Cisco buys Galileo to strengthen Splunk’s agentic monitoring capabilities

2026-04-10 · Source: AI – SiliconANGLE · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Intermediate, extended

Summary

Cisco Systems Inc. is acquiring Galileo Technologies Inc., an AI-native observability startup, to enhance the trustworthiness and reliability of "agentic workforces." Galileo's platform provides tools for observing and evaluating AI models, enabling real-time monitoring and guardrails for multiagent systems. These capabilities will be integrated into Cisco's Splunk observability platform, improving AI agent monitoring, providing real-time visibility into actions, and bolstering security within the agent development lifecycle. Cisco aims to address the unique security challenges of AI agents, which can produce unexpected or harmful outputs, by extending observability beyond traditional metrics to include issues like hallucinations, bias, and cost tracking. The acquisition price was not disclosed, with the deal expected to close by the end of Q4.

Key takeaway

For AI Architects and CTOs evaluating AI agent deployments, Cisco's acquisition of Galileo signals a critical shift towards integrated, AI-native observability and security. Your strategy must prioritize "action control" over traditional access control for agents, ensuring continuous monitoring and dynamic guardrails. Invest in solutions that provide granular visibility into agent behavior, including detection of hallucinations and bias, to mitigate risks and build trusted AI systems.

Key insights

Cisco's acquisition of Galileo aims to establish trust and security in autonomous AI agents through advanced observability.

Principles

• AI agent security requires action control, not just access control.
• Observability must extend to AI-specific issues like hallucinations and bias.
• AI fluency will be critical for future workforce employability.

Method

Galileo's platform instruments every stage of agent development, from prompt optimization and model selection to production monitoring, observability, and guardrail enforcement, providing a complete solution for AI quality and failure detection.

In practice

• Implement just-in-time, just-enough, and just-for-enough-time permissions for AI agents.
• Utilize agentic SOCs for machine-scale detection and response to AI-driven threats.
• Leverage open-source tools like DefenseClaw for AI agent security.

Topics

• Cisco Acquisition
• AI Observability
• Agentic AI Security
• Multiagent Systems
• Splunk Platform

Best for: AI Architect, Investor, CTO, AI Security Engineer, Director of AI/ML, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.