Governance Engine
Enforce runtime policies, detect risk, and control AI behavior across production workflows.
Note: This is an advanced platform feature. For local agent development, start with Agent Lab and Quick Start.
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.
Built-in detection covers:
- PII (credit card patterns, SSN)
- Medical/PHI context (diagnosis, patient, prescription, HIPAA)
- Workspace-specific custom regex patterns
Custom compliance rules
You can add your own detection patterns in Settings → Compliance. Each rule includes:
- A regex pattern
- A severity level (info, low, medium, high)
- A category label
- An enable/disable toggle
Rules are evaluated against the combined prompt and response text of every trace at ingest time.
False positive reporting
If a compliance rule triggers on content that is not actually sensitive, you can report it as a false positive. The matched term is automatically added to a noise filter, suppressing similar matches in future traces. This helps reduce alert noise over time as your team tunes the compliance engine.
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.