The hidden risks of GenAI and how to secure it

Generative AI (GenAI) is already reshaping how work gets done whether your organization planned for it or not. Employees are moving fast, adopting AI tools to boost productivity, streamline workflows, and spark new ideas. But while everyone’s focused on speed, the security risks are flying under the radar.

The reality? Employees aren’t thinking about GenAI security. They’re focused on getting the job done, often without considering where data is going or what risks they might be introducing. Without the right security framework, even well-intentioned adoption can lead to data leaks, compliance violations, and unmonitored interactions that put your company at risk.

If you’re responsible for GenAI adoption, waiting isn’t an option. Getting ahead of GenAI security now means preventing vulnerabilities before they turn into full-blown crises. This blog breaks down the hidden risks of generative AI and lays out a smarter approach to securing its use across your organization.

The hidden risks of generative AI in the workplace‍

GenAI security has rapidly emerged as a critical need, yet most enterprises are still in the early stages of figuring out how to implement it. While executives prioritize AI adoption, it is said that 90% of organizations remain in observer mode balancing interest with uncertainty. Many outright block GenAI while others selectively enable it through committees, but as GenAI weaves itself into every daily workflow and application, these approaches create backlogs and inefficiencies.The real challenge isn’t just controlling GenAI use, it’s enabling safe experimentation while scaling successful use cases across departments.

Within GenAI security, there are three key areas:

1. Securing model deployments

2. Securing model development

3. Securing workforce use.

It’s this last category, governing how employees interact with GenAI, that presents the greatest security challenge for enterprises today.

Data leaks and intellectual property risks

Using generative AI can expose sensitive enterprise data often without employees realizing it. A simple copy-paste into a chatbot for quick analysis might seem harmless, but that data doesn’t just disappear. If shared with a public GenAI model, it can be ingested and used to train future responses, meaning proprietary details could resurface in outputs to other users, potentially even competitors. Unlike a misplaced file, this information can’t be deleted or retrieved. Once a model learns it, it’s out of your control.

Compliance and privacy challenges‍

With regulations like GDPR, HIPAA, and SOC 2 in play, using GenAI at work isn’t just a security concern, it’s a legal one. Companies that don’t govern GenAI usage properly risk fines, lawsuits, and reputational damage. Ensuring interactions align with privacy laws isn’t optional; it’s essential.

Hallucinations and misinformation

Generative AI doesn’t always get it right. It confidently generates inaccurate or misleading information, often in ways that seem plausible at first glance. Employees who trust GenAI-generated insights without verifying them could make decisions based on false data, putting operations and finances at risk.

Shadow AI and unmonitored usage

Generative AI is being used across your organization whether you sanctioned it or not. Employees experiment with new tools on their own, often with good intentions, but without oversight. This creates "shadow AI" which is unauthorized, untracked usage that increases security gaps, compliance violations, and exposure to cyber threats.

Why traditional security approaches fall short

Blocking AI is an invitation to shadow AI‍

When organizations block GenAI tools, employees find workarounds. They turn to personal accounts, unapproved applications, and browser-based AI assistants, creating security blind spots and compliance risks.

‍Selective enablement slows innovation but doesn’t stop risk‍

Some companies form governance committees or restrict GenAI to pre-approved tools, but these approaches create bottlenecks as AI spreads across every SaaS application. Meanwhile, sensitive data still finds its way into AI models through integrations and third-party tools.

Point solutions don’t provide enterprise-wide visibility

Endpoint protection and browser plugins only cover a fraction of GenAI usage. Employees interact with GenAI across cloud apps, APIs, and custom workflows leaving security teams without a full picture of how it’s all being used and where data is going.

‍GenAI governance requires proactive, full-stack security‍

Effective GenAI security isn’t about blocking or selectively allowing, it’s about governing workforce use holistically. That means integrating endpoint protection, identity management, DLP, SIEM, model inference, and SaaS security into a single control plane that provides visibility and enforces policies across every AI interaction.

A stronger, more proactive approach 

Control what you can control

1. Assess GenAI usage and risks – Map out where and how you know GenAI is already being used across your organization.
2. Define security policies with enforcement – Set clear guidelines for GenAI usage, but back them up with technology that enforces them.
3. Implement network-level GenAI governance – Move beyond endpoint controls and deploy a security solution that governs GenAI usage holistically. 
4. Govern GenAI interactions in real time – Gain visibility into GenAI-driven conversations, enforce security policies dynamically, and prevent risky behaviors.
5. Educate and train employees – Make sure teams understand GenAI risks and best practices to minimize security threats.

The SurePath AI approach 

SurePath AI doesn’t rely on 'pretty pleases', endpoint protection, or browser plugins. Instead, it secures GenAI interactions at the network level covering all corporate-administered devices and ensuring complete visibility into GenAI usage.

Key capabilities of SurePath AI

1. Risk mitigation

• Prevents sensitive data from being leaked through GenAI prompts
• Detects and redacts confidential information before it reaches external AI models
• Controls access to private models and enterprise data
• Syncs with role-based policies to manage user permissions
• Creates guardrails that enforce security policies dynamically

2. Visibility

• Captures and records all AI interactions with risk tagging
• Uses synthetic data remediation to prevent exposure of sensitive details
• Provides detailed audit trails to ensure compliance with industry regulations
• Delivers insights into user activity, policy enforcement, and security threats

3. Increased, safe, adoption

• Deploys at the network edge for seamless integration
• Intercepts AI traffic without disrupting business operations
• Brings shadow AI into a controlled and compliant environment
• Balances user productivity with enterprise security requirements

Take control of GenAI security

Generative AI isn’t going anywhere, and neither are the risks. Securing generative AI requires a new approach that sees beyond endpoints and browser monitoring. SurePath AI gives enterprises complete oversight and control, ensuring GenAI adoption is safe, compliant, and risk-free.

Ready to safely make the most of GenAI and make every employee a power GenAI user? 

Depending on where you are in your journey, we suggest booking a demo with us or starting a free trial.