Shadow AI, the unauthorized use of AI tools within organizations, is like that sneaky kid in class who brings a slingshot to school—fun until someone is injured. As employees tap into AI platforms without IT’s blessing, the risks multiply faster than a cat video going viral.

Imagine a workplace where employees secretly use AI applications to write reports, generate graphics, or even code—all while IT is blissfully unaware. It’s like a digital game of hide-and-seek, except the stakes are data breaches and compliance violations. One moment, you’re drafting a proposal. The next, you’ve accidentally leaked sensitive information about a raccoon in a trash can to a chatbot that is as trustworthy as a raccoon.

The humor in this predicament isn’t lost on those in the know. Imagine the IT department, armed with old security tools, trying to catch wind of shadow AI usage. It’s akin to a group of medieval knights battling dragons while the real threat is a rogue squirrel stealing their lunch. They’re left wondering, “Is that a new AI tool, or just another employee trying to automate their way out of a performance review?”

And let’s not forget the compliance nightmares. Using shadow AI can lead to regulatory fines that could make even the most seasoned accountant weep. Employees might think they’re being clever by using AI to speed up their work, but when the legal team finds out, it’s like watching a magician reveal his tricks—suddenly, the magic is gone, and all that’s left is a sad rabbit in a hat.

In the end, while shadow AI might seem like a harmless office prank, it’s more like playing with fire in a room full of gasoline. So, the next time someone in the office suggests using an unapproved AI tool, remember that it’s all fun and games until someone gets a data breach.

Many organizations have experienced data breaches caused by Generative AI. Some breaches were in the news, and many others were not.

Shadow AI, Risks, and Things

Generative AI leverages vast datasets to drive innovation, optimize resource allocation, and enhance customer experiences. Unlike traditional analytics, which primarily analyze historical data, Generative AI can create data and generate insights, thus enabling organizations to make real-time decisions and adapt quickly to market changes.

In the era of Generative AI, data is your most valuable asset !

What is Shadow AI?

Shadow AI is the unauthorized use of artificial intelligence tools and applications within an organization, often without the knowledge or approval of the IT and security department. This emerging phenomenon has evolved from the “Shadow IT” concept but poses potentially more significant risks due to AI systems’ inherent complexity and unpredictability.

While this drive for innovation can lead to increased efficiency, it also introduces significant business challenges. From data privacy concerns to regulatory compliance issues, Shadow AI represents a hidden frontier of AI usage that demands careful attention and strategic management.

As AI tools become more accessible and user-friendly, the prevalence of Shadow AI is on the rise. According to IBM, 30% of IT professionals report that employees in their organizations have already adopted new AI and automation tools, often without proper governance processes. This unauthorized adoption of AI technology creates a complex landscape for IT departments and business leaders to navigate.

In this blog post, we’ll explore the intricacies of Shadow AI and its potential risks and benefits. We’ll also provide actionable strategies for organizations to effectively manage and harness the power of AI while mitigating associated risks. Whether you’re a CIO, IT professional, or business leader, understanding and addressing Shadow AI is crucial for maintaining data security, ensuring compliance, and fostering responsible AI adoption within your organization.

Unsanctioned and unknown Generative AI use is only growing inside your organizations. Here’s how you can handle it while keeping businesses safe. Shadow AI can be a governance nightmare, requiring you to decide what AI usage to permit or refuse to support while preserving the business’s security.

Shadow AI will be much worse than Shadow IT !

The conversations today about Shadow AI take me back to 2015 when the conversations about Shadow IT were “strange” or unimportant enough discussions. Since then, everyone has adopted tools that handle Shadow IT and still fight the situation, often losing it. The Shaodw AI will only worsen Shadow IT. Shadow AI is a known security issue many organizations face, and some still do not know how to handle it.

Are you familiar with the stories about companies that restrict (mostly try) the use of Generative AI? It’s primarily a nice shot because the employees still use it, even more than before. The attempt to block Public LLM from the technical side isn’t efficient, even if you maintain it daily. Traditional technologies are unfamiliar to all Public LLMs, and they get out daily. It’s okay; you are not alone!

Recent Dell research indicates that 91% of respondents have dabbled with generative AI in their lives in some capacity, with another 71% reporting they’ve specifically used it at work.

Much like Shadow IT before, Shadow AI appears to be here already. Many organizations are still trying to figure out how to use Generative AI (Public LLM, Private LLM, Employee side, etc.) and what their security posture will be. It must be at the top of everyone’s list because the costs of getting this wrong can be astronomical, and this new Generative AI era is not sitting out.

A comment about the security tools for Shadow AI and AI Security. Like in the Cloud Security perspective. The traditional (on-premises) security tools cannot manage Cloud Security. Therefore, the conventional (on-premises) and the Cloud Security tools cannot handle AI Security. You can do everything to adapt it, but it will not be practical.

Employees use dozens of Generative AI tools in their daily operations, most unbeknownst to their IT teams. Key concerns are limited visibility, absence of governance, compliance risk, and data exposure. ChatGPT marked the beginning of the widespread adoption of AI tools, but it’s not only ChatGPT. Today, in the average company, employees use over 30 different AI tools as part of their jobs, most without visibility to the IT and Security team. Managing these tools is crucial for success.

How does Shadow AI differ?

Shadow AI differs from traditional AI implementations in several key ways:

Approval and Oversight: The primary distinction is that Shadow AI operates without official approval or oversight from an organization’s IT department or leadership. Traditional AI implementations, on the other hand, go through proper channels for approval, integration, and governance.

Deployment Process: Shadow AI is often deployed rapidly by individual employees or departments to meet immediate needs, bypassing standard IT processes. Traditional AI implementations typically follow a more structured deployment process involving planning, testing, and integrating existing systems.

Governance and Control: With Shadow AI, there is limited or no centralized management and control. Traditional AI implementations are subject to organizational policies, security measures, and compliance standards.

The Risks of Shadow AI

While adopting AI can bring numerous benefits, the uncontrolled spread of shadow AI within an organization presents many potential risks that can significantly impact data security, compliance, and overall business operations. Shadow AI introduces significant risks that may go unaddressed, including:

Transparency and Visibility: Shadow AI usage is often opaque to IT teams, making it difficult to audit and monitor. Traditional security tools don’t provide visibility of AI activities within the organization.

Dynamic Nature: AI and its LLM, driven not just by code and data but also by the logic that learns from it, presents a moving target. Unseen risks, such as bias and unexpected responses, can lead to setbacks for security, data, and compliance teams.

Unsecured Models: Extending AI usage necessitates strict data controls on model inputs and outputs. Failure to implement security controls leaves AI models vulnerable to manipulation, data leakage, and malicious attacks.

Uncontrolled Relations: In the AI, unprotected prompts, agents, etc., create avenues for harmful interactions that threaten user safety and ethical principles. Security vulnerabilities like prompt injections or training data poisoning can occur.

Long-term Strategy: Shadow AI may not align with the organization’s long-term company primary security, AI strategy, and goals. Keep visibility for AI as well as part of the security posture.

Data Complexity: The complexity of AI-generated data raises concerns about its origin, use, and accuracy. This lack of transparency poses challenges to privacy and security, potentially exposing sensitive information to leaks.

Compliance Challenges: Shadow AI poses significant compliance challenges for organizations, particularly when adhering to data protection regulations like the GDPR. The unauthorized use of AI tools without proper oversight can lead to several compliance issues: Data Privacy Violations, Lack of Transparency and Control, Inadequate Data Protection Impact Assessments, etc.

While Shadow AI can drive innovation and agility, it presents significant security, compliance, and overall AI governance challenges. Organizations must balance enabling innovation and controlling AI usage to harness its benefits while mitigating associated risks.

For example, Sensitive Data Disclosure. Whether through ShadowAI or well-known platforms like ChatGPT or Jasper, sensitive data from your employee is being streamed to these GenAI tools 24/7. This is happening at an unprecedented pace, and unlike any other tool we’ve seen, there’s a significant probability that this data will be used for future training and potentially be generated by these tools on external endpoints in the coming weeks or months.

The Security Challenges of Detecting Shadow AI

One of the most significant AI security issues is Shadow AI. For many, it is still unknown. Explaining it changes mindsets and perceptions about the challenges and management of Shadow AI.

Attempting to block or set policies for every new AI application manually is impractical and impossible. AI’s dynamic nature means that new AI applications and updates are released at a pace that far outstrips the capabilities of traditional security tools. This issue is only set to grow more severe as organizations adopt AI at an unprecedented pace, leading to an unmanageable and invisible Shadow AI attack surface.

AI is not limited to standalone applications like ChatGPT or Gemini. It’s integrated into many AI tools or websites. From Used-LLM (such as Microsoft Copilot 365) or CRMs incorporating AI components to enhance customer service or developer tools providing AI coding assistants, organizations must detect such AI components in real time and implement necessary protections to safeguard data and assets.

The security leaders I regularly speak to initially relied on simple URL filtering to allow or block employee usage of AI tools. This method relies on static lists of known AI applications. However, these lists are often incomplete and cannot be updated quickly enough to maintain pace with employees’ rapid adoption of new AI applications.

After a conversation, they know the old security controls are incompatible with the new AI landscape.

How to handle Shadow AI?

When it comes to AI Security, the mindset MUST be different. A comprehensive approach combining proactive measures, transparent policies, and ongoing monitoring is necessary to handle Shadow AI within an organization effectively.

Here are key strategies to address Shadow AI:

Visibility is the Key

Like in many other situations, visibility is the key and can distinguish between controlled AI and Shadow AI.

Implement Visibility, Monitoring, and Control Measures: You should deploy a dedicated security tool that has visibility, monitors, and detects the use of any AI applications, unauthorized and authorized. Regular audits should be conducted to assess AI usage within the organization and identify potential Shadow AI activities. This ongoing monitoring helps maintain visibility into AI usage across the company.

Don’t Block it

The “Ban Mode” failed again (and again) in many organizations.

Rather than outright denying Shadow AI, know its potential benefits and use it in a controlled manner. Recognize that employees often adopt Shadow AI for good reasons, such as boosting productivity. Provide secure alternatives by offering official AI applications and platforms that meet employee needs while sticking to security standards.

Note: In all situations when an AI application was banned, it failed due to a lack of control and knowledge of the nature of AI applications.

Stay Tuned with AI Trends

AI is very dynamic. For example, Public-LLMs are updated daily, and new AI applications are released weekly.

Keep up-to-date on emerging AI technologies to determine new tools and potential risks. Review and update AI security policies to address evolving threats and technologies. This proactive approach helps the organization stay ahead of potential Shadow AI challenges.

Applying Policies

Design comprehensive AI usage policies clearly define acceptable and unacceptable AI use. These policies should abstract data handling, privacy, security, and compliance policies. Ensure employees know these policies and understand the potential risks of using unauthorized AI applications.

What about educating users? I don’t believe in educating users because users can take many actions without knowing the potential risks. In this case, putting effort into it can be useless.

Prompt Security to the Rescue

In the last two years, I’ve tested, simulated, and checked many AI Security tools across the different AI surface layers. After a while, I came across the desirable, ready, and most baked platform – Prompt Security.

In the field of AI, everyone tests or uses AI applications, whether private LLM, public LLM, or others. Most companies are eager to know how the next security tool will cover the threats, risks, and security gaps.

What is the AI Access surface? Below is a high-level view of the AI Access Surface and the layers of each level: Public LLM, Private LLM, Used LLM for Developer, and Used LLM for Native SaaS apps. Do you know what the security gaps are for each of them? Or attack techniques that can be used by adversaries? Or what are the threats and risks?

The Prompt Security and its capabilities for Shadow AI can perform a wide range of tasks, often without official approval or oversight. Here are some key capabilities:

Detection 

A dynamic detection mechanism that can keep up with the fast-paced AI landscape. The technology listens to browser traffic and uses advanced heuristics to identify behavior indicative of GenAI applications. It can detect previously unseen AI components, ensuring comprehensive security visibility and management of AI tools across users and user groups in the organization.

This mechanism also leverages the network effect to update its detection models constantly based on usage patterns. Our system becomes more effective over time, learning from the AI tools and components used across different organizations.

This approach involves a detailed analysis of webpage content, network traffic, and user interactions to provide the most accurate verdicts and avoid false positives. This allows us to determine whether a user, a product, or an automatic process generates an action.

Dynamic detection of GenAI tools and applications offers the broadest detection of Shadow AI in the market, including more than 9,000 different GenAI applications (yes, there’s more than just ChatGPT or Gemini). Whichever AI risk management policy you define, it catches all employee usage of emerging GenAI tools.

Constant Self-Testing

Almost as an anecdote, since we see infographics and lists of the GenAI tool landscape every day or week, we use those as a benchmark to test the detection rate of our dynamic Shadow AI detection mechanism. And guess what? We automatically detect between 98% and 100% of the tools listed in such reports weekly.

Thanks to our preemptive identification of these emerging GenAI tools, our customers and partners can apply necessary measures, from data redaction and sanitization to outright blocks, ensuring their organizations remain innovative and secure.

Note: The Prompt Security contains additional and rich policies for Shadow AI.

Conclusions

It’s not recommended to ignore the Shadow AI issue because organizations have already lost information, have been involved in incidents, and have realized that this is a significant risk. Shadow AI risks include security vulnerabilities, data privacy issues, operational inefficiencies, compliance risks, and ethical concerns. Organizations must implement effective governance and management strategies to mitigate these risks, such as awareness and education, centralized oversight, risk assessment, compliance enforcement, and ethical guidelines.

References

• Prompt Security Blog
• More AI Security blog posts in the CYBERDOM
• What is Responsible AI? Microsoft approach