AI Safety & Policy Enforcement Playbook
Evaluates AI outputs for safety violations, restricted content, and policy non-compliance. Ensures AI systems adhere to governance and regulatory standards.
Start building
Deploy this kit stack into your workspace. Customize bricks, scoring, and outputs to match your team.
Without this playbook
Most teams handle ai safety & policy enforcement through scattered call reviews, manager opinion, and isolated examples. Without a shared operational definition, the signals stay inconsistent and difficult to act on across volume.
With this playbook
A shared, repeatable lens for ai safety & policy enforcement - with structured outputs you can route into coaching, reporting, and workflow automation. Every conversation produces evidence, not just opinions.
Built for
AI product managers, ML engineers, and trust & safety teams
When teams use it
- Model evaluation and release gates
- Governance review and policy enforcement
- Safety and accuracy monitoring
The operational stack
1 kit behind this playbook
AI safety is not a single checkbox - it spans content safety, policy compliance, and sensitive topic handling, each with different failure modes and different stakeholders. This stack evaluates all three: whether outputs contain unsafe or restricted content, whether they comply with organisational policies and regulatory requirements, and whether sensitive topics are handled appropriately. Governance teams get structured evidence for each dimension rather than a single pass/fail that obscures where the system is actually failing.
Content Policy Compliance Kit
3 bricks
Checks AI output against policy requirements.
Included bricks
Customize this kitPolicy Violation Present
BooleanDetects language that violates defined content policies
Compliance Category Type
CategoryClassifies type of policy violation
Severity Score
ScoreScores severity of policy compliance issues
Knowledge base
Supporting materials
The kits in this playbook work best when backed by reference materials that ground the evaluation. Upload these into your workspace knowledge base to improve accuracy and relevance.
Learn more about Knowledge BasesAI safety policies and restricted content definitions
Organisational AI governance framework
Regulatory requirements for AI outputs in your industry
Sensitive topic handling guidelines and escalation procedures
AI content review rubrics and policy compliance checklists
Structured output
What you get back
Every conversation processed through this stack produces a structured JSON object. Each brick contributes a typed field - booleans, scores, categories, or string lists - that you can route, aggregate, and report on.
Example output shape
{
"policy_violation_present": true,
"compliance_category_type": "Strong",
"severity_score": 7
}In practice
How teams use these outputs
The structured outputs from this stack integrate into your existing workflows. Use them wherever you need repeatable, evidence-based signal from conversations.
Model evaluation and release gates
Governance review and policy enforcement
Safety and accuracy monitoring
AI agent performance benchmarking
Get started
Deploy this playbook in your workspace
Customizing creates a workspace-owned draft with this playbook's full kit stack. Adjust bricks, scoring, and outputs to fit your team, then publish when ready.