Seeing GenAI interactions in context gives IT more concrete insight https://demagneet.eu/wso2-completes-93-million-series-e-growth-funding-round-with-the-investment-from-info-edge/ into whether a tool should be embraced or blocked. This means you can more clearly understand what users are feeding into GenAI. Then, the race is on to control this shadow AI app across the company to ensure sensitive data doesn’t leak. The hype around it promises speedy content curation and analysis, but it can be used outside corporate-approved channels.
It isn’t just a rogue app or unvetted code—it’s a clandestine phenomenon, where the same innovative tools driving workplace productivity turn into information security risks across the board. Some security teams recognize this and are starting to implement shadow AI discovery and governance programs to get ahead of the risk, rather than play catch-up. Shadow AI is only becoming more deeply ingrained in enterprise workflows, and it remains largely invisible to traditional security measures. Essentially, more than one in four employees at a small firm might be using some AI app without IT’s knowledge.
This means sensitive company data was flowing through those tools for months on end outside official https://olbio.info/2024/05/16/the-role-of-ai-in-enhancing-cybersecurity/ channels. For example, two particular tools were found to have median usage durations of about 403 and 401 days, respectively, over a year of continuous use. Over 53% of all shadow AI activity across surveyed enterprises involved OpenAI, representing more than 10,000 active enterprise users.
Compare endpoint DLP to other shadow AI security tools
Shadow AI refers to the unauthorized or unmanaged use of AI tools, models, frameworks, APIs or platforms within an organization, operating outside established governance frameworks. As AI tools and services become easier to adopt, more models, agents, and APIs slip into codebases without review, creating blind spots for AppSec teams and compliance leaders. This allows employees to interact with an AI application while the organization controls what can be copied, pasted, printed, or downloaded from that session. Browser Isolation runs AI tool sessions in a remote, controlled environment rather than directly in the user’s browser. The vendor may retain that data, use it for model training, or store it in ways that create compliance, legal, or reputational exposure the organization cannot detect or contain. The risk is that sensitive data entered into these unsanctioned platforms sits outside the organization’s visibility and control.
Establishing AI Use Policies and Understanding Use Cases
Schedule a call with Zscaler today or learn more about our Unified Data Protection platform. Inline DLP intercepts at the moment of input with real-time content governance strategies that restrict sensitive data flow—all without killing the app. Never again will credit card numbers make their way into ChatGPT prompts. This tool micromanages what data can, or cannot, be entered into prompts for unsanctioned apps. With Zscaler, you can enforce blanket “deny lists” for persistent shadow AI perpetrators like ChatGPT or DeepSeek.
- In manufacturing, engineering specifications and proprietary process data entered into AI tools represent IP loss that may not surface for months or years after the exposure.
- Secure Web Gateway tools with SSL/TLS inspection can decrypt and analyze encrypted traffic, revealing data uploads to generative AI tools that would otherwise appear as generic HTTPS sessions.
- It actively processes it, often with outputs that reflect sensitive patterns and that can persist well beyond the original session.
- There is no deletion mechanism and no guarantee the data won’t surface in outputs for other users.
- A product manager summarizes an internal strategy deck in a public AI chatbot before sharing it with a vendor.
- Network detection and response remains the foundational layer because traffic analysis to generative AI endpoints provides visibility regardless of which tools employees choose.
- Harmonic Security’s analysis of 22.4 million enterprise AI prompts found 665 distinct generative AI tools operating across enterprise environments, yet only 40% of companies had purchased official AI subscriptions.
- As AI evolves from chatbots to autonomous agents, the risk surface is expanding faster than most security teams realize.
- Emerging capabilities include AI-native security platforms, SaaS posture management, browser-layer DLP, and identity-aware AI monitoring.
- Agentic shadow AI involves an autonomous agent with API access that chains actions across multiple services, runs continuously, and makes decisions without human review.
Two AI applications that gained thousands of enterprise users each have security scores so low that they should be disqualified from any enterprise environment. Workers in every department are integrating AI into daily workflows and shadow AI has basically infiltrated nearly every corner of the enterprise, creating massive blind spots that traditional security tools can’t cover. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Security teams must take the initiative and lead this change.
Strategies for CISOs to manage shadow AI
Employees use shadow AI despite its risks because it fosters innovation and allows quick experimentation with new tools, especially in fast-paced environments where time-to-market is critical. These containers can run on platforms like Kubernetes, but their encapsulated nature makes it hard for security teams to inspect their contents, leading to the rise of Shadow AI, where unmonitored AI components can operate undetected. Modern development practices allow developers to integrate AI models directly into CI/CD pipelines https://desyrel.eu/wso2-launches-wso2-private-ciam-cloud-to-deliver-best-in-class-ciam-support-for-both-b2b-and-b2c-demands/ without security oversight. AI tools are now more accessible than ever, with many being free, inexpensive, or requiring minimal setup, allowing non-technical users to adopt them easily without IT involvement. This creates an environment where AI components operate in the shadows of an organization’s codebase, invisible to security oversight but fully functional within applications. Developers don’t ask the application security team what to do when it comes to AI use.
