Governance Engine
Enforce runtime policies, detect risk, and control AI behavior across production workflows.
The Governance Engine is where AITracer moves beyond observability into active control.
Traditional monitoring platforms show teams what happened after execution.
The Governance Engine evaluates prompts, responses, tool calls, and workflow behavior as execution occurs—helping teams identify risk before it spreads across systems, users, or downstream workflows.
Teams use the Governance Engine to answer questions such as:
- Did this execution expose sensitive data?
- Did a workflow exceed cost thresholds?
- Did a model trigger restricted actions?
- Did an agent access systems it shouldn’t?
- Should this execution be escalated for review?
Governance workflow
Runtime Policy Enforcement
Apply governance rules while AI systems are actively running.
This includes:
- prompt restrictions
- output restrictions
- tool usage controls
- role-based permissions
- workflow execution constraints
Sensitive Data Detection
Identify risky content before it moves deeper into production systems.
Detect:
- PII
- API keys
- payment information
- medical data
- confidential business information
Cost Governance
Prevent runaway usage and unexpected operational spend.
Track:
- per-request cost thresholds
- model overuse
- inefficient routing
- abnormal token spikes
Risk Escalation
High-risk executions can automatically trigger review workflows.
Examples include:
- manual approvals
- security reviews
- compliance escalations
- incident investigations
Policy Decision Records
Every governance action is stored for future review.
This includes:
- triggered policy
- severity level
- execution timestamp
- affected workflow
- remediation actions
Why This Matters
Most organizations can observe AI failures after they happen.
Very few can stop risky behavior while systems are actively running.
The Governance Engine helps teams move from passive monitoring to enforceable operational control across production AI systems.