As enterprises rapidly deploy AI driven applications and agentic systems, securing runtime behavior has become a critical challenge for cybersecurity teams. Miggo Security latest platform expansion reflects a growing industry shift toward protecting AI environments where risks actually materialize during execution.

Miggo Security has announced a major upgrade to its Runtime Defense Platform, introducing capabilities such as AI Bill of Materials, runtime guardrails, and Agentic Detection and Response. The expansion is designed to provide deeper visibility and control over AI agents, Model Context Protocol toolchains, and shadow AI operating in production environments.

The need for enhanced runtime protection is driven by the evolving nature of AI systems. Unlike traditional applications, AI models dynamically select tools, access data, and adapt behavior in real time. This creates a non deterministic attack surface that cannot be fully secured through conventional static analysis or pre deployment controls. With the rise of development approaches such as vibe coding and tools like Claude Code, organizations are accelerating AI deployment, further increasing exposure to runtime risks.

Miggo’s Runtime Defense Platform addresses this gap by focusing on execution level security rather than static inputs. The company’s DeepTracing technology continuously analyzes how AI agents behave in production, mapping their actions, access patterns, and interactions with data and systems. This enables security teams to detect and respond to threats as they occur, rather than after an incident.

“AI risk materializes at runtime,” said Daniel Shechter, CEO of Miggo Security. “For teams using popular agent frameworks, like LangChain, and MCP-connected toolchains, this architecture makes runtime execution the primary attack surface. I’m proud of the technology we’ve built at Miggo, which has always been centered around deep context – and by extending our patented DeepTracing capabilities, we’re now bringing robust AI and agentic defense directly into modern environments.”

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The platform introduces an AI Bill of Materials that automatically discovers AI components across applications and agent environments, providing a clear view of models, tools, and data access paths. Behavioral drift detection establishes a baseline for agent activity and flags deviations, while runtime guardrails allow teams to enforce policies around approved models, tools, and permissions.

Additional capabilities include execution level detection that tracks tool usage, system actions, and network behavior to identify potential compromise paths. The platform also offers monitoring tailored to Model Context Protocol based toolchains, helping detect abnormal access patterns and risky execution chains.

Miggo’s approach extends beyond detection to provide actionable insights. By correlating events into unified timelines and assigning risk scores based on factors such as data exposure and system access, the platform helps security teams prioritize incidents and accelerate response. It also supports compliance efforts by generating runtime evidence aligned with emerging regulations such as the EU AI Act.

The expansion follows Miggo’s recent research into indirect prompt injection risks within Google Gemini integrations, highlighting how trusted inputs can influence downstream AI behavior. This underscores the importance of focusing on runtime execution rather than relying solely on input validation.

With this update, Miggo Security is reinforcing the importance of runtime defense in modern AI security strategies. As agentic systems continue to evolve, the ability to monitor, understand, and control AI behavior in real time will be essential for protecting enterprise environments from increasingly sophisticated threats.

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