Shadow AI is presenting new challenges for security leaders. While AI tools have already revolutionized how we work, they've also created unprecedented security challenges that our traditional strategies or tools simply weren't designed to handle.
I've spent the last decade working with organizations grappling with emerging tech risks, and I can tell you that this is different. In this post, we’ll talk about why, and more importantly, what you can do about it.
The Hidden Risks of AI Adoption: Shadow AI#
The Wiz research team recently uncovered a publicly exposed DeepSeek production ClickHouse database, leaking chat history, API secrets, and other sensitive data—raising serious concerns for any organization using DeepSeek’s models. Truth is that many teams rushed to try out DeepSeek given the hype around its truly advanced technologies. While the DeepSeek situation has been surrounded by FUD, drama, and misinformation, it has also set important precedents for privacy and security. In response to the incident:
- Texas banned DeepSeek from government devices.
- AI malware impersonating DeepSeek has appeared in PyPi, posing additional security risks.
- Taiwan and Italy have restricted DeepSeek, citing data privacy concerns.
- Despite the controversy, DeepSeek-R1 models are now available on AWS, raising further questions about adoption and security oversight.
This is a clear reminder that without the right safeguards, AI tools can introduce serious risks—and organizations will still use them because of the enhanced productivity value. I’m a firm believer that cybersecurity should be a business enabler, not a roadblock. So how do we ensure our organizations can adopt these technologies without exposing sensitive data? There’s a way, and we’ll get into it in the next sections!
A perfect example of this challenge is Samsung’s ChatGPT source code leak in early 2023. An engineer inadvertently shared sensitive source code via ChatGPT leading to a source code leak and prompting the company to temporarily ban generative AI (GenAI) tools. The key takeaway here is that employees will adopt new technologies quickly, often before security teams can assess the risks—even in some of the most mature security organizations.
The Hidden Cost of Innovation: Why Shadow AI Is Different#
Here’s why security leaders are facing a unique challenge—new AI tools are popping up constantly, each designed to solve a specific business problem, and teams are adopting them faster than security can keep up. When marketing, HR, accounting, developers, and just about every other team across the organization is adopting new AI tools, traditional security reviews often introduce weeks-long delays.
How do you tell a team that’s just doubled their productivity to stop using their favorite AI tool because it might be a security risk? In many cases, you don’t—by the time security steps in, AI adoption is already ingrained in workflows, making outright bans is a non-starter and one that can put security leaders at odds with their counterparts.
Another major challenge is that these tools are data sponges, capable of retaining and reproducing sensitive information in ways we’ve never encountered before. Have you ever asked ChatGPT what it thinks about you? Or asked it to summarize your character based on your interactions?
This is an example of how it looks like - remember that time when you asked for a specific recipe with no sugar for diabetics? The AI service retained that info from 6 months ago, and now knows about your health conditions. And since many of these AI tools operate as SaaS apps, they introduce an entirely new layer of risk—data flowing to third-party platforms for AI model training which falls outside traditional security controls. The reality is, most data security and SaaS security tools weren’t built to handle the dynamic, unpredictable nature of the AI apps we’re seeing.
Creating a Culture of Secure Innovation#
Successful security teams recognize that securing AI adoption is not just a technical challenge; it is also a cultural one. Instead of constantly reacting to unauthorized AI tools, they are building what I call an "AI-positive security culture"—one that enables innovation while maintaining security oversight.
What does this look like in practice? It starts with proactive governance and Shadow AI detection to identify and control unsanctioned AI apps before they become security liabilities. Key elements include:
- Cross-functional AI governance committees that bring together security, IT, legal, and business teams to establish clear guidelines for safe AI adoption. This is especially critical for heavily regulated industries like finance, critical infrastructure, and healthcare.
- Automated discovery of Shadow AI usage to provide real-time visibility into unauthorized AI apps and data flows.
- Clear, enforceable policies that educate teams on which AI tools are approved, what data can be used, and how to integrate them securely.
Gameplan: How to Win in the Shadow AI Era #
Security teams need a structured approach that combines detection, policy enforcement, and continuous monitoring. Here is a practical roadmap to get started:
Immediate Steps:#
- Map your AI exposure. Use Shadow AI detection tools to uncover which AI apps employees are already using, rather than relying on self-reporting. Encourage transparency instead of penalizing usage.
- Identify your crown jewels. Determine which data must be strictly protected from external AI tools and establish clear policies and access controls to enforce this.
- Enforce AI security policies. Leverage CASBs, SaaS Security Posture Management (SSPM), and AI-aware DLP solutions to detect and block unauthorized AI data sharing.
Long-Term Strategies:#
- Build an AI governance framework that evolves with the business to ensure policies remain relevant as AI tools advance.
- Invest in AI-specific company-wide security training that explains why AI tools pose risks, not just a list of restricted apps.
- Deploy continuous monitoring to flag unauthorized AI services as they emerge.
How Reco Helps Organizations Secure Shadow AI#
Security teams can’t afford to chase down every AI tool that enters the enterprise. Furthermore, asking people to report all the AI tools they use is not a scalable strategy. And while traditional solutions such as CASBs and browser plugins can offer insight into some shadow services, they fail to recognize AI assistants or copilots that share a domain with your approved tools.
So how can you take back control of the AI tools being used in your enterprise? Reco can help. Reco is a dynamic SaaS security solution that keeps pace with the SaaS rate of change. Reco can discover all the shadow AI tools operating in your environment. In fact, it will produce an alert the moment a new AI tool is spun up and provide intelligence on who is using what, how they’re authenticating, and how risky each service is. It ranks alerts in order or severity and provides business context, empowering security teams to take appropriate actions to manage and prioritize the risks associated with shadow AI.
Learn more about how Reco works in this article: Product Walkthrough: How Reco Discovers Shadow AI in SaaS.
About Author: Dvir is the Director of Security Research at Reco, where he contributes a vast array of cybersecurity expertise gained over a decade in both offensive and defensive capacities. His areas of specialization include red team operations, incident response, security operations, governance, security research, threat intelligence, and safeguarding cloud environments. With certifications in CISSP and OSCP, Dvir is passionate about problem-solving, developing automation scripts in PowerShell and Python, and delving into the mechanics of breaking things.
Dvir Sasson — Director of Security Research at Reco https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjIsDXLDxfzD9NNyhetG4qGm4HaH5fMaUR37JZKxYOBeVjJl6TZwD9tesXBL_GQkxJ0QRRHJGFh3Gsey6y0RzgFhiXhCLqlnFAJjHjDgExbmhRZvobaG8u8CSJSfiWv1-BPpSMqwxMYWfsYOWvamq2FoZWVleZYzVcObrq5rQDPP2UJP9wo-XjkXipd_Ad9/s100-rw-e365/reco.png
