The rise of generative AI tools like ChatGPT is transforming how employees work — but it’s also creating a dangerous cybersecurity blind spot. As more teams adopt AI tools without IT approval, sensitive data may be exposed, and most leaders are still underestimating the risks.

This trend, often called shadow IT, isn’t new. But now, its AI-powered cousin — shadow AI — is raising fresh concerns for cybersecurity experts.

“AI typically gets broader access to data than traditional tools, which makes the risk of exposure much higher in a breach,” explains Melissa Ruzzi, Director of AI at SaaS security firm AppOmni.

Why AI Makes Shadow IT More Dangerous

Unlike legacy shadow IT, unsanctioned AI tools can do more with the data they access — and the consequences can be severe. If AI tools use company data for model training or internal research, that data might unknowingly become public. Worse, hackers could exploit vulnerabilities in AI models to extract private information.

Shadow AI spans far beyond just chatbots. Employees are using it in transcription tools, coding assistants, customer service bots, data dashboards, and even AI features hidden in everyday CRM platforms — often without anyone reviewing the security behind them.

Ruzzi warns that many of these tools fail to meet compliance standards and store data in poorly secured environments.

GenAI vs. Embedded AI: Which Is Riskier?

Unapproved generative AI tools, like those used to create content or analyze code, are currently the biggest concern. They typically operate with little to no oversight, making them prime targets for misuse or accidental data leaks.

But embedded AI tools — features built into trusted SaaS platforms — also pose a hidden risk. Since the application itself is approved, the AI features within may go unnoticed, slipping past traditional monitoring tools.

“These AI capabilities can only be uncovered by deep SaaS security tools that analyze configurations and usage patterns,” says Ruzzi.

Traditional Tools Aren’t Enough

Most cloud security tools, like CASBs (Cloud Access Security Brokers), can identify what apps employees use and when ChatGPT is being accessed. But they fall short when it comes to identifying AI features embedded within apps or detecting newer tools as they emerge.

Ruzzi recommends adopting AI-powered security platforms that can adapt in real time, rather than relying on outdated rule-based systems.

Shadow AI and Compliance Pitfalls

Shadow AI doesn’t just pose technical risks — it can trigger serious compliance violations. Laws like the EU’s GDPR, California’s CCPA/CPRA, and the U.S. HIPAA regulations demand strict controls over how personal and sensitive data is handled.

Shadow AI can easily violate these rules by:

Collecting excessive data (violating data minimization)
Using data in unintended ways (violating purpose limitation)
Failing to secure data (violating data protection requirements)

“If AI tools use personal data without disclosure or consent, it’s a breach of privacy law,” says Ruzzi. “For healthcare providers, the unauthorized handling of protected health information (PHI) could even result in HIPAA lawsuits.”

With other global frameworks like Brazil’s LGPD and Canada’s PIPEDA in play, organizations must ensure compliance wherever their users are.

What Companies Should Do Now

To avoid these legal and security pitfalls, Ruzzi urges companies to take proactive steps:

Identify and test AI tools for vulnerabilities
Define a clear list of approved technologies
Educate employees on the risks of shadow AI
Monitor AI use across platforms with advanced SaaS security tools

As AI becomes more embedded in everyday business tools, the risks of shadow AI will only grow. Organizations that invest now in proper monitoring, employee training, and governance will be best equipped to manage this evolving threat.

“Shadow AI isn’t going away — it’s only getting more complex. The smartest move is to stay ahead with strong monitoring and clear AI usage policies,” Ruzzi concludes.

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