AI identity governance and security platforms focus on controlling who — or what — can act on behalf of an organization, and capturing auditable evidence of those actions. As enterprises deploy assistants (Microsoft 365 Copilot, IBM watsonx Assistant, Anthropic’s Claude family), agent builders (Lindy), vertical solutions (Monitaur for insurance), and task‑specific tools (Observe.AI, Pria AI), the identity surface expands beyond human users to ephemeral agents, service accounts and third‑party models. Specialized platforms such as Opal Security and Offroad (and similar ID/Gov tools) provide credential lifecycle management, fine‑grained access policies, token brokering, vendor-model mapping, runtime monitoring and immutable audit trails needed for compliance and risk management. This topic is timely in mid‑2026 because widespread model and agent adoption—plus tighter regulatory expectations (data residency, explainability, and supplier risk) and more frequent security incidents—have made access control, vendor governance, and observability core operational requirements. Key patterns include short‑lived identities for agents, brokered secrets to avoid hardcoded credentials, policy-driven access tied to roles and use cases, and continuous validation of model behavior and inputs. Tools discussed serve complementary roles: platform-native assistants (Copilot, watsonx, Claude, Mistral) provide productivity and inference capabilities; vertical governance platforms (Monitaur) centralize policy and vendor risk for regulated industries; agent builders (Lindy) accelerate autonomous workflows that then require governance; and conversation/voice platforms (Observe.AI) and sales automation (Pria AI) illustrate surfaces that need identity controls. For security, compliance, and audit teams, the practical focus is integrating identity governance into model supply chains, enforcing least‑privilege for agents, and producing verifiable logs that can support audits and incident response.