Shadow AI Security in SaaS: Challenges, Risks, and How to Protect Data

The recent surge in generative AI (GenAI) adoption has sparked a race amongst companies to integrate GenAI into their products and services, seeking to deliver more value to users. That’s good news for efficiency-seeking employees, but unfortunately it's given rise to a new security concern: shadow AI. A subset of shadow IT, shadow AI refers to the unapproved or unsanctioned use of AI tools and copilots within an organization. While AI can boost efficiency and innovation, when left unregulated, it can create serious security risks.

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Types of Shadow AI

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Shadow AI tools can be categorized into three types based on how they interact with systems. 

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Standalone Shadow AI

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Standalone shadow AI applications are AI tools that are being used by employees to support business functions, but they’re not integrated with corporate infrastructure. For example, a developer uses a ChatGPT instance to assist with writing code or a marketing manager uses Jasper to generate content. While these applications may seem harmless at the surface, there’s still the chance that employees will expose private data by inputting company information into these tools. In fact, recent studies indicate that 15% of employees regularly post company data into AI tools, with over a quarter of that data being sensitive information.

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Integrated Shadow AI

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Shadow AI applications that are connected to the organization's approved systems are much more dangerous. Through APIs or other integration points, these tools may exchange information with approved applications or share access across multiple platforms. This broadens the attack surface area as threat actors can potentially use these shadow AI platforms as a gateway to access other SaaS applications.

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Shadow AI Copilots

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AI copilots are GenAI assistants that are embedded within applications and designed to streamline tasks and increase employee productivity. They can help analyze data, generate content, or automate processes within a given app. Because shadow AI copilots are often built into approved business applications, they can be even more tricky for Security teams to discover and secure.

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Shadow AI Security Challenges for SaaS Security

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Although AI tools are usually used with good intentions, they create huge challenges for security teams.

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Visibility and Detection

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While traditional shadow IT tools can often be identified through network monitoring, shadow AI presents unique visibility challenges due to its ability to embed itself within approved applications and operate through personal accounts. One study found that 55% of data loss protection events involve users sharing personally identifiable information with generative AI sites, often through seemingly normal business activities.

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Lack of Control

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Spinning up a generative AI environment is as easy as downloading an app from the internet and creating a password. The self-service nature of SaaS makes it ideal for supporting innovation, but unfortunately this makes it difficult for IT and Security teams to stay on top of who is using what and how. 

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Additionally, AI offerings are constantly changing and being updated. New AI copilots are constantly being added to approved software products that employees can start engaging with, usually without consent from Security teams.

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Permanent Data Exposure

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Once employees begin uploading data to AI systems, that information can never be fully recalled or erased. This permanence creates a unique challenge where detection must focus on prevention rather than remediation, as traditional data recovery methods prove ineffective.

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Shadow AI Security Risks

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Like traditional shadow applications, shadow AI applications run the risk of becoming compromised, but they also create unique risks due to their ability to ingest and share information.

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Data Exposure

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The most severe risk stems from permanent data exposure through model sharing and training. When employees input sensitive information into consumer AI tools, that data becomes irretrievable as it enters the AI makers' black box of training models. Recent studies show that 38% of employees share sensitive work information without permission, and over 80% of legal documents are exposed through non-corporate AI accounts.

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Increased Risk of breach

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Shadow AI increases the attack surface area by introducing unapproved and unmonitored AI tools into an organization's environment. These tools often integrate with other applications via APIs, creating new entry points for attackers. Without IT oversight, they may lack proper security configurations, increasing the risk of exploitation or lateral movement within the network.

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Unauthorized Access

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AI operators gain persistent access to corporate information through human review of customer prompts and training data. This risk intensifies when employees use personal accounts that lack enterprise security protections, creating a natural security degradation compared to corporate identity providers.

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Misinformation

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AI tools are constantly learning from users, the internet, and from training data. While this increases their ability to provide value, naturally, there is a risk that AI models will train on false information and then produce inaccurate information and recommendations for users. When productivity-seeking employees do not fact check the information generated, there’s a risk they will utilize that bad information in corporate communications, from marketing to customer interactions. This can create mistrust in the brand, or worse, produce suboptimal outcomes for customers and business partners.

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Supply Chain Attacks

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Introducing AI tools that integrate with sanctioned business tools create new attack vectors that bad actors can exploit to gain access to sensitive data or infiltrate connected applications. Once compromised, attackers can move laterally across the organization’s supply chain, impacting multiple vendors and partners. This was seen in the Snowflake attack, where an insecure Snowflake environment was used to break into customers like Ticketmaster, Santander Group, and AT&T.

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How to Reduce Shadow AI Security Risks

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While the risk of shadow AI usage can’t be completely eliminated, it can be greatly reduced by implementing the right tools, processes, and training.

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Employee Education and Training

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Rather than implementing outright bans, which often drive usage underground, organizations should foster a culture of responsible AI adoption. This includes:

• Activating targeted awareness programs that help employees understand the risks of unauthorized AI usage and how to use AI safely.
• Creating clear channels for requesting new AI tool approval
• Establishing feedback mechanisms for tool suggestions
• Developing collaborative solutions between security and business units
• Maintaining open dialogue about AI usage and needs

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Third-Party App Discovery and Inventory

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Since shadow AI flies under the radar of traditional network monitoring, organizations should adopt solutions that can discover and inventory all applications connected to corporate infrastructure. A good tool will be able to map interactions between approved applications, shadow applications, and shadow AI applications and provide insight into who is using them and how. From there, Security teams can choose to unsanction risky apps, monitor their usage, or implement security controls while deploying targeted training.

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SSPM Implementation

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SaaS Security Posture Management (SSPM) has emerged as a critical component for maintaining visibility into shadow AI usage. SSPM provides:

• Continuous posture management across the SaaS environment
• Unified risk assessment and compliance monitoring
• Clear visibility into SaaS environment connections
• Real-time monitoring of user activities and data access patterns

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Threat Detection

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SSPM is great for managing configuration drift and reducing risk across SaaS and AI deployments, but it’s not enough to fully protect organizations’ SaaS ecosystems. Since threat actors often enter through the front door of SaaS applications via compromised credentials or excessively permissive tokens, businesses need more than SSPM to identify malicious intent. Look for a tool that provides event monitoring across SaaS applications so you can respond to anomalous activity like unusual downloads, impossible travel, or failed login attempts.

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→ Read Next: Configuration Management Isn’t Enough: The Crucial Role of Event Monitoring in SaaS Security (Blog)

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How Reco Discovers and Secures Shadow AI

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Reco’s SaaS security platform can help you discover shadow AI applications, manage and mitigate risks, and reduce exposure. Our AI-based graph technology connects to organizations’ Active Directory to identify known applications. From there, it analyzes email metadata to identify potential shadow AI applications. Then it subtracts known applications from the list to produce a list of shadow applications and shadow AI applications. 

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→ Read Next: Uncovering Shadow Apps and Shadow AI with Reco (Blog)

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To learn more about how to protect your business from shadow AI download The CISOs Guide to Shadow AI. Or reach out to Reco to schedule a demo to learn how Reco can help you manage and mitigate shadow AI risk.