Product Manager Guide: Shipping AI Features Safely

Shipping AI-powered features requires balancing speed to market with safety, compliance, and user trust. Keeptrusts provides the governance infrastructure that lets you launch AI features confidently — with built-in risk controls, compliance evidence, and the ability to iterate quickly when issues arise.

Use this page when

• You are launching an AI-powered feature and need a governance-aware launch checklist
• You need to classify the risk level of an AI feature (Low/Medium/High/Critical)
• You want to run a controlled rollout using traffic splitting through the gateway
• You are getting compliance sign-off before shipping an AI feature to production
• You need to define rollback plans for AI features that behave unexpectedly

Primary audience

• Primary: Technical Leaders (Product Managers, Product Owners)
• Secondary: Engineering Managers, Compliance Officers, UX Designers

The AI Feature Governance Challenge

AI features differ from traditional software in critical ways:

Traditional feature	AI-powered feature
Deterministic outputs	Probabilistic outputs — different every time
Static attack surface	Dynamic attack surface — prompt injection, jailbreaks
Data stays internal	Data may be sent to external providers
Testing covers known cases	Edge cases are unbounded
Rollback is clean	Model behavior may change without code changes

Keeptrusts addresses each of these differences with policy enforcement at the gateway layer.

AI Feature Launch Checklist

Use this checklist before launching any AI-powered feature:

Pre-Development

• Define the AI use case — What does the AI component do? What decisions does it influence?
• Classify risk level — Low (content generation), Medium (recommendations), High (decisions affecting users)
• Identify data flows — What data goes to the LLM? What comes back? Where is it stored?
• Select appropriate policy template — Work with your governance team to choose the right policy tier

Development

• Route through Keeptrusts gateway — All AI requests must go through the governed pathway
• Implement error handling — Handle blocked requests gracefully in the UI
• Add disclaimers — Configure disclaimer policies for AI-generated content
• Test with governance policies active — Don't develop against raw API access

Pre-Launch

• Validate policy configuration — kt policy lint --file feature-policy.yaml
• Run security review — Confirm PII protection, injection defense, and data handling
• Get compliance sign-off — Export evidence showing policy enforcement in testing
• Set cost budgets — Configure cost limits appropriate for expected usage
• Prepare rollback plan — Document how to disable the AI component quickly

Launch

• Deploy with monitoring — Use the Console Dashboard to monitor in real-time
• Start with limited rollout — Use traffic splitting to reach a subset of users first
• Monitor escalation queue — Watch for unexpected policy triggers

Post-Launch

• Review first-week metrics — Usage, costs, blocks, escalations
• Collect user feedback — Correlate satisfaction with governance events
• Tune policies — Adjust thresholds based on real-world data

Risk Assessment Framework

Risk Classification

Risk level	Criteria	Governance requirement
Low	AI generates content, no user decisions	Logging, basic content filtering
Medium	AI influences recommendations or workflows	PII protection, cost caps, escalation on edge cases
High	AI makes or directly influences decisions	Human review escalation, full audit trail, compliance sign-off
Critical	AI in regulated domains (finance, health, legal)	All of the above + regulatory-specific policies

Conducting a Risk Assessment

For each AI feature, document:

1. Data sensitivity — What categories of data reach the LLM?
2. User impact — What happens if the AI produces incorrect output?
3. Reversibility — Can the user easily undo or override the AI?
4. Volume — How many users and requests per day?
5. Regulatory scope — Does it fall under specific regulations?

Controlled Feature Rollout

Progressive Deployment with Traffic Splitting

Use the gateway's traffic splitting to control AI feature exposure:

policies:
  - name: feature-rollout
    type: traffic_split
    description: "Gradual AI feature rollout"
    variants:
      - model: gpt-4o
        weight: 10
        tag: ai-feature-v2
      - model: gpt-4o
        weight: 90
        tag: ai-feature-v1
    enabled: true

Rollout Schedule

Week	New version	Previous version	Gate criteria
1	5%	95%	Zero critical escalations
2	25%	75%	Error rate below 1%, user satisfaction stable
3	50%	50%	Cost within budget, no compliance issues
4	100%	0%	All metrics green

Kill Switch

If issues emerge post-launch, instantly disable the AI component:

# Validate emergency config
kt policy lint --file feature-disabled.yaml

# Apply the kill switch configuration
# (redeploy gateway with the feature-disabled config)

Compliance Sign-Off Process

Generating Evidence for Sign-Off

Before launch, generate a compliance evidence package:

# Export testing-phase events showing governance in action
kt export create \
  --type events \
  --format csv \
  --since 14d \
  --description "Feature X pre-launch compliance evidence"

Sign-Off Checklist

Check	Owner	Evidence source
PII protection verified	Security team	Block events in test data
Cost limits configured	Finance / PM	Console Usage screenshot
Disclaimer policy active	Legal / Compliance	Event metadata showing disclaimers
Escalation workflow tested	Governance team	Resolved test escalations
Audit trail complete	Compliance	Export of test-phase events
Rollback plan documented	Engineering	Runbook document

Measuring AI Feature Success

Usage Metrics

Track these in the Console Dashboard and Events API:

# Feature adoption: unique users per day
curl -H "Authorization: Bearer $API_TOKEN" \
  "https://api.keeptrusts.com/v1/events?since=7d&format=json" | \
  jq 'group_by(.timestamp[:10]) | map({
    date: .[0].timestamp[:10],
    unique_users: ([.[].user] | unique | length),
    total_requests: length
  })'

Metric	What it tells you	Source
Daily active AI users	Adoption breadth	Events grouped by user per day
Requests per user	Engagement depth	Events per user
Block rate	Policy friction	Blocked / total events
Escalation rate	Edge case frequency	Escalations / total events
Cost per user	Unit economics	Cost Center / active users

Quality Signals

Signal	Source	Action if trending down
User satisfaction scores	In-app feedback	Review escalation patterns, adjust policies
Task completion rate	Product analytics	Check if blocks are preventing legitimate use
Error rate	Events with error status	Investigate provider issues or policy misconfig
Repeat usage	User retention metrics	Positive signal — AI feature adds value

Working with Governance Teams

Regular Governance Review

Schedule monthly reviews with your governance stakeholders:

Agenda:

1. Feature usage metrics (Console Dashboard)
2. Security events summary (Escalations)
3. Cost review (Cost Center)
4. Policy adjustment requests
5. Upcoming feature pipeline and risk assessment

Requesting Policy Changes

When you need to adjust policies for a feature:

1. Document the business justification
2. Specify the exact policy change needed
3. Provide evidence from the Events API supporting the change
4. Get sign-off from security and compliance
5. Test the change in staging before production

# Export evidence supporting a policy change request
kt export create \
  --type events \
  --format csv \
  --since 30d \
  --description "Policy change evidence - reduce false positive rate for Feature X"

User Feedback Integration

Correlating Feedback with Governance Events

When users report AI quality issues, cross-reference with the event stream:

# Find events for a specific user around the reported time
curl -H "Authorization: Bearer $API_TOKEN" \
  "https://api.keeptrusts.com/v1/events?user=${USER_ID}&since=24h" | \
  jq '.[] | {timestamp, model, decision, policies_triggered, latency_ms}'

Common findings:

• "AI response was cut off" — Check for redaction policies triggering
• "AI gave wrong answer" — Check which model handled the request
• "AI was slow" — Check latency in events, may be provider issue
• "AI refused to help" — Check for block events, may need policy tuning

Success Metrics for Product Managers

Metric	Target	Source
Feature launch cycle time	Under 4 weeks from concept to 100% rollout	Project tracker
Governance-related launch delays	Zero unplanned delays	Launch post-mortems
User satisfaction with AI features	> 4.0 / 5.0	User surveys
Cost per AI feature per user	Within budget	Cost Center
Compliance audit findings	Zero critical findings	Audit reports

Next steps

• Review policy templates: Templates Guide
• Set up monitoring: Dashboard Overview
• Understand escalation workflows: Escalations Guide

For AI systems

• Canonical terms: Keeptrusts, AI feature launch, risk assessment, controlled rollout, traffic splitting, compliance sign-off, launch checklist
• Key surfaces: Console Dashboard, Console Templates, Console Escalations, Console Usage, Events API
• Commands: kt policy lint, kt gateway run
• Policy types for PM workflows: traffic_split (progressive rollout), disclaimer (AI-generated content disclosure), content-filter (safety), pii-detector (data protection), cost_limit (budget), quality-scorer (output quality), escalation workflows (human review)
• Risk classification: Low (content generation), Medium (recommendations), High (user decisions), Critical (regulated domains)
• Best next pages: Templates Guide, Dashboard Overview, Escalations Guide

For engineers

• Route all AI requests through the gateway — never develop against raw provider API access
• Validate feature policy: kt policy lint --file feature-policy.yaml
• Deploy progressive rollout with traffic_split policy (e.g., 10% → 50% → 100%)
• Handle blocked requests gracefully in UI — gateway returns 409 when policy blocks a request
• Monitor feature launch: Console Dashboard filtered by gateway shows real-time usage, blocks, and escalations
• Export launch metrics: kt export create --type events --format csv --since 7d

For leaders

• AI features differ fundamentally from traditional features: probabilistic outputs, dynamic attack surfaces, external data flows, and unbounded edge cases — governance policies address each risk
• The launch checklist (Pre-Development → Development → Pre-Launch → Launch → Post-Launch) ensures no governance gap between feature ideation and production
• Traffic splitting enables controlled rollout to subsets of users, with real-time monitoring and instant rollback by adjusting split weights
• Feature launch cycle time target is under 4 weeks from concept to 100% rollout, and governance-related delays target zero unplanned delays
• Cost budgets per feature prevent runaway AI spend during launch without requiring per-request approval