Shadow AI in India: Cybersecurity Risks and How CISOs Can Manage It

Shadow AI in India is becoming an emerging cybersecurity and data-governance challenge for organizations adopting artificial intelligence.

Employees are increasingly using public AI chatbots, coding assistants, browser extensions, AI-powered applications and other tools to complete everyday tasks faster. However, these tools may be used without formal approval, security assessment or governance.

An employee could paste source code, customer data, financial information or confidential contracts into an external AI service within seconds.

The security team may never know it happened.

This creates a growing Shadow AI cybersecurity blind spot for organizations across India.

The challenge is not that businesses are using AI. The challenge is knowing which AI tools are being used, what data is being shared, who can access that data and what those tools can do.

What Is Shadow AI?

Shadow AI refers to the use of AI-powered applications, services or systems without appropriate organizational approval, oversight or security controls.

It can include:

• Public AI chatbots
• AI coding assistants
• Browser extensions
• Personal AI accounts
• Custom AI models
• AI automation platforms
• Autonomous AI agents
• Direct API connections

Approved enterprise AI tools usually have a defined business purpose, an owner, access controls, vendor agreements and rules governing what information can be processed.

Shadow AI takes a different route.

An employee can create a personal account, install an AI browser extension, subscribe to an AI service or simply use a public website for work. The tool may be in use long before security, legal, procurement or compliance teams know about it.

For organizations exploring responsible AI adoption, an effective AI governance framework can provide a structured approach to identifying AI systems, assessing risks and defining governance responsibilities.

The Impact of Shadow AI on Indian Organizations

A Hidden Layer Inside Everyday Business

AI is now being used for tasks such as:

• Summarizing reports
• Reviewing contracts
• Analysing spreadsheets
• Drafting customer communications
• Troubleshooting code
• Creating marketing content
• Automating routine business processes

These activities can create Shadow AI risks when sensitive information is submitted to tools that have not been assessed or approved.

For Indian IT services companies, business-process organizations, global capability centres, fintechs, manufacturers and other data-intensive businesses, the exposure can be significant.

A single AI interaction could potentially involve customer information, intellectual property, confidential business information or regulated personal data.

Why Traditional Security Controls Can Miss Shadow AI

Traditional cloud and IT security controls are generally designed around known systems.

Procurement records the vendor. Identity systems record users. Configuration tools identify workloads. Security teams can determine which applications exist and who has access to them.

However, AI usage can be much less visible. For example, an employee may use an AI service entirely through a browser. Similarly, a developer may connect an AI coding assistant to a private repository.

Traditional inventories may identify the application but fail to capture what is happening inside it:

• AI prompts
• File uploads
• Source-code snippets
• Screenshots
• Browser extensions
• Plugins
• API calls
• Connections to other systems

This creates a Shadow AI blind spot across company-managed devices, SaaS applications, personal accounts and third-party AI services.

Shadow AI Risks for Indian Businesses

Public Chatbots and AI Coding Assistants

Employees may send internal reports, financial records, customer communications, source code or other sensitive information to public AI tools because they make work faster.

Developers face another challenge.

AI coding assistants can connect to private repositories, while AI-generated code may enter production without going through normal security reviews.

Potential risks include:

• Data leakage
• Exposed credentials and secrets
• Vulnerable dependencies
• Insecure packages
• Flawed AI-generated code
• Missed security checks

Organizations therefore need to ensure that AI adoption does not bypass established security practices such as code review, secrets scanning, software composition analysis and secure development processes.

AI Agents and Extensions Widen the Attack Surface

AI agents introduce another layer of risk because they can be given access to systems rather than simply receiving information from users.

An AI browser extension, for example, could potentially access websites, emails, documents, calendars or enterprise applications.

If permissions are broader than necessary, a productivity tool can become a pathway to sensitive information.

Prompt injection creates another concern.

Instructions hidden inside a document, website, email or other content could manipulate an AI system into following unintended instructions.

As AI systems become more autonomous, organizations need to consider permissions, access controls and monitoring alongside prompts and models.

India’s Regulatory Environment Makes AI Visibility More Important

DPDP and AI Data Processing

India’s Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025 make responsible personal-data handling increasingly important for organizations using AI.

The notified Rules establish the operational framework for implementing the DPDP Act, including requirements relating to personal-data protection and organizational responsibilities.

For an organization sending personal data to an AI provider, the question is therefore not simply whether the provider stores the information.

Organizations should consider:

• What personal data is being processed?
• Why is it being processed?
• Who can access it?
• How long is it retained?
• Are third parties involved?
• Where is processing taking place?
• How can the information be deleted?
• What security safeguards are in place?

The DPDP framework should not be treated as a complete Shadow AI rulebook. Organizations must also consider contractual confidentiality, intellectual property, sector-specific requirements and internal security policies.

ISOQAR India’s DPDP Act 2023 Compliance services can help organizations assess their data protection practices and identify compliance gaps.

CERT-In and Cybersecurity Visibility

CERT-In’s cybersecurity directions require covered entities to maintain ICT system logs securely for a rolling period of 180 days within Indian jurisdiction and establish processes for reporting specified cyber incidents.

This makes visibility, monitoring and incident response important considerations when organizations introduce new AI tools into their environments.

Organizations should also consider current CERT-In guidance covering areas such as AI-assisted vulnerability exploitation, software bills of materials and other emerging cybersecurity risks.

For Indian IT services and outsourcing organizations, an AI-related incident may involve customer-owned information, contractual commitments, privacy obligations and reputational risk.

Why Traditional CISO Programs Can Miss Shadow AI

Asset Inventories Don’t Capture Everything

CMDBs, endpoint-management platforms, SaaS discovery tools and cloud-security products can provide strong visibility into approved technology.

However, they may not identify:

• Personal AI accounts
• Browser-based AI usage
• Unauthorized AI extensions
• Department-level subscriptions
• Private AI APIs
• AI features hidden inside approved applications

A useful AI inventory therefore needs to go beyond product names.

Organizations should consider documenting:

• AI tool or model
• Business owner
• Users
• Permissions
• Intended use
• Data types
• Vendor terms
• Processing location
• Retention period
• Risk rating
• Review date

Without this information, organizations may know that AI is being used without knowing where the actual exposure exists.

Traditional DLP Can Miss AI Interactions

Many Data Loss Prevention systems are designed around files, email attachments and recognizable data patterns.

AI interactions can look very different.

Sensitive information may appear inside:

• Natural-language prompts
• Source-code snippets
• Screenshots
• Images
• Audio
• Spreadsheets
• Semi-structured documents

Organizations therefore need visibility across browsers, endpoints, identity systems, outbound traffic, approved AI gateways and data-classification controls.

At the same time, monitoring should remain proportionate and respect employee privacy and applicable requirements.

Why Blanket AI Bans May Not Solve Shadow AI

Banning AI completely may appear to be the simplest solution.

However, if employees still need AI to complete their work, they may move to personal accounts or consumer services that are even harder for security teams to monitor.

A better approach is to define clear boundaries.

Employees should understand:

• Which AI tools are approved
• What data cannot be submitted
• When human review is required
• What vendors must provide
• How long information may be retained
• How an AI-related incident should be reported

The objective should be to make secure AI adoption easier than risky AI adoption.

How Indian Organizations Can Manage Shadow AI

1. Discover Existing AI Usage

The first step is visibility.

Organizations can review:

• Proxy logs
• Browser extensions
• Software inventories
• Expense records
• API keys
• Existing AI projects
• Business-team workflows

The objective is not to stop AI adoption.

It is to understand how AI is already being used.

2. Create Approved AI Alternatives

Organizations should provide employees with approved AI tools. In addition, security teams should assess AI vendors before adoption. Regular monitoring can then help identify unusual or unauthorized AI usage.

This creates a safer alternative to unauthorized consumer AI services.

3. Classify Data Before It Reaches AI Systems

Data classification is one of the most important controls for managing Shadow AI.

Organizations should distinguish between:

• Public information
• Internal information
• Confidential information
• Regulated data
• Customer-owned information
• Restricted intellectual property

They can then define which information approved AI systems may process and which information requires redaction, anonymization or additional approval.

4. Assess AI Agents and Third Parties

Before connecting an AI agent to email, code repositories, enterprise search, document stores or production systems, security teams should ask three key questions:

What can the AI access?

Which systems or data can the AI change?

Can it take any actions without human approval?

In addition, assess AI suppliers and third parties according to the organization’s risk requirements.

This makes third-party risk management an important part of enterprise AI governance.

5. Test AI Security Controls

Security teams should consider red-team exercises and tabletop scenarios involving:

• Prompt injection
• Sensitive-data extraction
• Malicious extensions
• Poisoned data
• Insecure AI-generated code
• Excessive permissions
• Compromised integrations

The organization should then measure whether its controls actually work.

How ISO/IEC 42001, ISO/IEC 27001 and ISO/IEC 27701 Can Support Shadow AI Management?

Shadow AI sits at the intersection of AI governance, information security and privacy.

That means organizations may need more than a standalone AI policy.

ISO/IEC 42001 – AI Management System

ISO/IEC 42001 provides a structured management-system approach for AI governance, including AI risk management, roles, responsibilities and continual improvement.

For Shadow AI, this can support a structured approach to identifying AI use cases, assessing AI risks and establishing governance controls.

ISO/IEC 27001 – Information Security Management

ISO/IEC 27001 provides a risk-based framework for managing information-security risks.

For Shadow AI, information security controls can support areas such as access management, information classification, supplier security, incident management and risk assessment.

ISO/IEC 27701 – Privacy Information Management

ISO/IEC 27701 provides a privacy information management framework that builds on information-security management and helps organizations manage privacy-related risks.

This becomes particularly relevant when Shadow AI involves personal information, customer data or other privacy-sensitive information.

Digital Trust & Security

ISOQAR India’s Digital Trust & Security services bring together information security, privacy, AI governance, cybersecurity and regulatory compliance to help organizations strengthen their overall digital trust posture.

What the Evidence Shows About Shadow AI in India

Shadow AI should not be described as India’s “number one” cybersecurity threat without authoritative evidence supporting that ranking.

There is no established public ranking showing Shadow AI ahead of ransomware, identity attacks, cloud misconfiguration, supply-chain compromise or insider threats.

However, available evidence does show that Indian CISOs are increasingly concerned about GenAI-related data risks.

Proofpoint’s 2025 Voice of the CISO report found that 74% of Indian CISOs were concerned about customer data loss through GenAI tools, while 74% of organizations restricted or blocked employee GenAI usage. At the same time, 64% said enabling GenAI tool use was a strategic priority.

These findings point to an important shift:

The question is no longer whether organizations will use AI. The question is how they will use it safely.

Conclusion: Making AI Adoption Safer

Cloud security is not going away.

Shadow AI adds another layer to the cybersecurity challenge, sitting at the intersection of data, identity, applications, vendors and human behaviour.

Organizations cannot secure what they cannot see.

If security teams do not know which AI tools employees are using, what information is being submitted, what those tools can access or where the resulting data can go, traditional security controls can leave gaps.

For Indian CISOs, Shadow AI should be treated as a cross-functional risk involving cybersecurity, privacy, legal, compliance, intellectual property and third-party governance.

The response starts with visibility:

Discover the tools.
Classify the data.
Approve safer alternatives.
Limit permissions.
Assess third parties.
Monitor appropriately.
Train employees.
Test the controls.

The strongest organizations will not necessarily try to stop AI adoption.

They will make responsible AI use easier than risky AI use.

Because the real Shadow AI problem is not that employees are using AI.

It is that security teams may not know how, where or what they are using it for.