Have you ever asked a generative AI tool for help with a work project? More importantly, did your management know about it? Microsoft estimates that 75 percent of global knowledge workers use GenAI tools — but 78 percent of them use their own choice of software, and more than half don’t have formal employer approval.

As generative AI becomes a daily go-to, a new security challenge is emerging: shadow AI. Like shadow IT, where employees use unauthorized software and hardware, shadow AI refers to using GenAI applications without enterprise oversight or approval. While tools such as ChatGPT and QuillBot can boost productivity — and, let’s be honest, they’re fun to play with too — their unauthorized use can pose significant risks to enterprise security.

The “whoops” nature of AI risk Modern AI applications are inherently distributed, with interactions occurring among endpoints, data centers, and cloud services. This distributed architecture creates multiple attack vectors for cybercriminals. When employees use unauthorized AI tools, they may inadvertently expose sensitive company data, customer information, or intellectual property.

Consider a common scenario: an employee pastes confidential information into a public AI chat interface to help draft a report or analyze data. While this might seem harmless, the data is now outside the enterprise's control. It could be used to train the AI model, potentially exposing it to competitors or bad actors. Moreover, the data transmission occurs outside established security protocols, bypassing essential safeguards.

AI’s role in cyberattacks: beyond data leakage AI can multiply the threats of increasingly sophisticated and rapid cyberattacks. Bad actors are using AI to accelerate scans for vulnerabilities, taking advantage of windows between vulnerability discovery and patching that can extend to weeks or even months. GenAI chatbots can help attackers evolve existing attack signatures and develop zero-day exploits that can bypass traditional security defenses. Once inside a network, these AI-enhanced attacks can move laterally and quickly across the enterprise, specifically targeting valuable AI models and datasets. The ultimate objective often involves either poisoning the organization's AI models or exfiltrating sensitive data.

The risks extend beyond immediate data exposure. According to security experts, threat actors often establish persistence in networks through seemingly innocent applications. They might remain dormant for extended periods, activating only under specific conditions — perhaps at month's end or when triggered by certain system events. Traditional security mechanisms can miss these sophisticated threats, especially when they piggyback on legitimate-looking AI application traffic.

How to secure your AI landscape To address these challenges, enterprises need a multi-layered security approach.

• Comprehensive visibility and control: Organizations must first gain visibility into GenAI application usage across their networks. Cloud access security broker (CASB) software, found in SASE solutions, can help identify shadow AI applications and provide insights into their risk profiles and usage patterns.
• Zero-trust network access: As AI development teams become more geographically distributed, implementing zero-trust principles becomes crucial. This framework requires thorough verification of user credentials and device security posture, along with strict access controls based on user and device risk profiles. Organizations must limit application access to only what's necessary for specific roles, ensuring that users can access only the AI resources required for their work.
• Network segmentation prevents lateral movement of threats within the network. If a threat actor compromises a guest segment, robust segmentation prevents them from accessing sensitive AI workloads or data. This separation is essential for maintaining the integrity of AI Operations (AIOps) while allowing necessary business functions to continue.
• Protecting data in motion, given the distributed nature of AI applications, is as crucial as securing it at rest. This protection requires encrypted data transmission combined with comprehensive data loss prevention (DLP) capabilities. Organizations must ensure compliance with regulatory requirements such as GDPR, HIPAA, and PCI across all data movements involving AI systems.
• Centralized policy management: Instead of managing security in silos, organizations need centralized policy orchestration with distributed enforcement. This approach ensures consistent security controls across all network segments while maintaining performance through local policy enforcement. Centralized management simplifies security operations while distributed enforcement ensures efficient processing of AI workloads without creating network bottlenecks.

To effectively manage AI security risks, organizations should implement a comprehensive governance framework that addresses several key areas of concern.

First, companies need detailed policies that specifically address GenAI application usage to clarify which tools are approved for business use and what types of data employees can use with them. A formal procedure for requesting and approving new GenAI tools ensures that security considerations are addressed before deployment. The policies should also establish data classification levels and explicitly specify which categories of data are prohibited from AI tool input.

Modern threat detection and continuous monitoring form another crucial component of AI security. Today's threat landscape requires analyzing billions of data signals daily to identify evolving AI-related threats. Organizations can tighten their security posture with a solution underpinned by a robust threat intelligence gathering system that uses AI and ML to speed discovery and action based on threat signals coming from common points of entry like endpoints, email, or the internet. They should implement AI-powered security platforms capable of detecting anomalous patterns in AI application usage, unusual data transfers, and potential data exfiltration attempts. This monitoring must cover both north-south traffic between branch locations and data centers, as well as branch-to-branch or remote-user-to-branch traffic that might indicate lateral movement of threats.

Regular security control assessments must evolve to encompass GenAI-specific considerations. Security teams should regularly evaluate the security posture of approved AI vendors and test DLP controls with AI-specific scenarios. Network segmentation effectiveness for AI workloads requires ongoing review, as does the validation of zero-trust access controls for AI applications. Regular assessment of encryption effectiveness for AI-related data in transit ensures that sensitive information remains protected.

Employee training programs need to go beyond basic security awareness when it comes to GenAI. Effective training should provide hands-on instruction with approved AI tools and clear examples of acceptable and unacceptable AI use cases. Employees must understand data handling procedures specific to AI interactions and learn to recognize AI-specific security risks and attack vectors. Training should also cover proper procedures for reporting suspected AI security incidents.

Finally, incident response planning must adapt to address unique GenAI-related challenges. Response procedures should detail methods for identifying and containing AI data exposure incidents, as well as assessing the scope of potential data compromise in AI systems. Organizations need clear procedures for disconnecting compromised AI applications when necessary, along with communication protocols specific to AI-related security breaches. Recovery procedures must account for the unique characteristics of AI workloads and associated data.

Looking ahead As AI adoption accelerates, the challenge of shadow AI will only grow. Organizations must balance the productivity benefits of AI tools with robust security measures. This requires not just technical controls but also a cultural shift in how employees think about AI security.

The key is to provide secure, approved alternatives to popular AI tools while maintaining visibility and control over data flows. By implementing comprehensive security frameworks now, organizations can harness AI's benefits while protecting their critical assets from emerging threats.