24 hours. That makes policy adherence more of a hopeful assumption than a reliable outcome.
Even when policies are paired with security training, the impact is often minimal. Traditional formats—like slide decks, self-paced eLearning, or one-off webinars—are built to meet compliance requirements, not to change behavior. They rarely reflect the fast-evolving nature of AI tools, and they don’t create the kind of real-time awareness needed to prevent risky actions before they happen.
For organizations, this means that AI policies (while essential) aren’t enough on their own to ensure the secure use of AI—especially as the risks are too great to ignore.
The risks of policies alone
So, what do those risks look like? Here’s what can happen when organizations don’t properly secure and control AI usage.
Shadow AI
Shadow AI refers to the use of generative AI tools—such as ChatGPT, Bard, and others—without approval or oversight from IT or security teams. In practice, it often looks like employees independently using these tools to draft content, generate visuals, or write code as part of their day-to-day work.
At first glance, it may seem innocuous. But in reality, it creates significant visibility gaps for security teams. Without clear oversight, there’s no way to know which tools are being used, what data is being shared, or whether usage aligns with internal policies.
This lack of visibility makes it nearly impossible to enforce compliance or apply appropriate safeguards. And that brings us to the next challenge.
Data leakage
Public AI applications are public in every sense. The data that employees share is ingested by these tools’ neural networks. It could be regurgitated in answers to other people in totally different parts of the world—who have nothing to do with your organization.
Imagine, for example, a well-intended customer success rep trying to quickly create an Excel spreadsheet for a list of customers they need to reach out to. They turn to ChatGPT, and share a list of names, addresses and email addresses. Immediately, this is a data breach for your company. That data is personally identifiable information—and it’s now out in the world without your customers’ permission.
AI hijacking
AI hijacking is an emerging threat in the generative AI landscape. Through carefully engineered prompts, malicious actors can manipulate AI models into revealing sensitive information or performing unintended actions—often without triggering obvious red flags.
These attacks don’t rely on exploiting traditional software vulnerabilities. Instead, they target the AI’s behavior—bypassing standard controls through prompt injection or social engineering.
Without the right guardrails in place—both technical and procedural—organizations are exposed. Whether it’s a lack of monitoring, weak usage controls, or untrained employees, any gap in governance increases the likelihood of compromise.
From policies to true AI governance
True AI governance requires more than well-meaning policies. It demands systems that actively enforce controls, reduce risk in real time, and adapt to how generative AI is actually used across the business.
While AI can feel like a moving target, the foundations of governance remain familiar. Existing data governance frameworks can—and should—be extended to cover generative and agentic AI, built on core principles like:
- Data classification and quality
- Stewardship and accountability
- Protection and compliance
- Lifecycle and access management
In practice, that means treating sensitive data within AI tools no differently than data in cloud applications or enterprise networks. Organizations need mechanisms that can:
- Discover, classify, and continuously monitor sensitive data across AI prompts and responses
- Enforce granular access controls based on user roles and permissions
- Detect risky behavior—such as improper data sharing or anomalous usage patterns
Getting started
Traditionally, meeting all of these requirements meant stitching together multiple tools. But that’s changing. A new category of runtime security solutions now offers integrated visibility and control across generative AI platforms.
Polymer SecureRAG is one of them. It embeds directly into AI tools like ChatGPT to monitor usage and protect data in real time. Here’s how it can help you unlock AI’s potential—securely:
- Bi-directional data discovery and redaction: Automatically scan prompts and responses for sensitive data—and redact or block it in real time, based on your policies.
- Granular permissions: Ensure AI apps and employees interact with only the data they’re meant to see, based on flexible, real-time access policies that are fully customizable.
- Faster audits and investigations: Quickly surface relevant AI activity when responding to e-discovery, audits, or compliance reviews.
- In-the-moment learning: When a policy is violated, Polymer alerts employees immediately—giving them context and guidance so they understand what went wrong and how to fix it going forward.
- Full visibility: Get a clear picture of how employees are using generative AI tools, spot risky behavior, and identify compromised or malicious accounts before any damage is done.
The bottom line
Policies are important—but they’re only one piece of the puzzle. To unlock AI’s full potential while keeping data safe, organizations need practical, real-time controls that bring those policies to life.
Ready to make secure AI usage the default? Discover how Polymer SecureRAG can help you govern AI tools without slowing down innovation. Request a demo now.
