AI security becomes harder when models are no longer isolated chat systems, but part of pipelines that inspect code, analyze incidents, call tools, access sensitive data, and recommend or execute actions. In sovereign environments, security must extend across the entire path: model, agent, data, tools, infrastructure, and audit trail.

This session presents a working architecture for a private AI security ecosystem using specialized open models such as Antares for lightweight terminal and code investigation, Cisco Foundation-Sec for security reasoning, and security skills for vulnerability analysis, policy checks, and adversarial evaluation.

The pipeline combines heterogeneous private inference with deterministic orchestration, signed agent and tool registries, least-privilege authorization, PII and secret redaction, sandboxed execution, policy-as-code, human approval gates, and auditable action records.

A live workflow demonstrates how an incident can move from detection to investigation, code and vulnerability analysis, security validation, and controlled remediation without exposing sensitive operational data to external AI services.

Attendees will learn how to build complex sovereign AI-security pipelines where probabilistic models can reason freely, but execution remains bounded by deterministic security controls, privacy policies, and human authority.