The Professional Standard

We have built the pipelines, applied the guardrails, and integrated the humans. But in a large organization, security and safety are not just about "settings." They are about Standard Operating Procedures (SOPs). What happens when a model starts generating harmful content at 2 AM? Who is responsible? How is it documented?

In the final lesson of Domain 3, we focus on the Governance Framework. This is the glue that connects your technical implementation to the legal and ethical requirements of your business.

1. Incident Response for AI (The "Red Button")

In traditional software, an incident is a "Server Down." In AI, an incident is a "Safety Breach" or a "Severe Hallucination."

The AI Incident Response SOP:

Detection: Guardrails or CloudWatch Alarms trigger an alert.
Containment: Immediately "Kill" the specific agent session or rotate the API key to stop the AI's output.
Investigation: Use Bedrock Traces and S3 Logs to identify the prompt that caused the breach.
Remediation: Update the Guardrail "Denied Topics" or the System Prompt.
Post-Mortem: Document the event and the fix.

2. Model Cards and Documentation

A Model Card is a standardized document that describes a model's capabilities, limitations, and intended use cases.

For the professional exam, you should know that AWS provides these for Amazon-built models (Titan). For your own custom systems, you should create a "System Card" that explains:

Which datasets were used in the RAG Knowledge Base.
What safety filters are active.
Known limitations (e.g., "This bot cannot accurately solve complex differential equations").

3. Aligning with Global Frameworks

You don't have to reinvent the wheel. You should map your AI practices to established frameworks like:

NIST AI Risk Management Framework (RMF): A standard for managing AI risks.
ISO/IEC 42001: The international standard for AI Management Systems.
The EU AI Act: The world's first comprehensive legal framework for AI.

The Professional Action: Use AWS Audit Manager to automatically map your AWS configurations (like Guardrails and IAM) to these frameworks.

4. SOP for Model Updates

New models are released almost every month. You need an SOP for upgrading:

Evaluation: Test the new model using Amazon Bedrock Model Evaluation.
Comparison: Verify that the new model's latency and cost are better/worse.
Gradual Rollout: Use a Blue/Green deployment (Domain 2) to move traffic.
Rollback Plan: Always keep the old model ARN in your code as a variable in case the new model behaves unexpectedly.

5. Visualizing the Governance Lifecycle

graph LR
    P[Policy Design] --> I[Implementation: Guardrails/IAM]
    I --> M[Monitoring: CloudWatch]
    M --> A[Audit: Audit Manager]
    A --> R[Refinement: SOP Update]
    R --> P

The Continuous Governance Cycle.

6. Pro-Tip: The "Transparency" Header

A professional SOP should require that all AI-generated content is clearly labeled.

Use a Bedrock Guardrail to prepend "Generated by AI" to all responses.
In your UI, include a "Report Inappropriate Response" button that automatically captures the Trace ID for your developers to investigate.

Knowledge Check: Test Your SOP Knowledge

?Knowledge Check

An enterprise wants to formalize its 'Incident Response' plan for Generative AI. What is the most important technical action a developer can implement to ensure security teams can investigate a safety breach that occurred in the past?

Summary

Governance is the "Glue" of the AI stack. By formalizing your SOPs, Documentation, and Incident Response, you move from "Experimental AI" to "Enterprise-Grade AI."

This concludes Domain 3: AI Safety, Security, and Governance. You have mastered the most critical part of the exam for trust-sensitive industries.

Coming up next: Domain 4: Performance, Optimization, and Evaluation. We look at how to make your models faster, smarter, and cheaper.

Next Module: The Art of Steering: Advanced Prompt Engineering

The Foundation of Trust: Governance Frameworks and SOPs