In this article, we’ll explore the biggest security challenges businesses face when adopting generative AI, the key elements of a strong AI security strategy, and the steps organizations can take to prevent AI-related threats.

Key takeaways

  • Generative AI has introduced new ways for internal risks to emerge, while giving attackers more opportunities to exploit organizations.

  • Shadow AI and sensitive data exposure are among the biggest risks enterprises face as employees adopt AI tools.

  • Securing AI workflows requires comprehensive visibility and continuous monitoring of AI tool usage, along with granular access controls.

  • Solutions like NordPass provide organizations with a secure space to store and manage the credentials used for AI platforms.

The impact of generative AI on cybersecurity

To say that generative AI has changed the way organizations approach cybersecurity would be a major understatement. The fact is, it has created a whole new reality that every company must recognize and adapt to—or risk facing severe consequences. Adapting to this reality, however, may be easier said than done.

Part of the challenge comes from the way AI is spreading across organizations. Employees are rushing to adopt AI tools to get their work done faster, but they don’t always involve security teams in the process. This has led to the rise of shadow AI, which refers to the use of AI applications without proper oversight, governance, or security controls. The scope of this problem is very concerning, with 66% of professionals reportedly admitting to using banned AI tools at work.

At the same time, AI has become a major driving force behind cybercriminal operations. By removing many of the barriers that once stood in threat actors’ way, AI is helping them deploy sophisticated attacks faster and with less effort. As a result, black hat hackers can now launch highly targeted phishing campaigns, create more convincing social engineering messages, and develop deepfake attacks designed to bypass biometric security systems—all on an unprecedented scale.

However, AI’s impact on cybersecurity is not entirely negative, with generative AI also becoming a valuable tool for defenders. Security teams can use it, for example, to process large volumes of data more efficiently, identify patterns across security events, and detect suspicious activity that may indicate an attack. Therefore, AI now also plays an important part in keeping companies protected.

That said, AI is not a replacement for strong security practices, nor can it simply be used to counter every threat attackers create with it. Companies still need the right tools, clear usage policies, and ongoing oversight to manage new AI risks while taking advantage of the benefits that generative AI systems can provide.

Top generative AI security risks for enterprises

Given the scale of its impact, it’s important to understand the specific risks generative AI introduces and how they can affect businesses. Here are some of the most significant challenges.

Exposure of sensitive data

One of the most immediate risks comes from employees who enter sensitive information into public AI platforms without recognizing the possible implications. This can result in data such as proprietary source code, customer Personally Identifiable Information (PII), and API credentials being fed into large language models that were not designed with data protection in mind. Once that information is entered into an AI model, it can be difficult—or even impossible—for organizations to understand how it’s used and regain control over it.

Shadow AI

Shadow AI follows the same pattern as shadow IT, but moves faster and carries different risks. When developers bypass security oversight to use unapproved generative AI systems or connect directly to unmanaged third-party AI APIs, they create blind spots that security teams cannot see. The tools themselves may be legitimate, but without proper access controls or data handling policies in place, organizations are exposed to risks they can’t really measure or manage.

Prompt injection and AI model manipulation

As organizations integrate more AI tools into their workflows, a new class of cyberattacks targeting how these systems process and act on information is emerging. One of the most notable examples is prompt injection, where attackers embed malicious instructions into content processed by the model.

These instructions can cause the AI to ignore its intended behavior and operate outside its configured guardrails. As a result, the AI model can start leaking sensitive data or performing unauthorized actions on the attackers’ behalf, all without triggering traditional security controls.

Core pillars of a strong generative AI security strategy

To defend your company against AI risks, your strategy must cover all major areas of exposure. Here are the 3 key components it needs to include.

Visibility into AI usage

As your employees increasingly adopt AI tools, you need to know exactly which models are being used, the data they handle, and how they connect to the rest of your business environment. One practical step is to create an AI Bill of Materials (AI-BOM), which is a structured inventory that tracks every AI model, integration, and data pipeline used across your organization. With this visibility, your security team will be better equipped to identify risks, enforce policies, and make informed decisions about which tools employees can and cannot use.

Access management

It is absolutely essential to control who has access to the AI platforms and models officially approved for use across your organization and to ensure that this access is properly protected. In practice, this means enforcing the principle of least privilege, managing permissions appropriately, securing the credentials used to access AI platforms, and applying strong authentication and authorization controls to limit unauthorized access.

Continuous monitoring

A major concern for many companies is that this fast evolution of AI can quickly create gaps between their existing security policies and how these technologies are actually being used. That’s why it’s so important that you monitor not only which AI tools your employees are using, but also how they interact with them and where sensitive data is moving. This will help your security team detect unauthorized data sharing, identify risky behavior, and respond before it leads to a security breach.

6 ways enterprises can secure their generative AI workflows

  1. Keep an up-to-date inventory of all AI tools. Organizations need clear visibility into which AI tools and models their teams are using to identify potential risks, enforce security policies, and maintain control over AI adoption.

  2. Implement zero trust across all endpoints. A zero-trust approach requires continuous verification of every user, device, and application before granting access to the company’s AI resources. Applying these principles across endpoints helps prevent unauthorized access and limits the impact of compromised accounts or devices.

  3. Protect sensitive data with encryption. Sensitive information shared with or processed by AI systems should be protected with strong encryption wherever possible.

  4. Secure AI accounts with strong passwords and multi-factor authentication (MFA). Accounts for company-approved AI platforms should be protected with strong authentication controls to prevent unauthorized access. This means making sure every AI account has a strong, unique password and enabling MFA for an added layer of protection.

  5. Set clear rules for sharing data with AI tools. Organizations should establish specific guidelines around what data their employees can share with AI tools and under what conditions. These rules help prevent sensitive information from being uploaded to unauthorized generative AI applications.

  6. Train employees regularly on safe and responsible AI use. Regular training helps employees understand not just what the AI usage rules are, but why they matter. It also makes it easier for them to spot potential risks and use AI tools in a safer, more responsible way.

How NordPass can help secure your business in the era of generative AI

As teams adopt more AI platforms, the number of business accounts and, therefore, credentials that need to be created and shared grows rapidly. Without a centralized system for managing these credentials, they can end up in spreadsheets or chat messages and be reused across multiple services, creating unnecessary security risks. 

That’s where NordPass comes in. It gives organizations a secure, centralized place to store and manage the credentials used to access AI tools and the systems they connect to. It also helps teams follow stronger password practices and makes it easier to manage access without adding extra steps that might encourage employees to find workarounds. 

So, if you want to improve credential security while making it easier for teams to adopt AI tools, NordPass can help you stay in control without getting in the way of productivity.