Palo Alto Networks completes Chronosphere acquistion to close a growing visibility gap in the AI era

Palo Alto Networks has completed its acquisition of Chronosphere, bringing together security and observability at a time when enterprises are struggling to understand, and control, the data flows that now run their businesses.

As companies embed AI deeper into daily operations, from customer service to infrastructure management, the challenge has shifted. It is no longer just about stopping attacks. It is about knowing what is happening inside increasingly complex cloud systems, in real time, and doing so without drowning in data or cost.

Chronosphere was built for that environment. Its cloud-native observability platform was designed to handle the scale and volatility of modern infrastructure, where traditional monitoring tools often fail. In 2025, the company was named a leader in Gartner’s Magic Quadrant for Observability Platforms, reflecting its growing footprint among large, complex digital organisations.

Palo Alto Networks says the acquisition addresses a blind spot that has become more visible as AI systems move into production. Security tools can flag anomalies or threats, but without detailed operational context, responses often rely on manual investigation and guesswork. In an AI-driven environment, that lag can translate directly into outages, customer impact, or financial loss.

Nikesh Arora, chairman and chief executive of Palo Alto Networks, said customers are increasingly asking for consolidation rather than more tools.

“Enterprises today are looking for fewer vendors, deeper partnerships, and platforms they can rely on for mission-critical security and operations,” Arora said. “Great security starts with deep visibility into all your data, and Chronosphere provides that foundation.”

The company plans to integrate Chronosphere’s observability capabilities with its Cortex platform, including Cortex AgentiX, which uses AI agents to automate parts of security and IT operations. The goal is to allow those agents to not only detect issues, but also understand their context across applications, infrastructure, and AI systems — and resolve them before they affect users or revenue.

Chronosphere’s telemetry pipeline will remain available as a standalone product. The pipeline acts as a control layer for observability data, filtering out low-value signals before they are stored or analysed. The company says this approach can reduce data volumes by more than 30 percent and requires significantly less infrastructure than legacy alternatives — a claim that speaks to a growing concern among security and operations teams: the cost of data itself.

That capability is expected to play a role in Palo Alto Networks’ Cortex XSIAM strategy, which focuses on large-scale, automated security operations. As organisations generate more telemetry in pursuit of better visibility, many have found that observability costs rise faster than the risks they are meant to manage.

Martin Mao, co-founder and chief executive of Chronosphere, who will join Palo Alto Networks as senior vice president and general manager of observability, said the deal reflects a shift in how control is defined in the AI era.

“Chronosphere was built to help the world’s most complex digital organisations operate at scale with confidence,” Mao said. “Together, we’re delivering a new standard where observability, security, and AI come together to give organisations control over their most valuable asset: data.”

The acquisition lands at a moment when many large companies are discovering that AI systems are only as reliable as the data feeding them. As models are pushed into production, from customer support to fraud detection, failures are increasingly traced back not to the algorithms themselves, but to blind spots in infrastructure and monitoring.

By combining security tooling with observability, Palo Alto Networks is betting that customers want fewer layers between seeing what is happening in their systems and acting on it. Whether that integration delivers meaningful automation, rather than another complex platform to manage, will depend on how quickly the technology works in real environments.

For enterprises already struggling with cloud sprawl, rising telemetry costs, and growing dependence on AI-driven systems, the appeal is clear. The challenge now is execution — turning visibility into action, without adding another source of operational complexity.