5 Shadow IT Tools To Detect and Manage App Sprawl

If you manage IT, you've probably watched employees expense unauthorized SaaS apps to corporate cards while your approved tools sit unused. Here's what I've learned actually works to manage shadow IT tools and new AI threats.

Why are shadow IT tools still alive?

Shadow IT exists because your official tools lack the features that employees need, and your approval process takes too long, so they find their own solutions. Free or freemium SaaS, credit-card sign-ups, and cloud services make it ridiculously easy to do that.

Think about it from an employee's perspective. Your company’s approved project management tool lacks the features you need. The request process for new software takes three weeks and usually ends with "denied." 

Meanwhile, you can sign up for Notion, Airtable, or dozens of other tools in under five minutes and put them on a credit card. And now, with accessible AI and low-code automation tools, employees can even build their own solutions.

The same logic applies when teams can't find tools in your approved directory. If there’s no centralized visibility into what's already available, they’ll create new accounts in unvetted platforms.

Shadow IT vs. shadow AI

Shadow AI is shadow IT's more dangerous cousin. It happens when employees use or build AI models, chatbots, or automation scripts without IT oversight.

For example, someone pastes customer data into ChatGPT or builds an internal AI agent with no security controls.

It’s riskier than shadow IT. AI tools often process sensitive data and can make unpredictable decisions.

Here's how they compare:

Shadow IT in practice

Traditional shadow IT shows up as unsanctioned apps and tools that fill gaps left by approved systems.

Common examples:

• File-sharing apps outside your corporate stack
• Duplicate CRMs or spreadsheets for customer data management
• Unapproved Slack or productivity plug-ins

Key risks:

• Data exposure: Sensitive files shared or stored in unsecured platforms.
• Integration vulnerabilities: Unvetted tools connecting to core systems.
• Cost duplication: Multiple subscriptions for overlapping functionality.

Hidden costs:

• Unmanaged SaaS spend adding up across dozens of small subscriptions.
• Fragmented workflows where teams work in different tools.
• Time wasted recreating work that already exists elsewhere in the organization.

Shadow AI in practice

Shadow AI usually starts as a means of being more productive. But it spirals quickly.

Common shadow AI scenarios:

• Copy-pasting internal data into public AI assistants like ChatGPT
• Using personal Copilot or Claude accounts to write code or run analysis
• Connecting an LLM to a spreadsheet or webhook to automate replies

The risks are amplified:

• Compliance and privacy gaps: Regulated data (PII, health info, payment details) processed in tools without DPAs or regional data controls.
• Over-permissioned access: Bots or service accounts reading more data than necessary.
• Output integrity and bias issues: Inaccurate or biased AI outputs slipping into customer comms, code, or reports.
• Data exposure: Sensitive information pasted into public models with unclear retention policies.

Hidden costs:

• Fixing incorrect AI outputs, rewriting bad content, or repairing code takes time and money.
• Multiple AI subscriptions across teams with overlapping use cases.

How enterprises discover and manage shadow IT

You can't manage what you can't see. The first step is always finding out what’s actually running in your environment.

From there, you can move on to management, which is less about blocking everything and more about balancing risk with your team's need to stay productive.

Step 1: Discover what's out there

You need multiple approaches because any single method leaves blind spots.

You can use:

• Network and log analysis: Analyze firewall, proxy, and network traffic logs for unusual patterns. Look for traffic to unknown domains, large data transfers to cloud services, and unfamiliar application protocols.
• SaaS Management Platforms (SMPs): Tools like Torii or Zluri integrate with your finance systems, identity providers like Okta, and browsers to create a real-time inventory of every SaaS and AI tool in use.
• Cloud Access Security Brokers (CASBs): CASBs such as Microsoft Defender for Cloud Apps act as security checkpoints between users and cloud apps. They monitor usage in real-time, assess risk levels, and enforce policies such as blocking high-risk services or encrypting data sent to unapproved tools.

Step 2: Manage and mitigate the risk

Once you know what's out there, the goal isn't to block everything. That approach frustrates employees and pushes them toward more creative workarounds.

Instead, take a more strategic approach:

• Assess and categorize: Evaluate each discovered tool based on its risk level and then categorize apps into three buckets of approved, restricted, and blocked.
• Create clear and flexible policies: Write policies that explain the "why" behind restrictions. More importantly, create a fast approval process for new tool requests.
• Educate and collaborate: Regular training on shadow IT and AI risks works, but skip the fear-mongering. Focus on practical examples relevant to people's actual work. Foster a culture where people feel comfortable asking for tool reviews.

Why discovery alone isn’t enough

Discovery alone doesn’t reduce shadow IT risks because identifying the problem doesn’t automatically fix it.

Risk only decreases when you act on the information by approving secure tools, shutting down dangerous ones, or replacing problematic apps with better alternatives.

Plus, discovery tells you what people are using, but not why. Is your official project management tool terrible? Is your approval process a bureaucratic nightmare? You still need to address the root cause.

Leading tools to watch in 2025

The shadow IT management space is evolving quickly, and some of the platforms now include shadow AI management.

Here are some of the random tools worth considering:

• Superblocks provides a development platform where you can standardize and scale AI internal tool development while IT maintains oversight of security, governance, and compliance.
• Torii integrates with financial and identity systems to create a map of all cloud apps. It helps orgs optimize software spending and identify redundant or unused tools.
• Zluri combines discovery with automated access management. It handles user provisioning and de-provisioning while enforcing access policies across your software stack.
• BetterCloud manages SaaS apps by automating workflows and security controls. It focuses on user lifecycle management and data protection across supported cloud apps.
• Spin.AI offers risk scoring for both applications and users, with automated responses to protect against ransomware and insider threats.

What’s next for shadow IT and AI governance?

What’s next for shadow IT and AI governance is a move from outright blocking shadow apps to implementing smart, automated guardrails. Think of AI-aware Data Loss Prevention (DLP) that can detect and prevent sensitive data from being pasted into a public chatbot, or systems that log AI prompts and outputs for auditing purposes.

Smart organizations are also using controlled environments where teams can experiment with new AI platforms and validate use cases without exposing company data. These labs provide the innovation space people want while maintaining the control IT needs.

Frequently asked questions

Which tools can help enterprises detect Shadow IT?

SaaS Management Platforms like Torii or Zluri, along with CASBs like Microsoft Defender for Cloud Apps or  Netskope, help enterprises detect shadow IT.

Why does automation matter for governance?

Automation matters for governance because manual processes can't keep pace with how quickly employees adopt new tools and AI services.

Can discovery and governance be handled in one platform?

Yes, discovery and governance can be handled in one platform, but most organizations need multiple tools for complete coverage. Platforms like Zluri combine app discovery with access management, while app platforms like Superblocks offer centralized governance controls and oversight for internal tool development.

How often should enterprises audit Shadow IT?

Enterprises should audit shadow IT continuously rather than quarterly or annually because new tools and AI services launch constantly.

What trends will define IT and AI governance in 2025?

The biggest trends defining IT and AI governance in 2025 will be AI-aware security controls and sandboxed innovation environments.