Authored by Bikramdeep Singh, India Country Manager, Proofpoint
Artificial intelligence is no longer sitting at the edge of enterprise experimentation. Across India, AI assistants and autonomous agents are moving into live business environments, embedded across email, customer support, internal messaging, cloud applications and collaboration workflows.
That shift is creating enormous opportunity. AI can help organizations move faster, automate routine work, improve customer experience and support better decision-making. But it is also changing the security equation. As AI becomes part of how work gets done, it is expanding where risks appear, how quickly incidents unfold, and how difficult it is for security teams to investigate what happened.
Proofpoint’s 2026 AI and Human Risk Landscape report shows that AI adoption in India has already moved well beyond the pilot stage. 94% of organizations in India have deployed AI assistants beyond the pilot stage, while 88% are advancing autonomous agents. Yet security readiness has not kept pace. More than one-third (35%) of organizations describe their AI security posture as catching up, inconsistent, or reactive. More than three in five (63%) have already experienced a suspicious or confirmed AI-related incident.
This is the gap that should concern security leaders. AI is not waiting for governance frameworks to mature. Security leaders in India are under more pressure to address key areas of concern.
For many years, cybersecurity strategies were built around familiar control points: email, endpoints, cloud applications, identities and data repositories. Those still matter. But AI is now connecting these environments in new ways, allowing risk to move across workflows at machine speed.
In India, email remains the most common AI-related threat vector, affecting 70% of organizations. But exposure now extends much further: SaaS and cloud applications at 59%, SMS or text at 55%, and collaboration tools such as Teams or Slack at 54%. Among organizations that experienced an AI-related incident, exposure rises across every channel, including 73% in email and 65% involving SaaS and cloud applications.
This matters because enterprise work no longer happens in a single channel. A sensitive document may move from email into a collaboration platform, be summarised by an AI assistant, stored in a cloud application, and referenced by an autonomous workflow. Each step creates another point at which data, identity, and intent need to be understood.
Many organizations already have some form of AI security controls, such as monitoring shadow AI applications. However, the critical visibility is whether those controls can see across the connected environment how AI is actually being used.
One of the most common structural errors in how organizations approach AI security is treating it as a separate workstream from data security. It is not. They are facets of the same problem, and solving one without addressing the other creates compounding exposure.
The earliest AI security challenge was clear: employees were using consumer AI tools to process sensitive business information. In 63% of employees who used AI applications, confidential company data, such as source code and customer records, to personal chatbot accounts. According to IBM's Cost of a Data Breach Report, shadow AI breaches cost an average of US$670,000 more than standard security incidents, driven by delayed detection and difficulty determining the scope of exposure.
The second wave is more complex. As organizations moved to enterprise AI platforms — Microsoft Copilot, Salesforce Einstein, and others — the question became not whether data was leaving the organization, but whether AI tools were accessing only the data they were supposed to. That is a data security problem expressed through an AI lens.
The third wave is real-time and agentic. Autonomous agents do not just respond to prompts. Similar to humans, they connect to external tools and MCP servers, acquire new capabilities, and act on data across connected systems. Understanding what an AI agent is doing requires capturing not just the prompt and response, but every tool call and downstream action in between. When security teams lack visibility into what AI is connecting to and acquiring, they cannot tell the board they have it under control.
Gartner projects that by the end of 2026, up to 40% of enterprise applications will integrate with AI agents, up from less than 5% in 2025. It also predicts that by 2028, 25% of all enterprise GenAI applications will experience at least five minor security incidents per year, up from 9% in 2025. The risk is outpacing governance.
Security and data governance teams need a shared view of what data exists, who has access to it, and how AI agents are actually using it. Having a clear view of all your data is not a fiction; it should be the foundation for building robust AI security for any organization.
Only 57% of organizations in India say they are fully prepared to investigate an AI- or agent-related incident, while 47% report difficulty correlating threats across multiple channels. As AI activity increasingly spans email, collaboration platforms and cloud systems, visibility across connected environments becomes critical for understanding what happened and responding effectively.
Fragmented security stacks are compounding the challenge. Almost all organizations in India say managing multiple security tools is at least moderately challenging, and more than half describe it as very or extremely difficult. Respondents cite operational cost pressures, integration challenges and difficulty correlating threats.
When controls sit in separate systems, security teams lose time moving between dashboards, reconciling alerts and trying to connect activity across email, cloud, collaboration and AI systems. That delay matters when incidents can quickly spread across workflows.
As AI scales, security architecture becomes a strategic priority. Organizations are recognizing that AI security cannot be solved with isolated controls. It requires an architecture that can protect people, data, and AI systems across channels. Over the next 12 months, 67% of organizations in India plan to expand AI protections, 71% intend to extend collaboration channels, and 58% expect to move toward a unified platform approach.
AI adoption in India is not slowing down. The boards and CEOs driving it are right that falling behind carries real competitive cost. The security leaders are now in a position to enable this AI innovation with the visibility to secure it, govern it, and defend it. That is what setting the pace looks like.