This topic covers the emerging class of AI security and model‑protection platforms that combine visibility, governance, and incident‑response capabilities for agentic systems and blockchain‑connected workflows. Interest in enterprise model‑security suites has grown as organizations deploy multi‑agent orchestrations, fine‑tuning pipelines, and on‑chain integrations that expand the attack surface for model theft, prompt injection, supply‑chain poisoning, and DeFi exploits. Key tool categories include AI Security Governance (visibility, runtime policy enforcement, provenance), AI Governance Tools (SDLC controls, model risk assessments, assistant orchestration), and Post‑Quantum Blockchain Tools (signature/key management, post‑quantum upgrade paths for on‑chain incident response). Representative platforms from the stack illustrate these needs: Xilos focuses on visibility into agentic AI activity; IBM watsonx Assistant enables enterprise virtual agents and multi‑agent automation that require policy controls; Together AI provides full‑stack training and serverless inference that surface provenance and integrity requirements; Qodo (formerly Codium) brings code‑level quality and SDLC governance to model and application code; GitHub Copilot highlights developer‑facing assistants that can accelerate delivery but also introduce vulnerabilities needing governance. As of 2026, the landscape is shaped by broader production deployment of agentic systems, regulatory scrutiny of model risk, and the continued frequency of DeFi incidents that demand rapid, auditable response. Practical protection emphasizes observability, immutable provenance, automated governance in CI/CD and inference, and alignment with post‑quantum upgrade strategies for blockchain components. Organizations should evaluate integrated suites that span developer tooling, model lifecycle controls, runtime monitoring, and on‑chain incident orchestration rather than isolated point solutions.