Use cases

Runtime AI Security Use Cases in Real Systems

These scenarios require enforcement at the moment AI decisions are made.

These use cases share a common requirement: AI decisions must be enforced at runtime, not audited after the fact.

Internal AI copilots (Employee-facing assistants)

Copilots used by engineers, analysts, and operators often have broad access to internal data and tools.

Context: Employee queries internal systems
Risk: Overbroad data exposure or misuse
Failure mode: Static controls miss intent
Runtime requirement: Decisions must be enforced per request
Use-case 1: Internal AI copilot dashboard

Ops and finance agents (Tool-enabled AI workflows)

Agents with access to infrastructure, cost, or financial APIs can trigger high-impact actions.

Context: AI invokes operational tools
Risk: Unauthorized or unsafe actions
Failure mode: Prompt-level controls insufficient
Runtime requirement: Tool calls must be policy-gated
Use-case 2: Ops and finance agents policy creation UI

Customer support AI (Externally influenced systems)

Support agents interact with untrusted user input while accessing internal systems.

Context: External user drives AI behavior
Risk: Data leakage or role escalation
Failure mode: Training-time guardrails fail
Runtime requirement: Contextual enforcement required
Use-case 3: Customer support AI policy creation UI

Common risk patterns across deployments

Unauthorized data access

AI accessing data beyond user or role intent

Improper tool invocation

Unsafe or unintended API calls

Behavioral drift

Gradual deviation from expected usage

Policy circumvention

Attempts to bypass enforced constraints