What is AI Shadow Data? Hidden Information Used by Algorithms

In the light of embracing digital transformations, organizations use AI platforms to conduct their daily activities within the workplace environment. However, these developments have created another cyber vulnerability: AI Shadow Data. 

Shadow data includes sensitive, uncontrolled, and proprietary information submitted to unsanctioned AI algorithms, consumer AI generators, and browsing AI tools without the enterprise IT department’s consent.

Unlike traditional database records kept behind the organizational firewall, shadow data moves through external application programming interfaces (APIs), third-party plugins, and web prompts. 

In order to help security leaders protect intellectual property and to enable innovation for employees, it is important to learn what is AI shadow data.

How Shadow Data Makes Its Way Into the AI Environment

In order to understand how shadow data accumulates, it is important to consider the modern day-to-day employee behavior in the workplace. Employees do not intentionally submit corporate secrets to the internet. 

Instead, well-intentioned employees try to automate repetitive tasks, debug source code, write emails to customers, and create summaries of internal meetings.

Difference between Shadow IT, Shadow AI, and Shadow Data

For understanding security exposures in today’s environment, it is necessary to understand the difference between three related but distinct technical terms:

• Shadow IT

  Widespread usage of unauthorized software, devices, and cloud infrastructure (personal cloud storage or other messaging applications) within the business organization.

• Shadow AI

  Usage or deployment of artificial intelligence algorithms, agents, and generative software in the absence of IT security approvals.

• AI Shadow Data

  The sensitive data sets that include proprietary code, customers’ PII, or internal data that is sent to and consumed by the unauthorized AI algorithms. Unlike shadow IT, which involves unauthorized access to software, AI shadow data involves handling sensitive information through these algorithms.

Main Risks Posed by AI Shadow Data

With confidential data, organizations expose themselves to considerable risks in several spheres:

1. Data Leakage and Model Training

Many publicly available AI generators use user prompts to train future versions of the base models. As a result, any confidential data employees provide through prompts could be used by these models when answering other users’ requests.

2. Non-Compliance with Regulations

Regulated industries have to comply with rigorous data protection policies, including GDPR, HIPAA, and the DPDP Act. When PII and health metrics are uploaded to unauthorized AI systems, organizations violate regulatory compliance guidelines and become subject to legal action.

3. Widening of Organizational Attack Surface

Unmanaged AI extensions and plugins usually demand higher permission levels. When the unauthorized vendor is hacked, cybercriminals can use the access obtained as a back door to enter organizational networks.

4. Loss of Data Visibility and Auditing

SOCs use visible audit logs to assess network security. As shadow data flows outside of regular security gateways, SOC analysts lack visibility into the storage locations of the data, permissions to access it, and decision-making.

Governance Approaches for Ensuring AI Shadow Data Security

Businesses should try implementing the following security approach:

• Centralize AI Gateways Usage: All company AI traffic needs to be centralized through a dedicated platform to allow comprehensive logging, filtering, and prompt inspection.
• Data Policy Definition: Formulate detailed policies regarding data classification, such as classifying confidential customer information, finances, and code as off-limits in public models.
• Enterprise AI Solutions Approved by the Company: Supply the workforce with approved AI programs that will ensure privacy of the data, as they do not record or train on user prompts.
• System Endpoint Auditing via Automation: Automate system endpoint auditing to monitor any suspicious browser extension, desktop program, and API access to AI systems.

Conclusion

Understanding what is AI shadow data helps to see the fine line that exists between innovation in the workplace and cybersecurity. Organizations are able to utilize the advanced algorithms without putting their sensitive information at risk due to enterprise-approved software, data governance, and instructions for staff.

Frequently Asked Questions