Sharda Tickoo, Country Manager, India And SAARC At TrendAI, On Why Agentic AI Is Forcing Indian Enterprises To Rethink Cybersecurity Governance

Indian enterprises are no longer asking whether to adopt AI—they're asking how to adopt it safely and at scale. As AI becomes embedded across customer service, software development, operations, and decision-making, cybersecurity is evolving from protecting IT to enabling trusted AI.

• Visibility: We cannot control what we cannot see. CISOs need real-time insight into what agents exist, what they can access, and what they are actually doing across the enterprise.

• Governance at the interaction layer: Traditional controls secured the endpoint or the application, but agents operate across multiple systems, cloud platforms, and APIs simultaneously. Governance must shift to that interaction point.

• Rapid incident response: when an agent goes rogue or gets compromised, the blast radius expands at machine speed. Traditional post-breach response is too slow. Detection and containment must happen in real time.

For Indian enterprises, the shift is from compliance-driven security to capability-driven security. It translates into the ability to see, trust, and control autonomous systems.

The biggest gap is in AI governance. Enterprises are moving from AI experiments to AI everywhere, while security programs are still built for traditional applications. We're protecting infrastructure designed for humans, but we're now deploying systems that can reason, make decisions, and interact autonomously.

The gap is stark and dangerous. Studies have shown that the majority of Indian enterprises are already piloting agentic AI, yet only a handful of them have a shared understanding of what agentic AI actually is. Enterprises are scaling systems they have not yet defined. That is the core problem.

When you cannot articulate what agentic AI is, what it can do, what permissions it needs, and how it behaves, you cannot govern it. You are essentially deploying autonomous decision-making systems across your critical business processes without a playbook. The readiness gap sits between deployment speed and governance maturity. Enterprise leaders want the productivity gains agentic AI promises. Security teams want control and visibility they do not yet have. The intersection of those two desires is where the real risk lives.

There is also a skills gap. Governing agentic AI requires a fundamentally different mindset. Most security professionals were trained to defend against attack vectors, not to architect governance for autonomous systems.

It breaks almost every assumption cybersecurity was built on. Traditional automation was deterministic. Agentic AI is probabilistic. An agent can reason about problems, choose its own tools, spawn sub-agents, create temporary digital identities, and trigger workflows without any human in the loop. The attack surface is no longer a fixed perimeter. It is now dynamic and continuously expanding.

Consider a single compromised credential. In the old model, one employee's account gets breached. In the agentic AI model, that credential unlocks access to APIs, cloud workflows, and automated systems that the agent can leverage to cause damage far beyond what that single employee could do. An attacker does not just get into your systems; they get an autonomous workforce to orchestrate attacks across your enterprise. Lateral movement happens at machine speed. A single prompt injection or data poisoning attack can trigger cascading failures across interconnected agents.

The equation fundamentally shifts from "secure the perimeter" to "govern the interaction layer." Every API call, every tool invocation, every decision an agent makes becomes a potential attack point. This demands real-time monitoring, adaptive governance, and the ability to intervene at machine speed.

• Cascade failures: An agent makes a decision based on incomplete or poisoned data, triggering actions that ripple across interconnected systems. One bad decision cascades into dozens.

• Intent distortion: An agent optimizes for its stated goal in ways that were never intended. A trading agent maximizes profits. An access agent simplifies its own job by escalating its own permissions. An agent seeking efficiency might bypass security controls to move faster.

• Supply-chain compromise: If attackers manipulate the data, APIs, or tools an agent relies on, they do not need to compromise the agent itself; they compromise its decision-making.

• Velocity mismatch: Human governance cannot keep pace with agent-speed decision-making. By the time a human realizes something went wrong, the agent has already caused significant damage.

This demands new governance controls like kill switches, spending caps, step limits, approval gates for high-impact decisions, and continuous auditing of agent behavior against policy.

• Visibility at the interaction layer. Discover where agents exist, what they can access, how they interact with other systems, and what they are actually doing in real time. That visibility must cover cloud platforms, SaaS tools, internal APIs, and autonomous workflows.

• Governance that is operational. Policies must be enforceable at the moment agents act. Traditional governance documents sit in repositories. Agentic governance lives in the system, enforcing constraints at runtime like kill switches, spending caps, step limits, approval gates for sensitive decisions.

• Detection and response built for machine-speed threats. When an agent gets compromised or behaves anomalously, detection must happen in seconds, not hours. Response must be automated where possible.

TrendAI Vision One™ addresses this with Agentic Governance Gateway through discovery, monitoring, policy enforcement, and behavioral analysis all operating on the interaction layer where agent decisions turn into actions. Combined with TrendAI's threat intelligence and Zero Day Initiative expertise, organizations gain the visibility and control needed to scale agentic AI fearlessly. Indian enterprises should be able to deploy agentic AI with confidence, knowing they can see it, trust it, and control it. That is the foundation for responsible AI adoption.