Platform / RiGi Group
AI Guardrails
Apply runtime checks around sensitive data, unsafe requests, and output constraints.
WWAIOS / AI Guardrails
The role of ai guardrails in a governed workflow
Apply runtime checks around sensitive data, unsafe requests, and output constraints.
03 / 09
Capabilities
What the layer is designed to handle
Validate format and policy conditions before effects occur.
Test guardrails against adversarial and ordinary cases.
Operating pattern
A control path you can inspect
- 01Define the input, context, and authorized purpose.
- 02Detect and handle sensitive inputs and outputs.
- 03Validate format and policy conditions before effects occur.
- 04Capture the result, exception, and owner for review.
Questions for architecture review
What would change your decision?
Ask which evidence is current, which control actually executes, who owns an exception, and where a human must approve an action.
Explore
Adjacent layers
AI HarnessBind model interactions into a defined runtime with shared controls and observable behavior.↗AI Decision EngineDescribe decision paths, policy conditions, and approval points as explicit workflow rules.↗AI Knowledge BaseConnect retrieval to governed sources so answers can point back to what supports them.↗AI GovernanceManage policies, model inventory, ownership, and review throughout the AI lifecycle.↗
A closer look / AI Guardrails
From question to evidence to decision.
Use this framework to discuss the actual workflow and the proof needed to move forward.
01 / Situation
Sensitive information, prompt injection, and unsafe output can enter from either user input or retrieved material.
02 / Approach
Apply context-specific input checks, output validation, redaction rules, and pre-effect denials at known boundaries.
03 / Evidence
Run ordinary and hostile examples, including false positives, bypass attempts, and changed policy versions.
04 / Next decision
Investigate misses and overblocking before treating a guardrail as reliable for the use case.