What is Shadow AI? Practical Steps for Integrating AI Projects into Corporate Governance

These days, AI tools are everywhere, and people at work often start using them on their own without asking if that’s fine or not. What is Shadow AI? It’s the unauthorized use of AI tools within a company. At first, it looks like a good tool that can help you complete the task faster, but on the other hand, it can leak data or give wrong answers and even break the company’s rules without anyone noticing. This keeps happening, and rules keep on breaking. The AI shadow effect needs to be handled properly, and some ground rules need to be set to make people aware of using AI in the right way.

The shadow AI challenge shows up when employees try out AI tools or apps by themselves to get work done faster and easier. As noone is keeping an eye on it, these tools can end up sharing private information that company would ideally not want them to and is a point of concern. What seems as a quick solution at one point can create issues for the company later on.

What is Shadow AI, and how is it different from Shadow IT?

Shadow AI refers to the unauthorized use of AI tools within a company. If employees access applications that haven’t been approved or secured by IT, they create an instance of shadow AI.

The shadow AI challenges introduce risks around data leakage and compliance violations. This happens on a larger scale than normal. These tools have access to private sensitive information of the company. That’s where the problem lies. The AI shadow effect has earned its reputation as a top concern, and for good reason: the average cost of a breach involving shadow AI is more than half a million dollars higher than breaches with minimal or no AI involvement.

Shadow AI isn’t new. It is a distant cousin of shadow IT. However, while shadow IT involves rogue Dropbox accounts or unauthorized project management apps, shadow AI is riskier. Tools like Chatgpt, Claude and open source LLMs like Llama can be used easily and present a big risk.

Shadow IT refers to the general use of unauthorized technology, like apps or devices, which are present outside an organization’s IT framework. It often originates from employees finding shortcuts to meet, but it can create security vulnerabilities.

Shadow AI is quite similar to shadow IT, but the emphasis is on unauthorized AI programs. It involves unpredictable models, which makes them harder to secure and safeguard. Governance frameworks for AI are still in the early stages of development, which makes this a complex process to deal with.

What causes shadow AI?

Shadow AI is the use of unauthorized AI tools by employees within an organization, often without the knowledge of IT or management. The hidden adoption happens because of certain factors:

• Easy access to AI tools:
  
  There are a lot of AI and LLM tools widely available for everyone. Anyone can use it. Employees can register and download it without needing anyone’s permission. This accessibility makes it easier for these tools to enter the workplace.
• Lack of clear rules:
  
  Many organizations don't have a governance plan for AI. There are no clear policies on which tools are allowed, how they can be used, or a process to vet new ones. Without these "guardrails," employees might start using AI to handle sensitive data or critical tasks, unknowingly creating security, privacy, and compliance risks for the company.
• Unmet needs:
  
  People look for ways to be more productive all the time. Anyone would want to. If the company’s tools don’t meet employees' needs or are insufficient, they try to identify different ways to make it happen. They can use AI tools available in the market, automating tasks and speeding up the work. They seem to be motivated to use AI tools, which can pave the way for unapproved AI tool adoption that they see as a shortcut, but can hurt the organization in the long term.
• Pipelines and CI/CD Integration:
  
  Modern development practices are incredibly efficient, but they can also create hidden risks. AI models can now be integrated directly into a company's continuous integration/continuous delivery (CI/CD) pipelines, often without any security review. Because these pipelines are automated, they can automatically download and deploy AI models from public sources. This makes it extremely difficult for security teams to track and manage these components, as they are seamlessly embedded into applications and continuously deployed.

Top shadow AI risks and concerns

Shadow AI poses a silent yet significant threat to organizations. The uncontrolled use of these tools introduces serious security, privacy, and compliance risks that can have far-reaching consequences.

• Data exposure and confidentiality loss: Public AI models can unintentionally leak data, including intellectual property and customer information. These models may use this data for training, potentially posing a significant risk of data breaches. Examples include a Samsung employee’s confidential code entered into ChatGPT, potentially making it part of the model's training data.
• Misinformation and biased output: Shadow AI can produce inaccurate or biased information, which can lead to poor decision-making and reputational damage. AI models are known to hallucinate and generate false information. They can also reflect biases from their training data, leading to skewed results. For example, lawyers are fined after submitting false case citations generated by ChatGPT.
• Regulatory non-compliance: The use of unmonitored AI tools can violate new and emerging data protection and AI-specific regulations. Without proper auditing, companies are exposed to legal risks, significant fines, and damage to their reputation. This lack of compliance can be more costly and complex than risks associated with “Shadow IT”.

Examples of shadow AI

Shadow AI can take many forms, from simple productivity tools to complex machine learning models. These unauthorized applications often fly under the radar, creating significant risks. Here are some of the most common examples of Shadow AI that we see in organizations today.

Unsanctioned Generative AI

Nowadays, employees are making use of generative AI tools like ChatGPT, or Gemini to get work done faster. But when something sensitive is entered into the AI tools, that becomes a problem. That can provide data leakage and can help to train models. For example: A marketing intern uses ChatGPT to feed in confidential client details to draft a press release. This means that the client data is not stored in tool’s database without the company’s consent.

Predictive Modeling tools

Data analysts are often engaged in machine learning models for predictive modeling on an organization’s data. As the data moves out of the desired boundaries, it is bound to create problems. The models may produce biases or inconsistencies in the way they produce output. For example, a data analyst might use an unapproved AI tool to predict sales numbers for the quarter, which leads to inconsistencies or biased outcomes.

AI chat assistants

Employees have a habit of integrating AI chat assistants into their workflows for team collaboration. But if that happens without properly following the protocols, there can be risks to the organization. The unapproved chat assistants can process sensitive customer queries without essential data protection measures. Without proper vetting, they can give rise to incorrect responses which may cause serious operational issues or even reputational damage.

AI browser extensions

It’s common for employees to install AI tools browser extensions to enhance work productivity. However, the challenge is that browser extensions can access a lot of browser data, which can further lead to sensitive company data. Without proper security controls, these extensions can transfer data to outside elements, which presents big concerns for the IT teams. These extensions can bypass standard network security, leaving the organization's data at risk.

How to mitigate shadow AI?

While shadow AI may create a lot of risks, it is not impossible to control it. With controlled and collaborative effort, there are several activities that can help mitigate shadow AI from expanding and control it in the long run. Here are some of them:

• Implement an incremental AI governance framework:
  
  Instead of trying to implement a company-wide AI policy, adopt an incremental AI governance and ethics approach. This will lead to avoid resistance from teams. Pilot new AI tools in a controlled environment. This will allow you to gather proper feedback and navigate the challenges in a safe environment. With gradual adoption, the risk is minimized and trust is created among employees which is a positive factor to move forward with.
• Create a responsible AI policy:
  
  Most of the problems crop up because the way AI tools are handled is not counted in a responsible behavior. This will lead to and set a boundary for what type of data be used and execute essential security measures. It should also enlist protocols and approval process for new AI projects to make sure they meet security and data standards before implementing them.
• Involve employees in AI strategy:
  
  Employees engage in shadow AI when the official tools don’t meet their requirements. This can be controlled when there is regular engagement with them and they are made a part of the process, informing them of the next steps. By running surveys, you can come to know what unsanctioned tools are being used by them. Also, taking feedback occasionally bridges the gap between what’s happening and what’s required. It will provide them with the facilities to work on the approved or sanctioned tools they can use. The whole point is that they should feel they are important and heard.
• Implement access controls:
  
  One vital way to control AI risk is to implement role-based access controls. This will ensure that only authorized personnel use AI tools involving sensitive organizational data. To prevent potential data exposure, input and output logs must be regularly audited. These initiatives create a secure environment for your AI initiatives.
• Collaborate to standardize AI usage:
  
  As AI impacts every part and department of the organization, it is prudent that the departments coordinate and collaborate on AI usage. The IT team, operations, security, and compliance should work together to create a set of standard rules for selecting and integrating AI tools. This collaborative effort ensures that risk is minimized and everyone is on the same page. When everyone is on the same page, it is easier to spot errors and work in an efficient manner.
• Implement AI governance accountability:
  
  To mitigate shadow AI, it’s crucial to designate roles or leaders to ensure proper accountability. Assign a team member to oversee AI usage, manage risks, and follow compliance. This dedicated person will help make strong decisions and ensure the efficiency of the overall mission. This will create a centralized hub for all AI governance matters.
• Leadership Buy-in:
  
  To tackle shadow AI, you need more than just IT. You need the full support of executive leadership. Without C-suite buy-in, any efforts to manage unauthorized AI will likely fail. Since AI is used everywhere in the business, IT can’t handle the risk alone. Executive backing is crucial because it provides the necessary authority, helps secure budgets, and breaks through departmental resistance.
• Implement technical controls to combat shadow AI:
  
  To effectively manage shadow AI, IT teams need to implement technical controls. Tools like data loss prevention can help in detecting and controlling suspicious activity, doesn’t matter whether it’s coming from a person, or AI bot.
  
  In addition, SAAS discovery and management platforms are essential. These tools help you identify which applications are used by employees. By identifying unmanaged AI usage, either it can be blocked or brought under control to ensure they meet your security requirements.

Conclusion

Effectively eliminating shadow AI requires a proactive and multi-faceted approach. By securing executive buy-in, implementing clear AI governance and ethics policies, and using technical controls to detect and effectively manage unapproved AI projects, organizations can transform a potential risk into a strategic advantage. This integration ensures that AI initiatives are not only innovative but also secure, compliant, and aligned with core business objectives, ultimately fostering a culture of responsible and transparent AI adoption.