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Agents reason and act at runtime in ways no static scan can anticipate.

Traditional security checks can't predict how a non-deterministic agent will behave once it hits production.

I condensed the fix into a visual field guide — swipe through below.

Most people think a standard security scan is enough for AI. What actually happens is a predictability gap where the agent ignores its instructions or leaks data because the risk only appears during the interaction.

The solution is a two-part framework:

Shift-Left focuses on the build. It uses primitive scanning to find misconfigurations in the agent's building blocks and generates an AIBOM. While a standard SBOM tracks code libraries, an AIBOM tracks reasoning dependencies like specific LLM versions and third-party plugins.

Shield-Right focuses on the runtime. It uses an AI Firewall to filter out prompt injections and adversarial inputs before they reach the model.

For an ops leader, this turns a chaotic deployment into a governed process. Instead of manually auditing every agent, you use the AIBOM to instantly identify which agents are using a deprecated or vulnerable model version.

The full breakdown — including the Shift-Left pipeline diagram — is in the 12-page guide below.

How are you currently tracking which model versions and plugins are active across your different AI agents?

#YourBrand #LLMOps #AIAutomation #BusinessAnalysis #WorkflowAutomation