What is Shadow AI?
Shadow AI is a new form of an older issue, Shadow IT. Shadow IT is the employee practice of using apps, services, and programs without the authorization of their IT department. Shadow AI is the employee practice of using unauthorized LLMs (Large Language Models) and AI agents.
Why is Shadow AI a Threat to Cloud Network Security?
Shadow AI is much more of a risk than Shadow IT because AI is more powerful and autonomous than traditional software solutions.
AI can ingest, store, and use information on a larger scale with greater creativity than traditional applications. For example, it can process data from images, video, code, and structured data as well as plain text. It can act autonomously across systems, executing instructions without continual human oversight. It can make granular API (application programming interface) calls that are difficult to detect with traditional security tools.
Unchecked Shadow AI can damage your company’s integrity, reputation, and security posture by leading to:
Sensitive data leakage – LLMs and AI agents can expose sensitive data from your organization: customer payment information, confidential merger and acquisition plans, or intellectual property. For example, in 2023, Samsung banned employees from using ChatGPT after some leaked code and sensitive internal meeting notes into the LLM.
Compliance violations – While AI regulations are adopted more slowly than new AI tools are developed, new standards like ISO/IEC 42001, FedRAMP updates, the EU AI Act, and state-level legislation like California SB-1047 mandate controls for AI use: for example, certain types of data cannot legally leave a country or region. Shadow AI can cause companies to violate these standards. For example, a user in London could upload some website user data to ChatGPT for analysis. That data would be sent to locations in the US and India for processing, training, and learning, which violates those location restrictions. Other types of data such as PII (personally identifiable information) or PHI (personal health information) have even stricter standards. Violating those standards makes your company liable to audits and fines.
Reputational damage – A breach due to Shadow AI can erode brand trust. For customers, partners, and prospects, it can raise the question of what a company is doing with the data they have and if it’s being properly secured or used. If unapproved tools can be used, what chance do they have from stopping an attacker from compromising the data or system? Shadow IT/AI also expose organizations' cultures and how they operate. Well-run companies put measures in place to prevent this from happening; chaotic companies don’t.
How Can I Protect My Network from Shadow AI?
The key to protecting your organization from shadow AI-related threats is zero trust cloud security. Zero trust sets up proactive controls against inside and outside risks by enforcing least-privilege access, continuous visibility, and ongoing safeguards to prevent data theft and unauthorized access.
Alongside training your employees on the risks of shadow AI, here are some zero trust principles you can implement to protect your organization through the network layer:
Centralized visibility – Shadow AI thrives in fragmented cloud environments with multiple blind spots and a lack of consistent security policies. Prioritize centralized, network-wide visibility over your network’s workloads, traffic flows, and users.
Egress filtering – One of the greatest dangers of Shadow AI is data leakage. Stop data leakage in the act by filtering egress or outbound network traffic and enforcing security policies that prevent sensitive data from going to suspicious locations.
Identity verification – Most security policies are designed to regulate the actions of human users, but AI agents are not human. Adjust your security policies to cover the actions of both humans and agents.
Network segmentation – AI often requires connectivity to multiple systems and data sources – but no user or agent should have unlimited access to your whole network. Use network segmentation, also called microsegmentation, to segment pieces of your network that don’t need to communicate and prevent massive data thefts.
Encryption – Make sure AI cannot access and move unencrypted data outside your network. Even if AI accidentally sends data out of your network, encrypting workload-to-workload traffic ensures that data is unusable.
The advantage of these zero trust security principles is that they provide proactive, pervasive, and holistic cloud network defense – freeing your organization to enjoy the benefits of AI without the risks of Shadow AI.
