Cyber threats are very real and growing significantly. At the same time, organizations are wholeheartedly embracing artificial intelligence (AI) tools and leveraging them across departments. Although AI promises undeniable advantages, it comes with its own set of risks.
Most notably and lurking in the shadows is an almost invisible risk known as shadow AI.
Humans have always been the weakest link in the cybersecurity chain. Despite constant awareness and training, employees continue to bypass protocols and officially approved applications that can be utilized in a corporate environment. Thus, the birth of the term shadow AI, which refers to employees discreetly adopting generative AI tools.
Organizations can adopt the latest security measures, and yet a single blind spot can cripple all initiatives. The real danger is evident; sensitive data is at risk. Hence, understanding Shadow AI is imperative to evade inadvertent sensitive data exposure.
What is Shadow AI?
Shadow AI is the unauthorized, unapproved, and undocumented use of AI-powered tools, applications, models, and systems within an organization, without the organization’s knowledge, explicit approval, or security oversight. A staggering 90% of AI usage in the enterprise occurs without the knowledge of security and IT teams, exposing organizations to risks such as data leakage and unauthorized access.
Shadow AI shares similarities with shadow IT, where technology, apps, tools, or services are used without approval from an organization's IT department. The primary reason for its usage is often to improve efficiency and streamline workflows.
Shadow AI is often hiding in plain sight, embedded in the workflows of nearly every business unit and sector. Employees across industries are using GenAI for efficiency, often outside of IT-sanctioned governance.
Although utilizing unapproved AI tools might not involve ill intention, the lack of visibility and governance by IT teams makes shadow AI a silent threat that could expose data to malicious actors.
The Silent Threats of Shadow AI
Shadow AI consists of inherent risks that compromise data integrity, privacy and security. This is primarily because AI tools are data-hungry systems that accumulate vast amounts of information provided to them and add this information to a broader database that may train AI models.
Employees who, without realizing the full extent of how AI tools operate, may input sensitive data into unauthorized apps. Those apps may inadvertently expose sensitive information to external entities that lack secure data handling practices.
Several AI tools clearly outline in their privacy policy: Sensitive data could be processed outside your organization’s security perimeter. This evidently states that an organization’s confidential data may be utilized or shared with third parties.
According to the Varonis State of Data Security Report, 99% of organizations have sensitive data dangerously exposed to AI tools. The same report also details that 98% of companies have employees using unsanctioned apps, including shadow AI, with each company having 1,200 unofficial apps on average.
Since Shadow AI operates outside of formal oversight, it leads to:
a. Unauthorized Sensitive Data Processing
An organization’s employees may mistakenly inject proprietary, highly sensitive, confidential data into unapproved, unsanctioned AI systems without fully understanding its implications, such as whether the AI tool will store this data, if so, for how long, and whether it will be shared externally or processed to train the AI model, etc.
b. Expansion of Threat Surface
Cyber threats are already on the rise. Feeding proprietary information and sensitive data to AI models further expands the attack surface by attracting malicious actors to snoop into unsecure APIs, models that lack security protocols and compromise data.
c. Model Poisoning and Corrupt Outputs
Not all AI models are created with good intent. Even if they are, they might be trained on malicious and inaccurate data that the employees can use in an official setting, resulting in biased and manipulated information.
d. Regulatory and Compliance Gaps
Lack of data privacy and security leads to heightened risk of data exposure and compliance violations with regulations such as GDPR, CCPA/CPRA, HIPAA, the EU AI Act, etc.
e. Lack of Traceability and Accountability
Unsanctioned AI models are unapproved for a reason. They may produce biased and inaccurate outputs that are often untraceable, meaning the information utilized by an employee may turn out to be corrupt, and there’s no way of verifying which data was injected or processed and how the output was generated.
Shadow AI vs. Sanctioned AI
Unlike shadow AI, which is unregulated and unauthorized, sanctioned AI involves the use of vetted, secure, and IT-approved AI tools. The reason why an IT team approved such AI platforms is probably that the platform offers built-in security safeguards, aligns with corporate policies and regulatory requirements, and, most importantly, has transparent governance controls.
Sanctioned AI tools empower employees to leverage the platform with ease as they are developed under corporate governance. On the other hand, shadow AI bypasses IT oversight, resulting in employees using the AI tool in silos and enhancing the threat surface. This results in the expansion of invisible vulnerabilities that may only surface when issues escalate.
How to Protect Against Shadow AI
According to Gartner, 41% of employees in 2022 installed and used applications that were beyond the visibility of their IT departments. This figure is forecasted to rise to 75% by 2027. To protect against shadow AI, organizations need to adopt a proactive approach, including:
a. Gain Comprehensive Visibility
You cannot secure what you cannot see. Understand that shadow AI already exists within your organization and automate the discovery of AI usage. Determine which AI models/apps are being utilized and authorize usage accordingly.
b. Establish a Clear AI Usage Policy
Establish a dedicated AI usage policy detailing which AI tools are approved, whether employees can input sensitive data, and what safeguards exist. Govern accessibility through corporate identities and monitor usage accordingly. Adopt a proactive approach rather than a reactive approach.
c. Train Employees
Educate employees and all relevant stakeholders on the safe usage of AI tools. Maintain a vetted list of AI tools and block unauthorized tools. Provide regular training to employees on the dangers of utilizing shadow AI tools, such as data leakage and hallucinated outputs, which could compromise data integrity and result in noncompliance penalties.
Enabling Fast, Safe Adoption of AI in Your Organization
Overcoming challenges associated with shadow AI requires a robust data and AI security framework that automates the discovery of AI systems that are being utilized within the organization. Securiti enables organizations to discover, assess and safeguard AI usage across the organization.
Request a demo to learn more about how Securiti helps organizations overcome shadow AI.
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