Shadow AI: A Fatal Flaw for Most Organizations

Learn more about ManageEngine

"Shadow AI represents both the greatest governance risk and the biggest strategic opportunity in the enterprise," said Ramprakash Ramamoorthy, Director of AI Research at ManageEngine. "Organizations that will thrive are those that address the security threats and reframe shadow AI as a strategic indicator of genuine business needs. IT leaders must shift from playing defense to proactively building transparent, collaborative, and secure AI ecosystems that employees feel empowered to use."

The Shadow AI Surge in Enterprises: Insights from the US and Canadian Workplace, a report from ManageEngine based on a survey of IT decision makers (ITDMs) and business employees, investigates the rise of shadow AI — unauthorized AI tools used for work — and identifies critical gaps that organizations need to close if they want to reduce the risks of shadow AI and turn it into a strategic advantage.

The rise: 60% of employees are using unapproved AI tools more than they were a year ago, and 93% of employees admit to inputting information into AI tools without approval.

The risks: 63% of ITDMs see data leakage or exposure as the primary risk of shadow AI. Conversely, 91% of employees think shadow AI poses no risk, not much risk, or some risk that's outweighed by reward.

The rewards: Summarizing notes or calls (55%), brainstorming (55%), and analyzing data or reports (47%) are the top tasks employees complete with shadow AI. Generative AI text tools (73%), AI writing tools (60%), and code assistants (59%) are the top AI tools ITDMs have approved for employee use.

Identifying the Shadow AI Gaps

To turn the use of shadow AI from a liability into a strategic advantage, IT leaders need to close the gaps in education, visibility, and governance revealed by the report. Specifically, a lack of education around AI model training, safe user behavior, and organizational impact is driving systematic misuse. Blind spots continue to grow in organizations, even as IT teams move to approve and integrate AI tools as quickly as possible. Meanwhile, shadow AI proliferates due to inadequate enforcement of established governance policies.

  • 85% of ITDMs report that employees are adopting AI tools faster than their IT teams can assess them.
  • 32% of employees entered confidential client data into AI tools without confirming company approval, while 37% entered private, internal company data.
  • 53% of ITDMs say employees' use of personal devices for work-related AI tasks is creating a blind spot in their organization's security posture.
  • Only 54% of ITDMs report their organizations have implemented clear, enforced AI governance policies and actively monitor for unauthorized use, while 91% have implemented policies overall.

Pivoting to Proactive AI Management

Proactively managing AI means harnessing employee initiative while maintaining security. It delivers the business value discovered in shadow AI but does so via AI tools that are approved by IT. To that end, ITDMs and employees make several strategic recommendations in the report.

  • 63% of ITDMs advise integrating approved AI tools into standard workflows and business applications, 60% suggest implementing clear policies on acceptable AI use, and 55% suggest establishing a list of vetted and approved tools.
  • 66% of employees recommend setting clear policies that are fair and practical, 63% recommend providing official tools that are relevant to their tasks, and 60% advise providing better education on understanding the risks.

"Shadow AI is a fatal flaw for most organizations," said Sathish Sagayaraj Joseph, regional technical head at ManageEngine. "IT teams can't manage risk they can't see — and they can't enable business value that users won't divulge. Proactive AI management unites IT and business professionals in their pursuit of common, organizational goals. That means employees are equipped to understand and avoid AI-related risks, and IT is empowered to help them use AI in ways that drive real business outcomes."

Survey Methodology: In May 2025, ManageEngine commissioned independent market research agency Censuswide to conduct a study of 350 ITDMs and 350 working professionals across the US and Canada, employed in organizations with at least 500 employees and $10M in annual revenue. The survey explored AI usage patterns, security concerns, and governance gaps, with a focus on real-world behaviors across organizations of varying sizes and industries.

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Shadow AI: A Fatal Flaw for Most Organizations

Learn more about ManageEngine

"Shadow AI represents both the greatest governance risk and the biggest strategic opportunity in the enterprise," said Ramprakash Ramamoorthy, Director of AI Research at ManageEngine. "Organizations that will thrive are those that address the security threats and reframe shadow AI as a strategic indicator of genuine business needs. IT leaders must shift from playing defense to proactively building transparent, collaborative, and secure AI ecosystems that employees feel empowered to use."

The Shadow AI Surge in Enterprises: Insights from the US and Canadian Workplace, a report from ManageEngine based on a survey of IT decision makers (ITDMs) and business employees, investigates the rise of shadow AI — unauthorized AI tools used for work — and identifies critical gaps that organizations need to close if they want to reduce the risks of shadow AI and turn it into a strategic advantage.

The rise: 60% of employees are using unapproved AI tools more than they were a year ago, and 93% of employees admit to inputting information into AI tools without approval.

The risks: 63% of ITDMs see data leakage or exposure as the primary risk of shadow AI. Conversely, 91% of employees think shadow AI poses no risk, not much risk, or some risk that's outweighed by reward.

The rewards: Summarizing notes or calls (55%), brainstorming (55%), and analyzing data or reports (47%) are the top tasks employees complete with shadow AI. Generative AI text tools (73%), AI writing tools (60%), and code assistants (59%) are the top AI tools ITDMs have approved for employee use.

Identifying the Shadow AI Gaps

To turn the use of shadow AI from a liability into a strategic advantage, IT leaders need to close the gaps in education, visibility, and governance revealed by the report. Specifically, a lack of education around AI model training, safe user behavior, and organizational impact is driving systematic misuse. Blind spots continue to grow in organizations, even as IT teams move to approve and integrate AI tools as quickly as possible. Meanwhile, shadow AI proliferates due to inadequate enforcement of established governance policies.

  • 85% of ITDMs report that employees are adopting AI tools faster than their IT teams can assess them.
  • 32% of employees entered confidential client data into AI tools without confirming company approval, while 37% entered private, internal company data.
  • 53% of ITDMs say employees' use of personal devices for work-related AI tasks is creating a blind spot in their organization's security posture.
  • Only 54% of ITDMs report their organizations have implemented clear, enforced AI governance policies and actively monitor for unauthorized use, while 91% have implemented policies overall.

Pivoting to Proactive AI Management

Proactively managing AI means harnessing employee initiative while maintaining security. It delivers the business value discovered in shadow AI but does so via AI tools that are approved by IT. To that end, ITDMs and employees make several strategic recommendations in the report.

  • 63% of ITDMs advise integrating approved AI tools into standard workflows and business applications, 60% suggest implementing clear policies on acceptable AI use, and 55% suggest establishing a list of vetted and approved tools.
  • 66% of employees recommend setting clear policies that are fair and practical, 63% recommend providing official tools that are relevant to their tasks, and 60% advise providing better education on understanding the risks.

"Shadow AI is a fatal flaw for most organizations," said Sathish Sagayaraj Joseph, regional technical head at ManageEngine. "IT teams can't manage risk they can't see — and they can't enable business value that users won't divulge. Proactive AI management unites IT and business professionals in their pursuit of common, organizational goals. That means employees are equipped to understand and avoid AI-related risks, and IT is empowered to help them use AI in ways that drive real business outcomes."

Survey Methodology: In May 2025, ManageEngine commissioned independent market research agency Censuswide to conduct a study of 350 ITDMs and 350 working professionals across the US and Canada, employed in organizations with at least 500 employees and $10M in annual revenue. The survey explored AI usage patterns, security concerns, and governance gaps, with a focus on real-world behaviors across organizations of varying sizes and industries.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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