This topic covers tools and practices that give enterprises control over AI identities, access rights, and the inputs that drive large language models and agentic assistants. As organizations deploy agentic platforms, embedded copilots and domain‑specific assistants across workflows, the attack surface expands beyond traditional IAM: models, agents, and prompt channels become vectors for data exfiltration, prompt injection, and unauthorized actions. Relevant solutions range from AI‑aware identity and access management (IAM) to prompt‑defense and runtime policy enforcement. Examples include identity-focused platforms (e.g., Opti) that issue and attest machine/agent identities and manage fine‑grained model access; and prompt‑defense tools (e.g., BrowseSafe) that sanitize and fence prompt inputs, block malicious instructions, and enforce data‑handling policies at inference time. These tools sit alongside enterprise AI platforms and assistants—Yellow.ai and Cimba.AI for agentic automation, Microsoft 365 Copilot and Claude for productivity assistants, Harvey for legal workflows, Kay AI for insurance operations, IBM watsonx Assistant for virtual agents, Anakin.ai for no‑code app automation, and Cohere for private LLM infrastructure—each introducing different governance needs. Key capabilities enterprises look for include per‑agent authentication and attestation, model‑scoped tokens, role‑ and attribute‑based access controls, prompt sanitization and intent validation, retrieval and RAG controls, audit trails, and SIEM/EDR integration. The topic is timely because broad adoption of multi‑vendor LLM stacks and agentic automation in 2025 increases regulatory scrutiny and operational risk, making consistent, AI‑aware identity and prompt‑defense essential to secure AI-driven workflows.