Artificial intelligence has moved beyond sanctioned enterprise systems. In India’s business world, a quiet revolution is happening. This change is driven not by official IT rollouts but by employees and departments who are experimenting with unsanctioned AI tools.
This trend, known as Shadow AI, resembles the earlier days of shadow IT when teams used unapproved cloud services to speed up their work. The key difference today is that modern tools—like generative AI platforms and autonomous agents—function with much more independence, often outside the usual oversight. The result is a growing and hard-to-monitor attack surface for companies that are already struggling to manage complex digital environments.
While this trend is seen globally, the challenge in India is particularly urgent. The country’s rapid AI adoption, spurred by innovation and productivity goals, has outpaced governance frameworks. This leaves Chief Information Security Officers scrambling to manage risks effectively.
**From Experimentation to Enterprise-Wide Exposure**
Indian technology leaders view data privacy, source code integrity, and compliance as the main areas of risk.
“The biggest risk often lies in customer data and compliance. With the rise of generative AI tools, information generated and processed can evade traditional security measures,” says Arjun Nagulapally, CTO at AIONOS.
He notes that modern attackers increasingly focus on customer personal information, proprietary algorithms, and confidential code, particularly when these assets are processed in unmonitored AI workflows.
Dr. Kannan Srinivasan, Practice Head at Happiest Minds Technologies, agrees. “Unauthorized use of AI tools has become a significant issue across different industries,” he says. “It remains difficult to measure the full extent of this risk.”
Organizations are implementing technical monitoring systems, AI activity audits, and detection models to keep track of how employees use AI platforms. However, the rapid development of these tools means defenses must evolve quickly.
At Akamai, Reuben Koh highlights the growing risk of uncontrolled data flows between AI-powered integrations and unmanaged APIs. “These AI agents can act independently, exchange data, and establish new connections in seconds,” he explains. To counter this, companies are integrating API observability and prompt filtering into their security systems.
From an identity and privilege perspective, Sumit Srivastava from CyberArk India emphasizes that monitoring unmanaged AI agents and enforcing access control have become essential. “This offers complete visibility, allowing organizations to shift from reactive to proactive risk management,” he explains.
**Policy Evolution: India’s Path vs. Global Standards**
The discussion around AI governance in India is evolving rapidly. It is shifting from isolated IT control to a framework that understands the risks of unchecked innovation.
Siddhesh Naik, Country Leader for Data & AI Software at IBM India South Asia, points out a concerning trend: despite shadow AI being a major cause of data breach costs—adding approximately ₹17.9 million per incident—only 42% of Indian businesses have formal policies to detect or manage it.
As generative AI becomes a regular part of daily operations—from marketing copy to code creation—companies are focusing on incorporating “trust, transparency, and accountability” into every AI deployment.
Globally, compliance frameworks are more developed, but challenges remain. According to Cycode’s “State of Product Security: AI Era 2026,” while every company surveyed uses AI-generated code, 81% of security teams lack visibility into how these tools are used.
In India, similar gaps exist, but regulatory and client demands are driving quicker changes. As Nagulapally explains, businesses are now expected to keep model audit trails, explainability logs, compliance matrices, and ethical risk documents. This indicates that the Digital Personal Data Protection (DPDP) Act and related sector laws are setting new standards for responsible AI use.
**Balancing Innovation and Control: The Rise of AI Sandboxes**
Despite the risks, industry leaders agree that outright banning AI tools is not the answer. Instead, attention is turning towards controlled enablement—encouraging innovation within safe, governed environments.
Dr. Kannan Srinivasan and Kiran Kumar Bandari, Head of R&D at Hexagon India, describe a shift toward “controlled sandboxes and internal platforms,” which allow teams to experiment safely. These setups support real-time monitoring, compliance tagging, and federated governance to ensure innovation continues without unnecessary risks.
At Check Point, an internal policy prevents uploading R&D data into public AI models, while automated alerts and access restrictions are enforced to safeguard proprietary information.
Across organizations, common practices are emerging. Continuous AI activity logging, prompt monitoring, data loss prevention (DLP) integration, and role-based access control are becoming standard requirements.
As Umesh Shah, Director at Orient Technologies, states, “Ban policies just push risky AI use underground. Leadership is focused on enabling approved creativity within clear boundaries.”
**Governance Maturity and Regulatory Pressure**
The demand for clear, auditable AI governance is growing among Indian businesses.
“In India, proof of AI governance is no longer optional—it’s essential for enterprise procurement and partner programs,” says Shah of Orient Technologies. “Organizations that create trust-centered AI foundations now will define standards for enterprise adoption in the future.”
This means companies must not only create policies but also demonstrate compliance through monitoring dashboards, audit logs, and regular risk assessments. Many are investing in AI asset inventories—internal databases cataloging every AI system, model, and integration used across departments.
These investments are not just about compliance; they are becoming competitive advantages. As international clients and regulators ask for evidence of responsible AI use, companies that show transparency and control will enjoy greater trust.
Education also plays a key role. Top firms are launching employee AI literacy programs to teach staff not only how to use AI effectively but also how to do so safely and ethically within policy guidelines.
**The Road Ahead: Embedding Accountability Into AI**
The rise of shadow AI has compelled Indian companies to rethink cybersecurity and governance fundamentals. What began as a quiet wave of employee-led experimentation has become a priority in boardrooms, influencing investments, purchasing, and compliance strategies.
The response is becoming more advanced. Instead of restricting access, companies are creating AI governance frameworks that balance innovation with accountability. From automated policy enforcement to regulated AI sandboxes, the focus is shifting towards sustainable enablement instead of just reactive containment.
In the end, the new frontier in enterprise security will be about making sure every AI interaction is traceable, explainable, and adheres to trust principles rather than trying to stop AI altogether.
As Indian companies advance in their AI adoption, their experiences will likely shape the global dialogue on what “responsible AI” truly means—not as an ambition, but as a practical reality.
