As AI Agents Gain Power, Security Becomes the Real Test

As artificial intelligence systems become more capable, a new concern is rising alongside the excitement. According to remarks highlighted at the India AI Impact Summit 2026, the biggest obstacle to useful AI may no longer be model intelligence. It may be security.

Researcher and podcaster Lex Fridman, speaking at the summit, framed the issue in stark terms. The same factors that make AI agents genuinely helpful also increase the potential for harm if something fails. In other words, more capability is arriving with more responsibility, and the guardrails are still catching up.

Fridman’s simple but uncomfortable framework

Fridman broke the power of modern AI agents into three pieces. First is the intelligence of the model itself. Second is how much data the system can access. Third is how much authority the agent is given to act on a user’s behalf.

His key argument is that the first piece is improving rapidly across the industry. The real constraint is shifting toward the second and third. As agents gain deeper access and more autonomy, security risk expands quickly.

In practical terms, intelligence is no longer the obvious bottleneck in many use cases. Trust is.

A summit that reflects rising global stakes

The comments came during the India AI Impact Summit 2026 at Bharat Mandapam in New Delhi, an event positioned as one of the world’s largest AI gatherings. The speaker lineup itself signaled how seriously governments and industry leaders now view agentic AI.

Technology leaders such as Sundar Pichai, Sam Altman, and Dario Amodei were part of the broader conversation, alongside political leaders including Prime Minister Narendra Modi and other heads of state. The setting underscored a clear shift. AI agents are no longer experimental curiosities. They are being discussed as infrastructure that could reshape work, governance, and security.

Why more access creates more exposure

The logic behind Fridman’s warning is straightforward. AI agents become more useful when they can see more of a user’s digital world. Connecting email, documents, calendars, and internal systems gives the model valuable context. But each connection also increases the potential blast radius if something goes wrong.

Security researchers have been flagging this tension for several years. Sensitive data fed into AI systems can surface in logs, training pipelines, or future outputs if protections are weak or misconfigured. Even well-intentioned deployments can create long-term data exposure risks.

What makes the current moment different is the speed of capability growth. Access is expanding faster than most organizations’ security models were designed to handle.

Autonomy changes the risk equation

The second half of Fridman’s concern centers on control. When AI systems only generate text, mistakes are usually contained. When they begin executing actions, the consequences become more concrete.

Modern agent frameworks are increasingly able to send messages, modify files, trigger workflows, and interact with external tools. That shift introduces new classes of failure. Prompt injection attacks, tool misuse, and unintended automation loops are now active areas of security research.

Experts often note that large language models can convincingly simulate reasoning without consistently performing it. Granting these systems broader operational authority does not eliminate that limitation. In some cases, it simply allows mistakes to happen faster and at larger scale.

Policy and governance are racing to keep pace

Regulators and enterprise risk teams are beginning to converge on the same two questions Fridman’s comments imply. What happens to user data once it enters an AI system, and who is accountable when an autonomous agent takes action?

The EU AI Act, which moves toward fuller enforcement in 2026, reflects this shift with stronger requirements around transparency, risk classification, and oversight. Corporate AI governance playbooks are also evolving. Many now recommend restricting agent access to highly sensitive domains unless strict controls and human oversight are in place.

Even so, the pace of technical deployment continues to outstrip the maturity of many governance frameworks.

The adoption dilemma now facing organizations

The industry is entering an uncomfortable middle phase. To unlock the full value of AI agents, companies are encouraged to give them deeper context and more freedom to operate. That is where the productivity gains live.

At the same time, every additional permission increases exposure. Connecting financial systems, code repositories, or customer data can dramatically improve automation quality. It can also magnify the impact of misalignment, bugs, or adversarial inputs.

This creates a balancing act that many organizations are still learning to manage. The technology is moving quickly. Risk models are evolving more cautiously.

A shift from capability questions to trust questions

For years, the central debate around AI focused on whether models were smart enough to be useful. That question is increasingly being replaced by a more practical one. Can these systems be trusted with meaningful authority?

Fridman’s remarks capture this turning point. Intelligence is improving at a rapid clip. The limiting factor is becoming whether security, access control, and verification systems can keep up.

Until that gap narrows, many experts believe adoption will be governed less by what AI agents can technically do and more by what organizations feel safe letting them do.

The bottom line

AI agents are clearly entering a more powerful phase. They are gaining deeper context, broader tool access, and increasing autonomy across digital workflows. But with that progress comes a parallel rise in risk.

The message emerging from the India AI Impact Summit is not anti-AI. It is cautionary. The next major breakthrough in agent adoption may depend less on smarter models and more on building systems that can be trusted with real-world responsibility.

In the race toward more capable AI, usefulness and safety are now moving together. The question is whether the safeguards will scale as fast as the ambition.