Hidden algorithms: Shadow AI reshapes Indian enterprise security

Shadow AI—the use of artificial intelligence tools beyond official IT oversight—has added new complexity to enterprise risk management in India. Over the past decade, the shift from conventional shadow IT to increasingly autonomous, AI-powered agents has significantly broadened the attack surface. Earlier, unapproved cloud apps and unsanctioned software posed security threats; today, it is the invisible adoption of generative AI platforms across teams and departments that is hardest to track. While this issue is global, the scope and urgency in Indian enterprises are acute, given the recent surge in AI-fueled productivity initiatives—often ahead of governance frameworks.

From Experimentation to Enterprise-Wide Exposure

Industry leaders consistently highlight data, code, and compliance as the areas of highest exposure. “The greatest exposure is often in customer data and compliance risk. With the proliferation of generative AI tools, data generated and processed can bypass traditional security models,” says Arjun Nagulapally, CTO at AIONOS. He adds that enterprise attacks today target customer PII, sensitive business algorithms, and proprietary code—often in AI workflows outside monitored environments.​

Dr. Kannan Srinivasan, Practice Head at Happiest Minds Technologies, notes a parallel concern: “Unauthorized use of AI tools has emerged as a major challenge across industries…measuring the extent of this exposure remains a challenge.” Approaches like technical monitoring, process audits, and purpose-built detection models are being deployed, although the evolving AI landscape means these measures must be continuously updated.​

At Akamai, uncontrolled data flows across AI-powered integrations and unmanaged APIs are top concerns. “These AI agents can independently initiate actions, exchange data, and create new connections in seconds,” says Reuben Koh of Akamai. Modernized API observability and prompt filtering are now being embedded into enterprise security stacks.​

Sumit Srivastava, Solutions Engineering Director of CyberArk India, highlights that, “The use of unsanctioned AI tools poses the most significant threat to customer data and compliance. Monitoring unmanaged AI agents and access levels provides complete visibility, enabling organizations to move from reactive to proactive risk management.”

Policy Evolution: India vs. Global Benchmarks

Indian enterprises are shifting from reactive IT controls to proactive, AI-literate governance. Siddhesh Naik, Country Leader, Data AI Software, IBM India South Asia, points out that, despite shadow AI being a top breach cost driver (adding about ₹17.9 million per incident), only 42% of Indian firms currently have policies in place to detect or manage it. Tech leaders note this exposure will likely increase as generative AI becomes commonplace across business functions, so organizations are “embedding trust, transparency, and accountability at the core of every AI deployment”.​

Globally, the trajectory is similar but compliance mandates tend to be more mature. Cycode’s “State of Product Security: AI Era 2026” finds that while 100% of surveyed companies now have AI-generated code, 81% of security teams still lack visibility into where or how these tools are being used. In India, similar patterns are emerging as regulators and clients demand “model audit trails, explainability records, DPDP compliance matrices, and ethical risk documentation,” according to AIONOS’ Nagulapally. The DPDP Act and sectoral laws are rapidly raising the bar for what constitutes responsible AI deployment.​

Innovation and Control: Sandboxes, Guardrails, and Managed Enablement

The risk of a blanket ban on AI is widely acknowledged. Leaders like Dr. Kannan Srinivasan and Hexagon RD India's Kiran Kumar Bandari describe a shift towards “controlled sandboxes and internal platforms” rather than outright prohibition. Controlled experimentation—supported by real-time monitoring, compliance tagging, and federated governance boards—enables innovation while reducing systemic risk. At Check Point, staff cannot enter R&D data into external AI models; alerts and access restrictions are in place to enforce this.​

Feedback from various CISOs and CIOs agrees that continuous AI activity logging, prompt monitoring, DLP integration, and role-based access are now core requirements for safe and compliant AI adoption. Umesh Shah, Director at Orient Technologies, emphasizes that “ban” policies simply drive risky AI use underground; the leadership focus is on enabling sanctioned creativity within clear boundaries.

Governance Maturity and Regulatory Pressure

Across conversations eith industry leaders, one theme stands out: the need for demonstrable, auditable AI governance. “In India, evidence of AI governance is no longer a nice-to-have, it is now a pre-requisite for enterprise procurement and partner programs. Organizations that build trust-centric AI foundations today will set the standard for enterprise-grade adoption tomorrow,” says Shah of Orient Technologies.​

The ability to monitor, audit, and evidence every AI interaction is referenced as both a strategic necessity and a competitive differentiator. As Indian and global clients, partners, and regulators increasingly require proof of responsible AI operations, internal investments are being routed into AI asset inventories, continuous risk assessments, and ongoing employee education.

Conclusion

Shadow AI has changed not just the shape of enterprise security risk but also the response models of leading Indian firms. The experience of Indian enterprises shows that automated tools, policy innovation, regulatory alignment, and robust internal governance are all vital in navigating this shifting landscape. The next frontier will be less about restricting AI, and more about embedding clarity, accountability, and resilience into every step of AI adoption—from experimentation through to enterprise scale.