Shadow AI: Harnessing and Securing Unsanctioned AI Use in Organizations

Learn about shadow AI and its profound impact on businesses. Explore the best governance strategies to ensure the use of responsible AI in your organization.

Haziqa Sajid

March 5, 2024

March 4, 2024

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As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.

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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.

English to French Translation:

Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?

As businesses rely more on AI, a new term is gaining prominence—shadow AI. The phenomenon includes the unsanctioned and ad-hoc use of generative AI tools without the explicit knowledge or oversight of an organization’s IT department. It’s an emerging trend fueled by the accessibility of consumer-grade AI tools.

This is evident in the rapid adoption of technologies like ChatGPT among employees.

Shadow AI introduces unpredictability, mainly due to the complex nature of AI systems. One factor that makes shadow AI more vulnerable is its potential for greater risks, including data privacy concerns and non-compliance with regulatory standards.

Think about how rapidly generative AI is becoming integrated into our daily tasks. Over 50% of US employees integrate Gen AI tools for work-related tasks like writing, designing, and coding. According to Salesforce, over one-third of employees incorporate AI tools into their daily routines.

As a result, the threat of shadow AI is not only present but growing—presenting challenges for corporate governance and risk management. As the use of generative AI accelerates, understanding and managing shadow AI becomes crucial. Finding the right balance between innovation and risk management is vital in today’s digital landscape.

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The Risks and Challenges of Shadow AI

As employees use shadow AI, several questions surface, highlighting risks and challenges previously unseen by C-suite and enterprise security teams.

Here are some of the risks and challenges organizations may face:

1. Dynamic Nature of AI Models

AI models, driven not just by code and data but also by the logic that learns from it, present a moving target. Unseen risks, such as bias, discrimination, and unexpected responses, can lead to setbacks for security, data, and compliance teams. The hidden risks in AI models, like bias, raise the chance of ethical violations and damage to reputation.

For example, a customer service AI chatbot may provide biased responses, favoring certain customer inquiries over others. This unseen bias can lead to unequal treatment. It risks the company’s reputation and may lead to potential legal implications.

2. Data Complexity and Privacy Concerns

The complexity of data generated by AI models raises concerns about its origin, use, and accuracy. This lack of transparency poses challenges to privacy and security, potentially exposing sensitive information to leaks. For instance, an employee using AI to analyze customer data may unintentionally expose sensitive information online.

Considering shadow AI risks, a prominent consumer electronics company, Samsung, decided to ban ChatGPT among employees after a sensitive data leak.

3. Security Risks with Unsecured Models

Expanding 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. Treating AI security as an afterthought threatens the integrity of the enterprise and the brand's reliability.

4. Uncontrolled Interactions and Potential Abuse

Unguarded prompts, agents, and assistants in the AI space create avenues for harmful interactions, threatening user safety and ethical principles. Security vulnerabilities like prompt injections and training data poisoning can also occur. For instance, a developer using an unsanctioned AI code assistant may unintentionally introduce vulnerable code snippets into the company's software.

It is important to understand how data generated by these models is used in various contexts. While serving legitimate queries, these agents can become potential targets for new attacks on AI systems.

5. Regulatory Compliance Challenges

Using AI without proper oversight poses challenges to complying with standards like the General Data Protection Regulation (GDPR). For instance, a marketing team deploying an AI-driven chatbot without proper oversight may collect user data without compliance.

Furthermore, global attention to responsible AI is evident in laws like the EU AI Act and other regulations like China’s AI regulations. This highlights the need to stay informed to avoid penalties and protect your business.

**💡Pro Tip: Read more: The EU AI Act: A Stepping Stone Towards Safe and Secure AI **

Lakera’s Contribution to Ensure Secure ChatGPT Usage

With ChatGPT’s widespread adoption in professional settings, there is a substantial risk of unintentional data leaks outside secure environments. Lakera has developed the Lakera Chrome Extension to provide you with ChatGPT data leak protection.

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Our plugin is designed to ensure secure use by providing timely notifications for potential private data in ChatGPT prompts. It grants you the authority to determine whether the context involves private data. Learn more about Lakera Chrome Extension here.

Identifying Shadow AI within Your Organization

Detecting and managing shadow AI requires a combination of technical controls and proactive measures to ensure that AI initiatives align with security and regulatory compliance requirements.

Here are key strategies to identify and address unauthorized AI use:

Active Monitoring and Testing

Active monitoring helps organizations identify shadow AI. Some strategies for active monitoring and testing include:

AI Audits: Conduct periodic AI audits to inventory all AI applications, verify their sources, and ensure compliance with company guidelines.

Network Traffic Analysis: Analyze network traffic for unique digital footprints of unsanctioned AI tools, such as frequent data transfers or unusual access patterns.

User Behavior Analysis: Recognize the critical role of user behavior analysis in identifying patterns indicative of shadow AI use. Unusual activities such as increased data usage at non-standard hours or frequent access to AI tool websites can serve as red flags.

Whistleblower Mechanisms: Encourage employees to report unauthorized AI use through confidential channels.

Technical Controls

You can also use existing technical controls to identify shadow AI usage in your organization. Some of them include:

Endpoint Detection and Response (EDR): Deploy EDR solutions to detect unauthorized AI apps’ installations, monitor their behavior, and promptly address security risks.

Vulnerability Scanning: Implement ongoing vulnerability scanning to identify security weaknesses associated with shadow AI. This helps ensure the security posture of your systems is robust.

Blocking Known External AI Sites: Evaluate your business risk profile and consider blocking access to known external AI sites from company networks and devices.

Network Traffic Analysis Tools: Implement network traffic analysis tools that flag the use of AI APIs not on the approved list.

The Impact of Shadow AI on Businesses

The emergence of shadow AI within businesses has brought about a delicate equilibrium between fostering innovation and the need for control to mitigate potential risks. Striking this balance is crucial for organizations aiming to benefit from AI while ensuring security and compliance.

Let’s explore the positive and negative impact of shadow AI on businesses.

Agility and Flexibility

Positive Impact: Shadow AI introduces heightened agility, allowing departments to experiment and implement AI solutions independently. This helps overcome the delays associated with traditional AI projects.

Risk Consideration: This autonomy brings the challenge of potential unregulated experimentation. It can lead to unforeseen consequences such as data breaches.

Domain Expertise

Positive Impact: Shadow AI empowers specific departments to apply their expertise to projects directly. This results in more tailored and effective solutions.

Risk Consideration: This localized expertise may lack a comprehensive understanding of broader organizational implications, raising the risk of overlooking compliance and security measures.

Urgent Needs

Positive Impact: Shadow AI provides a mechanism for swift responses to emerging challenges without waiting for centralized resources in rapidly evolving industries.

Risk Consideration: The urgency may lead to shortcuts and inadequate risk assessments, potentially exposing the organization to unforeseen risks and vulnerabilities.

As businesses navigate the impact of Shadow AI, they should carefully balance the opportunities with associated risks.

While generative AI tools help enhance employee productivity and innovation, they expose them to security risks. The positive impacts of Shadow AI provide avenues for innovation. However, a nuanced understanding of the risks is essential to ensure responsible integration and mitigate unintended consequences.

Lakera's Approach to Mitigating Shadow AI Risks

Lakera proactively aligns its security solutions to address the risks outlined in the OWASP Top 10 for LLM applications.

Moreover, with a comprehensive growing database of 30 million attacks and vigilant monitoring of the threats, Lakera ensures protection to mitigate the risks of shadow AI.

Lakera provides several solutions to address the risks in OWASP Top 10 and mitigate them. Some of them include:

Proactive Threat Monitoring: Lakera Guard enables proactive monitoring to stress-test foundational models and keep a vigilant eye on publicly available jailbreaks. This ensures that Lakera Guard is always updated on potential threats, offering real-time insights into emerging risks.

Balancing Access and Exposure: Lakera employs red teaming strategies to assess plugins, focusing on their permission settings and potential vulnerabilities. The key strategy is determining the 'right level of exposure' for a plugin, ensuring effective functionality without unnecessary risks.

Development of Protective Measures: Lakera Red identifies compromised systems based on their behavior. It enables teams to assess whether their models have been attacked, even after fine-tuning.

Vigilant Oversight: Lakera Guard allows organizations to continuously assess, track, report, and responsibly manage AI systems. This ensures ongoing monitoring and prompt addressing of AI risks.

Vulnerability Flagging: Lakera's systems continually monitor and identify potential vulnerabilities, proactively detecting and addressing security issues before they escalate.

Regular Security Alerts: Lakera issues regular security alerts. These alerts keep users informed about the latest security findings and potential risks, ensuring a constant awareness of their defense status.

Empowering Security with the Lakera Guard Dashboard

You can gain complete visibility into GenAI security with the Lakera Guard Dashboard. The dashboard provides:

Real-time Visibility: Monitor GenAI security vulnerabilities across your organization in real time.
In-Depth Analytics: Dig deep to understand how your LLM applications are being compromised and identify mitigating actions.
Instant Notifications: Receive instant alerts for any attacks or spikes in detections, enabling swift responses by your SOC team.

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Lakera empowers you to mitigate the risks linked with shadow AI while securely driving innovation.

Implementing Governance Strategies for Shadow AI

Organizations need clear governance strategies to manage shadow AI effectively. Here are five approaches to ensure responsible AI use in your organization.

1. Establish Clear AI Policies

Develop AI policies that address the challenges of AI within the organizations.

These policies should clearly define approved AI systems and outline a review and approval process for AI tools requested by departments. Simultaneously, communicate the consequences of using unauthorized AI tools to encourage a culture of responsibility and adherence in employees.

2. Provide Approved Tools and Training

Provide employees with approved AI tools tailored to their specific job requirements.

This helps mitigate the temptation to use unauthorized tools and reinforces Responsible AI use.

Moreover, develop educational and hands-on training programs to demonstrate responsible use of Gen AI tools.

Workshops, webinars, and self-paced e-learning modules can also help enhance employees’ understanding of risks associated with unsanctioned tools. This will also allow them to understand how to use AI responsibly without compromising company data security.

3. Map AI Systems in Use

Thoroughly understand and refine use policies by mapping all AI systems in use.

Identify key information such as system type, users, owners, dependencies, data sources, access points, and functional descriptions. This information is crucial for aligning governance with organizational goals.

4. Audit and Monitor AI Use

Implement regular audits and robust compliance monitoring mechanisms.

Use sophisticated software capable of detecting unusual network activity. This ensures early identification of unauthorized AI systems or applications.

5. Foster Transparency and Reporting Culture

Establish an open culture where employees feel comfortable reporting the use of unauthorized AI tools or systems without fear of retaliation.

This transparency facilitates a rapid response and remediation process, minimizing the impact of incidents.

6. Enable Constant Communication of AI Governance Policies

Communicate internal policies regarding generative AI tools extensively to employees.

Specify approved tools, purposes, and data usage guidelines for different organizational roles. Establish channels to consistently update and inform employees about any modifications. This ensures organizational adaptability, with every member committed to responsible AI usage.

Use Lakera for Improved AI Security

Lakera uses advanced tools to enhance the security of AI applications. A foundation of Lakera's defensive strategy is its AI behavioral analysis system – a real-time monitoring system that identifies and mitigates potential threats.

Lakera Guard executes the following strategic measures:

Blocking Suspicious Users: Lakera Guard can proactively block users demonstrating suspicious behavior, preventing any potential misuse of the AI model.
API Token Management: Users can configure Lakera Guard to block specific API tokens. This provides an added security layer and control over authorized access to the Large Language Model (LLM).
Preventing System Overload: Lakera Guard strategically mitigates the risk of system overload by identifying and neutralizing potential threats. This ensures the uninterrupted and secure operation of AI systems.

Future of Shadow AI: Trends and Predictions

Looking ahead at the future of shadow AI, organizations are recognizing the impracticality of completely preventing its use within the workplace.

Instead, there is a growing emphasis on strategically implementing guardrails around AI technology. This approach aims to ensure corporate data security while establishing clear governance guidelines.

The emergence of new-generation LLMs and the growing Software as a Service (SaaS) tools suggest a likely increase in shadow AI initiatives. Organizations must adopt flexible and adaptive governance strategies to navigate this evolving landscape.

Organizations must prepare for more sophisticated cyber-attacks. This requires the implementation of advanced detection techniques to safeguard against potential risks.

Jason Lau, ISACA board director, and CISO at Crypto.com, stresses the urgency for organizations to catch up with their employees and actively explore AI technologies. He stated:

“Employees are not waiting for permission to explore and leverage generative AI to bring value to their work, and it is clear that their organizations need to catch up in providing policies, guidance, and training to ensure the technology is used appropriately and ethically,”

Jason Lau

Lau emphasizes the need for comprehensive policies, guidance, and training to align understanding between employers and staff.

With this alignment, organizations can enhance their teams' understanding of AI technologies, maximize benefits, and protect themselves from associated risks.

The future of shadow AI calls for a strategic shift from prevention to proactive management. Organizations must balance the advantages of AI with robust security standards, adapt governance strategies to the changing threat landscape, and foster a culture of responsible and informed AI usage.

Key Takeaways

The rise of shadow AI involves the unauthorized use of generative AI tools without organizational oversight. This is driven by the accessibility of consumer-grade AI tools like ChatGPT.

This article discussed shadow AI, associated risks, and governance strategies.

Here are the main points:.

Shadow AI poses significant risks, including data privacy concerns, non-compliance with regulations, and security vulnerabilities due to the dynamic nature of AI models.
The challenges presented by shadow AI include dynamic AI models, data opacity, security risks, uncontrolled interactions, and regulatory compliance issues.
Technical controls and proactive security measures, like network traffic analysis and user behavior analysis, are required to detect and manage shadow AI in organizations.
While shadow AI increases data security risks, it presents opportunities for agility and productivity among employees.
Organizations need clear governance strategies to manage shadow AI effectively.
Organizations must shift from preventing shadow AI to proactive management, balancing AI advantages with robust security standards and fostering a culture of responsible and informed AI usage for long-term success.
Lakera addresses the risks associated with shadow AI through its Lakera Chrome Extension, providing data leak protection for ChatGPT.
Lakera Guard offers advanced security measures, including blocking suspicious users, API token management, and preventing system overload.

Lakera recommends adopting the following governance strategies for shadow AI:

Establishing clear AI policies
Providing approved tools and training
Mapping AI systems in use
Conducting audits
Fostering transparency
Ensuring constant communication of AI governance policies

Lakera is an industry-leading AI security solution to secure GenAI tools, specifically Large Language Models (LLMs). Lakera's proactive approach involves threat monitoring, balancing access and exposure, protective measures, vigilant oversight, and vulnerability flagging.

Create a free account to get started with Lakera today!

Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Download now

Haziqa Sajid

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