Must Learn AI Security Compendium 16: Shadow AI
Out of Band 16
This post is part of an ongoing series to educate about new and known security vulnerabilities against AI.
The full series index (including code, queries, and detections) is located here:
https://aka.ms/MustLearnAISecurity
The book version (pdf) of this series is located here: https://github.com/rod-trent/OpenAISecurity/tree/main/Must_Learn/Book_Version
The book will be updated when each new part in this series is released.
Periodically, throughout the Must Learn AI Security series, there will be a need to envelop previous chapters and prepare for upcoming chapters. These Compendiums serve as juncture points for the series, even though they might function well as standalone articles. So, welcome! This post serves as one of those compendiums. It’ll all make much more sense as the series progresses.
Historically, Shadow AI has been known as dark AI or black box AI that refers to the negative consequences that can arise from using artificial intelligence systems without fully understanding how they work. However, with wider adoption and use of Generative AI, Shadow AI has taken on a new aspect in terms of security.
Shadow AI is now a term that refers to artificial intelligence systems that are developed or used without the knowledge or control of the IT department or the organization’s leaders. Shadow AI can pose significant risks to the security and privacy of an organization, as well as its reputation and compliance. Some of the potential impacts of shadow AI are:
Data leakage: Shadow AI can expose sensitive or confidential data to unauthorized parties, either intentionally or unintentionally. For example, an employee may use a third-party AI service to process customer data without proper encryption or consent, resulting in data breaches or violations of privacy laws.
Model poisoning and theft: Shadow AI can compromise the integrity or availability of the AI models that the organization relies on. For example, an attacker may inject malicious data or code into a shadow AI system to alter its behavior or performance or steal the model and its intellectual property.
Unethical or biased outcomes: Shadow AI can produce results that are inconsistent with the organization’s values or standards, or that harm its stakeholders. For example, a shadow AI system may generate misleading or discriminatory content or recommendations or make decisions that are unfair or inaccurate.
Lack of accountability and governance: Shadow AI can create challenges for the organization to monitor, audit, or explain the AI systems and their outcomes. For example, a shadow AI system may operate without proper documentation, testing, or validation, or without following the organization’s policies or regulations.
To prevent or mitigate the negative impacts of shadow AI, organizations should adopt a proactive and strategic approach to responsible generative AI. This includes:
Establishing a clear vision and strategy for AI that aligns with the organization’s mission and values and communicates it to all employees and stakeholders.
Creating a culture of trust and collaboration among the IT department, the business units, and the end users, and providing them with the necessary training, tools, and support to use AI effectively and ethically.
Implementing a robust governance framework for AI that defines the roles, responsibilities, and processes for developing, deploying, and managing AI systems, and ensures compliance with the relevant laws and regulations.
Leveraging the best practices and standards for AI, such as the Microsoft Responsible AI principles, and applying them throughout the AI lifecycle, from design to evaluation.
Monitoring and reviewing the AI systems and their outcomes regularly and addressing any issues or risks promptly and transparently.
Shadow AI can be a source of innovation and value for an organization, but it can also pose serious threats to its security and privacy. By adopting a responsible and strategic approach to generative AI, organizations can harness the benefits of AI while minimizing the risks of shadow AI.
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