Orca Security Brings AI-Native Cloud Risk Visibility to Claude and Beyond

By integrating with Anthropic’s Model Context Protocol, Orca is making deep cloud telemetry conversational—for everyone, not just security experts.

The future of cloud security might just be a simple question away.

Orca Security, known for pioneering agentless cloud security, has announced its integration with the Model Context Protocol (MCP)—an open standard developed by Anthropic to help large language models interface securely with real-world data. With the launch of the Orca MCP Server, security teams can now query deep cloud risk data from the Orca Unified Data Model directly through Claude or any other GenAI assistant, without opening the Orca platform itself.

In other words: Cloud security just went conversational.

“The promise of GenAI was quick, detailed answers to simple, natural language queries. This reality has largely eluded security professionals due to a lack of integrations with popular AI tooling,” said Gil Geron, CEO and Co-founder of Orca Security. “Universal access to Orca cloud security telemetry through any AI chatbot gives security professionals the deep cloud security visibility that would previously have only been accessible as a result of intensive, manual investigation.”

From Data Lake to Chat Window

It’s a classic pain point in cybersecurity: The insights exist, but surfacing them often requires advanced query languages, dozens of dashboards, and time security teams don’t have. For cloud environments—especially those spanning AWS, Azure, and GCP—the complexity multiplies.

Orca’s solution? Let AI do the digging.

With the new MCP integration, a security analyst could simply ask, “Where are we out of compliance with CIS benchmarks?” or “Do we have any publicly exposed S3 buckets with sensitive data?” and get back detailed, structured answers within seconds—powered by Orca’s patented SideScanning technology and its Unified Data Model.

The real innovation here is that these queries can happen outside the Orca platform. Claude, OpenAI-powered chatbots, and other GenAI tools can now interface with an organization’s own Orca environment via the open-source MCP framework.

This means desktop apps, internal portals, or custom tools can securely tap into cloud risk intelligence—all without building bespoke integrations or exposing sensitive telemetry.

A Real-Time Window Into Risk

The implications are huge. As security teams increasingly serve as real-time advisors to executives, product leads, and compliance teams, the need for fast, accurate answers has skyrocketed.

Previously, cloud risk analysis was a game of speed vs. depth: fast meant shallow, and deep meant slow. Now, with GenAI and the Orca MCP Server, the two are no longer mutually exclusive.

The integration also plays nicely with Orca’s own AI-Driven Search tools, which already support more than 50 languages. But the MCP Server opens the door to third-party AI, making this a significant leap in accessibility and extensibility.

The Bigger Picture: Making Cloud Security More Human

As GenAI evolves from novelty to necessity, security vendors are under pressure to adapt. Tools that once required specialized knowledge are being reimagined for broader audiences—product managers, DevOps engineers, even business leaders.

Orca’s move is part of a larger trend: shifting cybersecurity from a siloed function into a cross-functional conversation. By using LLMs as the interface, Orca is enabling a broader set of stakeholders to ask smart questions about cloud security—and get meaningful, accurate answers.

This democratization of access could also have an unexpected benefit: shrinking the talent gap. With natural language queries replacing complex logic and dashboards, entry-level analysts (or even non-security teams) could contribute to identifying and remediating cloud risks.

Standards Matter—and So Does Timing

By adopting Anthropic’s open-source Model Context Protocol, Orca becomes the first cloud security platform to embrace a standardized AI interface for telemetry. This kind of plug-and-play compatibility will be increasingly important as organizations invest in AI agents trained on their own data.

The fact that the integration works without direct access to the Orca Platform is a signal: context is no longer locked inside tools—it’s becoming portable, accessible, and, most importantly, useful.