Artificial intelligence has quickly moved from being an emerging technology to becoming part of everyday business.Employees use AI tools to write emails, summarise documents, analyse information, generate code, prepare presentations and solve technical problems. While this can improve productivity, it also creates a privacy question that many organisations are only beginning to address:What happens when employees put personal data into AI tools that the organisation has not approved? This is the growing challenge of Shadow AI.Shadow AI refers to the use of AI applications by employees without formal approval, visibility or governance from an organisation’s IT, security, legal or privacy teams. For businesses in India, this issue deserves particular attention as organisations work towards understanding and implementing the requirements of the DPDP Act and its associated rules.

The concern is not that employees are using AI.

The concern is that personal data can move into AI systems without the organisation fully knowing where it is going, how it is being processed or what controls apply to it.

What Is Shadow AI?

Shadow AI is essentially the AI version of shadow IT.

In the past, employees might have installed an unapproved software application because it helped them complete their work faster. Today, they can simply open an AI platform and start using it within minutes.

A marketing employee may use an AI tool to improve customer communications.

A developer may upload an error log to get help debugging an application.

An HR professional may ask an AI assistant to summarise employee-related documents.

A customer-support employee may paste a complaint into an AI chatbot and ask it to create a response.

None of these actions necessarily involve malicious intent.

In fact, employees may believe they are helping the organisation become more productive.

The problem begins when those activities involve personal data.

Why Shadow AI Matters Under the DPDP Act

The DPDP Act establishes a framework for processing digital personal data in India and places responsibilities on organisations that determine the purpose and means of processing personal data.

India’s Digital Personal Data Protection Rules, 2025 were notified in November 2025, adding operational requirements and providing a phased commencement framework for different provisions.

This makes data visibility increasingly important.

An organisation may know where personal data exists in its CRM, HR system, databases and cloud applications.

But does it know whether employees are copying that information into AI platforms?

That is the gap Shadow AI can create.

The question is no longer simply:

“Where is our personal data stored?”

It is also:

“Where is our personal data being sent?”

A Simple Example of the Problem

Consider a customer-service employee handling a complaint.

The complaint contains:

• Customer name
• Email address
• Phone number
• Order information
• Complaint details

The employee wants to respond quickly.

They copy the entire complaint into an AI assistant and ask it to:

“Write a professional response to this customer.”

From the employee’s perspective, this is simply a productivity shortcut.

From a data protection perspective, several questions arise.

Was the AI tool approved?

Was the personal data necessary for the task?

Was the information shared with an external service?

How is that information handled?

How long might it be retained?

What controls does the organisation have over the processing?

These questions demonstrate why the DPDP Act conversation cannot be separated completely from modern AI usage.

Personal Data Can Appear in Unexpected Places

One of the biggest challenges organisations face is that personal data does not always exist in clearly labelled databases.

It can appear in:

• Emails
• Spreadsheets
• Customer-support tickets
• Screenshots
• Documents
• Application logs
• Meeting transcripts
• CRM exports
• Development environments
• Analytics reports
• AI prompts
• Uploaded files

This creates a significant data-discovery challenge.

An organisation might have a strong privacy policy but still have limited visibility into how employees actually handle information.

This is where Shadow AI becomes particularly important.

The Risk of “Just One Prompt”

AI tools are designed to work with context.

The more information a user provides, the more useful the output can appear.

That creates a natural temptation:

“I’ll just paste the whole document.”

But the entire document may contain much more information than the AI actually needs.

For example, if an employee wants an AI system to improve the wording of a customer email, the customer’s phone number, address or account number may be completely unnecessary.

A privacy-conscious approach is therefore to minimise the information provided.

Instead of sharing the entire record, provide only the information required to complete the task.

Shadow AI and Data Leakage

Shadow AI can also create a new pathway for data leakage.

Traditional security teams often focus on:

• Email
• USB devices
• Cloud storage
• File sharing
• Unauthorised applications
• External websites

AI applications add another category.

Employees may upload documents, paste text or submit information through AI interfaces.

If those activities are not visible to security teams, organisations may have difficulty identifying where information is leaving their controlled environment.

This makes Shadow AI both a privacy risk and a cybersecurity visibility problem.

Why a Blanket AI Ban May Not Work

A natural response might be to prohibit employees from using AI altogether.

But that approach can create another problem.

Employees already use AI because it saves time.

If an organisation provides no approved alternative, some employees may simply continue using AI tools privately.

The organisation then loses visibility.

A better strategy is to establish controlled AI adoption.

Employees should know:

• Which AI tools they can use
• What information they can share
• What information they cannot share
• Which use cases are approved
• When additional approval is required
• How to report an accidental data disclosure

The objective should not be to stop innovation.

It should be to make safe AI usage easier than unsafe AI usage.

How Organisations Can Manage Shadow AI

1. Discover What Employees Are Already Using

Before creating a new policy, understand the current situation.

Ask business teams which AI tools they use and why.

The results may reveal AI applications that security or IT teams were unaware of.

2. Create an Approved AI Framework

Organisations can establish a list of approved AI tools and acceptable use cases.

Not every AI application needs to receive the same level of scrutiny.

A tool used for generating generic ideas may present a different risk from one used to process customer information.

3. Classify the Data

Employees should understand the difference between:

Public information

Information that can be safely shared publicly.

Internal information

Business information intended for authorised organisational use.

Confidential information

Information that requires additional protection.

Personal data

Information relating to identifiable individuals that may fall within the scope of the DPDP 

This classification helps employees make better decisions before using AI.

4. Minimise Personal Data

If an AI tool does not need personal data to perform a task, don’t provide it.

For example, instead of uploading a complete customer record, remove unnecessary identifiers before using an AI system.

The less personal data being shared, the lower the potential exposure.

5. Evaluate AI Vendors

Organisations should understand how AI providers handle information before allowing business data to be processed through their services.

Depending on the use case, businesses may need to consider:

• Data handling practices
• Security controls
• Retention
• Access controls
• Data processing arrangements
• Contractual terms
• Data location
• Deletion processes
• Use of submitted information

Vendor assessment should become part of responsible AI adoption.

6. Combine Policy With Technology

A policy alone cannot prevent every accidental disclosure.

Organisations can consider security controls such as:

• Data Loss Prevention
• Identity and Access Management
• Endpoint security
• Web filtering
• Application controls
• Data classification
• Security monitoring
• Logging
• Access restrictions

The right combination will depend on the organisation’s technology environment and risk profile.

The Role of Employees in DPDP Compliance

Technology cannot solve everything.

Employees remain an important part of data protection.

A person who understands why personal data should not be copied into an unapproved AI application is less likely to make that mistake.

This means DPDP  awareness should extend beyond legal and compliance teams.

Employees in customer service, HR, finance, sales, marketing, IT and development may all interact with personal data.

They should understand how everyday activities can affect data protection.

Training should therefore focus on realistic scenarios rather than only explaining legal terminology.

For example:

“Can I paste a customer complaint into an AI chatbot?”

is a much more useful training question than simply telling employees to “protect personal data.”

Privacy and Cybersecurity Need to Work Together

The rise of Shadow AI also highlights the growing connection between privacy and cybersecurity.

Privacy teams may focus on how personal data is collected and processed.

Security teams focus on protecting systems and preventing unauthorised access or data loss.

AI brings these responsibilities closer together.

A security team may detect unusual uploads to an AI service.

A privacy team may need to determine whether personal data was involved.

Together, these teams can provide a more complete response.

The DPDP should therefore not be treated as an isolated legal or compliance project.

It should become part of a broader data-security and governance strategy.

A Practical Shadow AI Checklist

Organisations looking to address Shadow AI can start with ten questions:

1. What AI tools are employees using today?

2. Which of those tools are officially approved?

3. What types of data are employees entering into them?

4. Could personal data be included in prompts or uploads?

5. Are employees aware of the organisation’s AI policy?

6. Are approved AI alternatives available?

7. Have relevant AI vendors been assessed?

8. Can the organisation detect unusual data movement?

9. Are privacy and security teams working together?

10. Is AI usage reviewed regularly as new tools emerge?

These questions can help organisations identify gaps before they become larger privacy or security problems.

The Future of the DPDP Act and AI Governance

AI adoption is unlikely to slow down.

New tools are appearing rapidly, and employees will continue finding creative ways to use them.

That means organisations cannot rely on a one-time AI policy.

AI governance will need to evolve alongside the technology.

The DPDP Act provides an important foundation for responsible handling of digital personal data in India, but effective data protection ultimately depends on what organisations do in practice.

That includes understanding where data is stored, who can access it, how it is shared and increasingly, which AI systems are processing it.

Conclusion

Shadow AI is not simply an IT problem.

It is a business problem that sits at the intersection of AI adoption, data privacy, cybersecurity, employee behaviour and governance.

The biggest risk may not be an employee deliberately trying to expose personal information.

It may be someone trying to save five minutes.

An employee uploads a document.

A developer pastes a log.

A support agent copies a customer complaint.

A recruiter asks an AI tool to summarise candidate information.

Each action may seem harmless on its own.

But collectively, they can create a significant data-governance challenge.

As businesses continue adapting to the DPDP Act, they should look beyond policies and compliance documents and examine how personal data is actually being used every day.

Because the future of privacy will not only depend on protecting databases.

It will also depend on controlling what happens when people start talking to AI.

The goal is not to choose between AI innovation and data privacy. The goal is to build an environment where organisations can have both.