Hi folks! What if an employee pastes a customer complaint into an AI-based chatbot in order to receive a more timely response? Or the employee turns to an AI code assistant for an assessment of proprietary source code. The marketing department deploys an AI image generator using unannounced campaign details. None of this is intentional. But the company might just lose control over its sensitive information. This is what leads to Shadow AI.
Shadow AI turns into a pressing problem for the workplace. Employees seek quicker solutions to composing, analyzing, coding, researching, summarizing, and automating their work. AI can help employees accomplish this, but employees often implement such tools prior to any assessment by the company's security, legal, procurement, or IT departments.
There is no need for a blanket ban on all AI applications. Companies require visibility, policies, employee training, and alternatives that enable employees to be productive without introducing unnecessary risks.
What Is Shadow AI?
Shadow AI is the use of artificial intelligence tools in organizations without permission from the IT team. Just as shadow IT entails the installation of unapproved software in the organization, shadow AI arises due to the deployment of AI models, chatbots, and automation tools without the IT team’s knowledge.
Unlike software that holds data, AI can gain access to it in different ways. This means that sensitive data might get into the training of the AI model, moving from being proprietary to becoming accessible to everyone publicly. AI processes sensitive data and makes decisions based on it. And all of this comes with compliance risks.
In the absence of proper supervision, organizations risk exposing proprietary and customer data, having biased output from AI, among other compliance issues. In light of this, managing shadow AI becomes necessary.
Difference Between Shadow AI and Shadow IT
Shadow AI | Shadow IT |
Unauthorized AI features or tools | Unauthorized software |
Comes with model, data, result, and automation risks | Concentrates primarily on application access |
Related to automated decisions and data processing | Involves software adoption |
Needs monitoring and AI-specific governance | Conventional security controls are useful |
What Causes Shadow AI?
Artificial intelligence applications are available via free subscription plans, intuitive user interfaces, and web-based software.
Employees Seek Convenience and Speed
Often, employees use AI tools to facilitate the completion of a task in less time. If the chosen solution is time-consuming, an employee opts for an AI tool or program to get quick or immediate results.
AI Features Become Ubiquitous
Modern Software as a Service (SaaS) solutions have incorporated artificial intelligence components. Consequently, the IT department has to monitor not only separate applications but also artificial intelligence functionality embedded in already installed products.
Poor Policies Encourage Shadow AI
Inadequate AI policy does not provide sufficient guidelines regarding data sharing. Thus, an explicit policy should cover such aspects as sharing customer records, source code, contracts, financial data, and other internal documents.
Insufficient AI Literacy
Artificial intelligence can generate inaccurate or biased information. Unaware employees can easily rely on this misleading information and create risks related to data exposure, errors, and inadequate governance.
Decentralized Procurement
If the purchasing decisions for software lie with individual business units and employees, AI solutions can be introduced into the company without strict IT oversight.
Absence of Governance
In the absence of AI policies, an organization might start using such technologies without adequate consideration of security, compliance, and procurement processes, thus creating problems with the adoption of AI solutions.
In regard to security, if you wish to know about cloud security testing, you can check this article out for your convenience.
A Gap in Employee Preparedness
The employees in an organization may not possess the necessary skills and knowledge that would allow them to work with AI technology safely and correctly.
What are the Risks Associated with Shadow AI?
Exposure of Sensitive Data
Employees may unknowingly disclose customer data, source code, internal documents, or sensitive data to any outside AI tool. There is a need for clear policy guidelines on data employees can never disclose.
Privacy and Non-Compliance Issues
Uncontrolled AI usage poses compliance risk since companies do not know where data flows, how long providers store data, and whether third parties access such data.
Biased or Unreliable Decisions
AI can give answers that may be incorrect or biased. Employees can use AI as an aid to making decisions.
Third-Party and Security Risks
AI outside vendors raise security and third-party risks. You are required to assess several aspects like vendor security, storage, data management, and data integration, among other things.
On a slightly different note, it is essential for you to understand IoT penetration testing. This is why you must read this blog.
Additional Costs
Unapproved AI implementation will require additional costs because of subscriptions and purchases from various departments, as well as a pay-per-use model.
What to Do Once You Discover Shadow AI at Your Organization?
If you discover that there is shadow AI at your organization, you need to measure its impact, identify risks and mitigate them, and establish policies to avoid such issues in the future.
Measure AI Adoption Scope
Identify the AI solutions that have been adopted, how many people use them, and whether those solutions work with the organization’s enterprise software. You can do it through the discovery of SaaS applications (e.g., Zylo), expense audits, and security audits.
Identify Risks and Compliance Issues
Understand how those AI solutions handle the organization's data of the organization, whether they meet security policies, and what risks it brings.
Collaborate with Stakeholder
Work with IT, Security, Legal, and other departments to decide on the best way to minimize risks while allowing AI adoption.
Decide on Restricting, Monitoring, or Adopting Those AI Solutions
Some AI-enabled applications add value; others are too risky. Determine how you will integrate useful solutions into your operations.
Inform Employees about Findings
It is important to let employees know what kinds of AI tools they are allowed to use in the organization. The company should also guide them on how they can ask for AI-powered solutions.
With this strategy, organizations can move from discovering AI in a reactive manner to managing AI proactively.
How to Control Shadow AI within Your Organization?
Businesses should not ban the complete use of AI tools. Instead, it would be better to create an environment where people will be able to safely apply the approved AI tools.
Define the Policy of Using AI
Define which tools can be used, what type of data is not supposed to be analyzed by these tools, which type of use is permitted, and how often to have reviews and reports on that.
Keep the List of Approved AI Tools
Analyze the most popular AI tools in terms of their privacy policy, data security, retention policy, and integration with third-party software and services. Easy access to AI will lower the chances of people using other tools.
Conduct Regular Employee Training
Train your employees on data protection, verification of AI output, risks associated with some services, and tool discovery reporting.
Monitor AI Usage Routinely
Make it a habit to procure AI usage data, software inventory, and network activities, and check if there are any unauthorized AI activities.
It is more about mitigating risks rather than punishing innovation.
Evaluate the Features of SaaS Tools and Applications
Some SaaS tools may get AI features and functionalities during their updates. The IT and cybersecurity teams should check what kind of data AI can process and how.
Use Strict Access Control
A speedy approval process is useful for mitigating Shadow AI. Determine what the tool is doing, what data it is using, where the data flows to, what integrations it requires, and if an alternate has been approved.
Tools & Techniques You Can Use to Detect Shadow AI
Once you realize that shadow AI-related risks might impact your operations, there are quite sophisticated ways to deal with that:
Endpoint Security Agents: Lightweight solutions to find out any shadow information technology deployed on endpoints.
Automated Discovery Tools: The specific software solution scans networks to find any unregistered AI systems.
Logging/SIEM: Gather all the logs in your ecosystem to see if there is any pattern related to shadow IT.
DSPM (Data Security Posture Management): This solution maps and monitors sensitive data and identifies any exposure from shadow or AI misuse.
Vulnerability Scanning: Regular scans are extremely helpful to detect misconfiguration of the new or existing AI systems.
AI-SPM (AI Security Posture Management): This solution can monitor AI models and configuration to detect any unapproved deployment and access.
Best Practices to Follow
When a company finds an unauthorized AI system, the company should probe further before assuming malfeasance on the part of the employee.
Identify the System and its Function
Find out who uses the system, what purpose it is intended for within the business, what data goes into it, and whether it is integrated with any other systems.
Decide Whether to Approve or Disapprove the Tool
If there is enough business value in the use of the system and security standards are maintained, then approve the system for continued use. If not, deny or restrict access.
Turning a Risk into Governance
Here, the company turns a risk into governance, where they assess the use of the technology, determine compliance issues, engage stakeholders, and finally make decisions to either restrict, monitor, or adopt the system.
Conclusion
Shadow AI does not necessarily originate in malicious intent. Instead, it usually begins with a basic need of employees – to gain speed, convenience, or efficiency.
The actual risk is that a company fails to have proper oversight over the use of AI by employees, what information is being shared, and which systems can be accessed.
To mitigate the risks, a company can do several things, including setting up policies, using only approved tools, educating employees, monitoring, access management, vendor review, and an easy approval process.
The objective should not be preventing employees from using AI. Instead, it should be enabling safe and responsible use of AI.
In this way, AI can transform into a productivity benefit.
FAQs (Frequently Asked Questions)
Q1.What is your understanding of Shadow AI?
It is vital to be aware of the risks involved in relation to privacy, security, and access control.
Q2. Why does Shadow AI involve security risks?
It may result in disclosure of confidential data, use of insecure technologies, and unauthorized access to information and systems.
Q3. Is it possible to prevent the emergence of Shadow AI?
Shadow AI can be prevented from happening through the provision of adequate tools, policy development, employee training, and an effective monitoring process.
Q4. Is Shadow AI similar to shadow IT?
No, Shadow AI entails the utilization of unsuitable AI tools and creates further security challenges due to the processing of data and output of AI technologies.