Cisco Expands AI Security and Observability for Agentic Systems

/ AI, Cyber Security, Observability

The News

Cisco announced its intent to acquire Galileo to enhance AI agent observability and protection, joined Anthropic’s Project Glasswing to advance AI security, and released new research on industrial AI adoption. Altogether, Cisco is signaling a broader push to secure and operationalize AI at scale.

Analysis

AI Observability Evolves Into a Control Layer for Agentic Systems

The application development market is rapidly shifting toward agentic AI systems, where autonomous agents execute workflows across applications and infrastructure. Cisco’s planned acquisition of Galileo highlights a growing need for real-time observability across these systems, not just at runtime, but throughout the entire development lifecycle.

Efficiently Connected research shows that real-time visibility remains a top priority as organizations scale AI workloads. However, traditional observability tools were not designed to track agent behavior, model performance, or token usage across distributed systems.

For developers, this signals a transition toward observability as a control layer that provides not only insight into system behavior but also the ability to enforce guardrails and ensure reliable execution.

Security Shifts to Keep Pace with Autonomous AI

Cisco’s involvement in Anthropic’s Project Glasswing reflects a broader industry response to the rise of AI-driven threats. As AI systems become more capable, they also introduce new attack surfaces and vulnerabilities that require more advanced defensive strategies.

The use of frontier models to identify and remediate vulnerabilities suggests a shift toward AI-assisted security operations. Efficiently Connected data indicates that organizations are increasingly prioritizing automation in security workflows, particularly as environments grow more complex.

For developers, this introduces new considerations around secure application design. Systems must account for both AI-enabled functionality and AI-enabled threats, requiring tighter integration between development and security practices.

Market Challenges and Insights in Scaling AI Across Enterprises

Organizations continue to face challenges in operationalizing AI at scale. One of the most significant is trust: ensuring that AI systems behave as intended, align with business objectives, and operate within defined constraints.

Another challenge is visibility. As AI agents interact with multiple systems and data sources, tracking their behavior and performance becomes increasingly difficult. Without adequate visibility, organizations risk inefficiencies, unexpected costs, and security vulnerabilities.

Additionally, industrial and operational environments introduce unique complexities. Cisco’s State of Industrial AI report highlights the intersection of IT and OT systems, where AI adoption must account for physical infrastructure, real-time constraints, and heightened security requirements.

Toward Integrated AI Security, Observability, and Governance

Cisco’s announcements point toward a more integrated approach to AI operations, where observability, security, and governance are tightly connected. By embedding these capabilities into platforms like Splunk Observability Cloud, organizations can gain a more holistic view of AI system behavior.

For developers, this suggests a future where building AI applications involves not only developing models and workflows but also integrating monitoring, security, and governance from the outset. Real-time guardrails, policy enforcement, and behavioral insights will become standard components of AI-enabled systems.

The addition of leadership with enterprise transformation experience further reinforces the importance of aligning technical innovation with organizational change, particularly as AI adoption expands across business functions.

Looking Ahead

The application development market is moving toward a model where AI systems are continuously monitored, secured, and governed as they operate. As agentic AI becomes more prevalent, the ability to ensure trust, visibility, and control will be critical for enterprise adoption.

Cisco’s direction suggests that future platforms will converge around unified AI control planes, combining observability, security, and governance into a single operational framework. For developers, this evolution will require building applications that are not only intelligent, but also transparent, secure, and accountable at scale.

Author

  • With over 15 years of hands-on experience in operations roles across legal, financial, and technology sectors, Sam Weston brings deep expertise in the systems that power modern enterprises such as ERP, CRM, HCM, CX, and beyond. Her career has spanned the full spectrum of enterprise applications, from optimizing business processes and managing platforms to leading digital transformation initiatives.

    Sam has transitioned her expertise into the analyst arena, focusing on enterprise applications and the evolving role they play in business productivity and transformation. She provides independent insights that bridge technology capabilities with business outcomes, helping organizations and vendors alike navigate a changing enterprise software landscape.

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