AI identity, access and security platforms focus on authenticating and authorizing machine identities, controlling agentic AI behavior, and preventing model and data exfiltration across complex enterprise environments. By mid‑2026 the topic is urgent because organizations are deploying multi‑agent orchestrations, integrating AI assistants into core productivity stacks, and hosting models and embeddings across cloud platforms—expanding attack surface and regulatory scrutiny. Key categories include AI‑native identity and access providers (examples: Opal, Offroad), hardware‑backed credentialing (e.g., Tangem), runtime observability and agent management (Xilos’ stated visibility into connected services and agentic activity), and governance platforms that centralize policy, monitoring and vendor controls (Monitaur’s focus on insurance and regulated industries). Cloud model platforms (Vertex AI, Cohere) and enterprise assistants (IBM watsonx Assistant, Microsoft 365 Copilot) increase the need for fine‑grained access controls, entitlements, and audit trails, while no‑code app marketplaces (Anakin.ai) multiply integration points needing least‑privilege enforcement. Practical priorities are cryptographic machine identity, attestation of agent behavior, real‑time observability of service-to-service calls, centralized policy enforcement, and vendor/supply‑chain governance. Buyers should evaluate interoperability with existing IAM, support for hardware keys and short‑lived credentials, audit and compliance reporting, and controls to limit model/data exposure. The field sits at the intersection of cloud IAM, hardware security, and AI governance—shifting focus from human user authentication to scalable, verifiable machine identities and continuous runtime control to reduce operational and regulatory risk.