AWS Learning Material Certification Cheatsheets
AIF-C01 Domain 5 14% domain

Task 5.2: Recognize governance and compliance regulations for AI systems

Security, Compliance, and Governance for AI Solutions · 6,538 words · source: Certified-AI-Practitioner-AIF-C01/domain-5/task-5-2-cheat-sheet.md

Domain 5: Security, Compliance, and Governance for AI Solutions

Task Statement 5.2: Recognize governance and compliance regulations for AI systems

Domain 5 is "Security, Compliance, and Governance for AI Solutions" and represents 14% of the scored AIF-C01 exam content. Task 5.2 focuses on governance and compliance for AI systems: AWS services that support audits and regulation compliance, data governance strategies, logging, residency, monitoring, observability, retention, policies, review cadences, governance frameworks, transparency standards, and team training.

Use this sheet for questions such as "which AWS service helps prove compliance?", "how do you collect audit evidence?", "how should AI training data be governed?", "which service records API activity?", "how do you manage data retention?", "what changes when you fine-tune a model with company data?", and "how should an organization follow an AI governance protocol?"

Official Study Path

Use these in order:

  1. AWS Skill Builder: AWS Artificial Intelligence Practitioner Learning Plan
  2. AWS Skill Builder: Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01)
  3. AWS Skill Builder: Domain 5 Review: AWS Certified AI Practitioner
  4. AWS Skill Builder: Domain 5 Practice: AWS Certified AI Practitioner
  5. AWS Exam Guide: Content Domain 5, Task Statement 5.2
  6. AWS Docs: AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor, AWS Glue, AWS Glue Data Catalog, AWS Glue Data Quality, AWS Glue DataBrew, AWS Lake Formation, Amazon S3 lifecycle management, and AWS Generative AI Security Scoping Matrix
  7. Local Skill Builder transcripts: Task Statement 5.2, Lessons 1 through 6

Exam Objective Map

You should be able to answer questions about:

Objective What to know for the exam
AWS governance and compliance services AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor
Audit evidence and reports Artifact provides AWS compliance reports; Audit Manager collects customer-side evidence and produces assessment reports
Data governance strategies Data lifecycle, logging, residency, monitoring, observability, retention, curation, discovery, understanding, protection
Data governance roles Data owner, data steward, IT, security, legal, compliance, model owner, business owner
AI standards and regulations ISO/IEC 42001, ISO/IEC 23894, EU AI Act, NIST AI RMF, Algorithmic Accountability Act concepts
Risk management Identify use case and stakeholders, estimate likelihood and impact, distinguish inherent and residual risk, document overall system risk
Governance protocols Policies, review cadence, review strategies, transparency standards, employee training, governance frameworks
Generative AI scope AWS Generative AI Security Scoping Matrix: responsibility increases as you move from consuming third-party apps to building and training your own models

One-Minute Mental Model

Governance is the system for deciding who is accountable, what policies apply, how AI systems are reviewed, and how risks are monitored. Compliance is proof that the system follows required laws, standards, contracts, and internal policies.

Governance = people + policies + processes + controls + review.
Compliance = evidence that required controls are working.
Audit = independent review of that evidence.

Exam shortcut:

Artifact = AWS compliance reports.
Audit Manager = customer audit evidence and assessment reports.
CloudTrail = who did what, where, and when.
Config = resource configuration history and compliance rules.
Inspector = vulnerability findings for workloads and code.
Trusted Advisor = best-practice recommendations.
S3 lifecycle = retention, archival, deletion.
Lake Formation = fine-grained data lake governance.
Glue Data Catalog = metadata catalog.
Glue Data Quality = data quality rules and anomaly detection.

1. Governance vs Compliance vs Audit

These terms appear together, but they are not identical.

Term Exam meaning AI example
Governance How an organization directs and controls AI use AI use policy, model approval board, data owner sign-off, review cadence
Compliance Meeting external or internal requirements SOC 2 controls, GDPR requirements, EU AI Act obligations, internal privacy policy
Audit Review of controls and evidence Auditor asks for access logs, data retention evidence, model risk documentation
Control A safeguard or process used to reduce risk Encryption, least privilege, access reviews, bias testing, output monitoring
Evidence Proof that a control exists or operated CloudTrail logs, Config compliance result, training record, policy document
Framework Structured set of controls or practices SOC 2, ISO 27001, ISO/IEC 42001, NIST AI RMF, AWS Audit Manager framework
Policy Organization rule "No customer PII may be used for fine-tuning without privacy approval"
Procedure Repeatable steps to satisfy a policy Review data classification, approve access, log sign-off, retain evidence

Exam pattern:

Need proof for an auditor -> evidence and reports.
Need rules for AI use -> governance policy.
Need verify AWS service compliance posture -> AWS Artifact.
Need verify your AWS resource configuration -> AWS Config.

2. AWS Governance and Compliance Service Chooser

Need Best AWS answer Why
Download AWS compliance reports and third-party attestations AWS Artifact Provides on-demand access to AWS and ISV security and compliance reports, certifications, and agreements
Collect evidence for customer audits AWS Audit Manager Maps frameworks to AWS data sources, collects evidence, and generates assessment reports
Use prebuilt compliance frameworks AWS Audit Manager Includes prebuilt frameworks for standards and regulations; can also use custom frameworks
Track resource configuration changes AWS Config Records configuration history and relationships for supported resources
Evaluate resources against compliance rules AWS Config rules Checks whether resources comply with desired configuration rules
Deploy compliance rules at scale AWS Config conformance packs Packages Config rules and remediation actions for accounts, Regions, or organizations
Automatically remediate a noncompliant resource configuration AWS Config with Systems Manager Automation Config can trigger remediation actions when rules fail
Record account activity and API calls AWS CloudTrail Records user activity and API calls as events
Answer "who did what, where, and when?" AWS CloudTrail Core audit trail service for governance, compliance, security monitoring, and troubleshooting
Store long-term API activity logs CloudTrail trails to Amazon S3 Trails deliver events to S3 and optionally CloudWatch Logs/EventBridge
Find software vulnerabilities and unintended network exposure Amazon Inspector Continuously scans supported workloads and code repositories and prioritizes findings
Get best-practice recommendations AWS Trusted Advisor Checks cost, performance, resilience, security, operational excellence, and service limits
Govern data lake access AWS Lake Formation Fine-grained permissions for data in S3 using AWS Glue Data Catalog metadata
Catalog data assets and schemas AWS Glue Data Catalog Metadata store for data locations, schemas, data types, and table definitions
Check data quality rules AWS Glue Data Quality Evaluates data quality rules and can use ML to detect anomalies
Profile, clean, and prepare data visually AWS Glue DataBrew Data profiling, cleaning, normalization, and lineage-style preparation views
Manage retention, archival, and deletion Amazon S3 lifecycle rules Transitions or expires objects based on time and storage class policies
Discover sensitive data in S3 Amazon Macie Finds sensitive data such as PII; useful before training, fine-tuning, or RAG
Filter GenAI inputs and outputs Amazon Bedrock Guardrails Supports content filters, denied topics, PII handling, and grounding checks

3. Shared Responsibility for Compliance

AWS Skill Builder emphasizes that the shared responsibility model applies to both security and compliance.

Responsibility area AWS responsibility Customer responsibility
Physical infrastructure Secure data centers, hardware, networking, and facilities Use AWS compliance reports as inherited evidence
Managed services Operate and secure the service infrastructure Configure service access, logging, encryption, data use, and retention
Compliance reports Provide third-party audit reports and attestations for AWS controls Determine which standards apply to the workload and provide customer-side evidence
AI data Provide security and governance features Classify, protect, retain, delete, and monitor data used for AI
AI model and app behavior Provide service controls and documentation Choose models, document risks, add guardrails, monitor outputs, train teams, review policies

Exam Pattern

If the question asks who provides the SOC 2 report for AWS data center controls, answer AWS through AWS Artifact.

If the question asks who proves that the application team configured encryption, access controls, logging, and retention correctly, answer the customer, often using AWS Audit Manager, AWS Config, CloudTrail, and related evidence.

If the question asks why AWS reports reduce audit scope, the answer is inherited controls: AWS has already had its side audited by third parties, so the customer's auditors can focus on the customer's workload and processes.

4. AWS Artifact

AWS Artifact is the exam answer when the organization needs AWS compliance documentation.

Use AWS Artifact when the question says:

  • Download AWS SOC, ISO, PCI, or other compliance reports.
  • Provide AWS third-party auditor reports to internal or external auditors.
  • Understand the compliance and security posture of AWS.
  • Review, accept, or manage select AWS agreements.
  • Assess security and compliance reports for some ISVs on AWS Marketplace.

What AWS Artifact is not:

Not this Use instead
Evidence that your workload is configured correctly AWS Audit Manager, AWS Config, CloudTrail
Vulnerability scanner Amazon Inspector
Runtime monitoring service Amazon CloudWatch, CloudTrail, Security Hub, GuardDuty depending on scenario
Data catalog AWS Glue Data Catalog

Memory hook:

Artifact = AWS gives you AWS-side audit artifacts.

5. AWS Audit Manager

AWS Audit Manager helps continuously audit AWS usage and simplify risk and compliance assessment.

What Audit Manager Does

Capability Exam meaning
Prebuilt frameworks Frameworks map controls to AWS data sources for standards such as SOC 2, PCI DSS, HIPAA, GDPR, NIST, and others
Custom frameworks Create controls and frameworks for internal policies or special audit needs
Automated evidence collection Collects evidence from AWS services such as CloudTrail, AWS Config, Security Hub, and License Manager
Multi-account evidence collection Uses AWS Organizations to collect and consolidate evidence across accounts
Delegation workflow Assigns control sets to responsible team members for review
Evidence finder Helps search and export assessment evidence
Assessment report Auditor-ready package of selected evidence and assessment summary

Audit Manager Exam Pattern

Choose AWS Audit Manager when the question says:

  • "Automate evidence collection for an audit."
  • "Map compliance requirements to AWS usage data."
  • "Generate an assessment report for auditors."
  • "Use a framework to assess controls."
  • "Collect evidence across multiple AWS accounts."
  • "Demonstrate that controls are operating as intended."

Do not confuse:

Audit Manager = collects and organizes audit evidence.
Artifact = provides AWS compliance reports.
Config = evaluates resource configuration compliance.
CloudTrail = records API activity used as evidence.

6. AWS Config

AWS Config is the exam answer for resource configuration history, resource relationships, and compliance evaluation against configuration rules.

What Config Does

Capability Exam meaning
Configuration history Records what a resource configuration looked like over time
Resource relationships Tracks relationships between resources, such as security groups and EC2 instances
Config rules Evaluates resources against built-in or custom rules
Custom rules Use Lambda or policy-as-code logic for organization-specific checks
Conformance packs Package rules and remediation actions into a deployable compliance baseline
Aggregators Centralize configuration and compliance data across accounts and Regions
Remediation Can invoke Systems Manager Automation documents to fix noncompliant resources

AI and ML Compliance Examples

Requirement Config pattern
S3 buckets containing training data must block public access Config rule for S3 public access settings
CloudTrail must be enabled Config rule or conformance pack
SageMaker notebooks must not allow direct internet access Config rule or custom rule
Encryption must be enabled for storage resources Config managed rule or custom rule
AI/ML baseline must be applied across accounts Conformance pack such as operational best practices for AI/ML or SageMaker security best practices

Exam shortcut:

Configuration drift or noncompliant resource setting -> AWS Config.
Compliance evidence report -> Audit Manager.
API activity trail -> CloudTrail.

7. AWS CloudTrail

AWS CloudTrail supports auditing, security monitoring, operational troubleshooting, and compliance by recording user activity and API calls across AWS services.

CloudTrail Event Types

Event type What it captures Example
Management events Control plane actions Create bucket, update IAM policy, invoke Bedrock model configuration API
Data events Data plane actions for supported resources Read or write S3 object, invoke Lambda
Network activity events Actions through VPC endpoints, including denied API calls Private API access attempts
Insights events Unusual API activity or error rates Spike in failed API calls

CloudTrail Exam Pattern

Choose CloudTrail when the question says:

  • "Who changed this configuration?"
  • "Who accessed this API?"
  • "What action happened, from which identity, source IP, and time?"
  • "Provide an audit trail for AI interactions with AWS services."
  • "Store API activity logs in S3 for long-term retention."
  • "Monitor or alert on account activity."

CloudTrail supports audit trails, but be careful:

CloudTrail logs AWS API activity.
Application-level AI chat logs are usually logged by the application to CloudWatch Logs, S3, or another logging system.

Current-doc note:

As of the AWS CloudTrail features page, CloudTrail Lake is no longer open to new customers starting May 31, 2026. For AIF-C01, focus on CloudTrail Event History and trails unless the question specifically asks about querying event data.

8. Amazon Inspector

Amazon Inspector is a vulnerability management service. It continually scans supported workloads and code repositories for software vulnerabilities and unintended network exposure.

What Inspector Scans

Target Exam meaning
Amazon EC2 instances Software vulnerabilities and unintended network exposure
Amazon ECR container images Vulnerabilities in container images
AWS Lambda functions Package vulnerabilities and code-related risks
Code repositories Code security findings where supported

Inspector Exam Pattern

Choose Amazon Inspector when the question says:

  • "Find vulnerabilities in workloads."
  • "Assess containers for CVEs."
  • "Prioritize vulnerability findings by severity or risk."
  • "Detect unintended network exposure."
  • "Continuously scan compute workloads."
  • "Route vulnerability findings to Security Hub or EventBridge."

Do not choose Inspector for:

Need Better answer
Prove compliance with an auditor report Audit Manager
View AWS compliance certificates Artifact
Record API activity CloudTrail
Evaluate resource configuration rules Config
Find sensitive data in S3 Macie

9. AWS Trusted Advisor

AWS Trusted Advisor continuously evaluates an AWS environment against best-practice checks and recommends actions.

Category What it helps with
Cost optimization Unused or underused resources, cost-saving opportunities
Performance Configuration and usage recommendations
Resilience Redundancy and availability checks
Security Security gaps and best-practice deviations
Operational excellence Operational recommendations
Service limits Usage approaching or exceeding quotas

Exam pattern:

Best-practice recommendations across cost, performance, resilience, security, operations, and service limits -> Trusted Advisor.

Trusted Advisor is advisory. It recommends actions. It is not the main source of formal audit evidence, configuration history, or compliance reports.

10. AI Standards and Regulations

The exam does not require you to be a lawyer. It expects you to recognize why governance, risk management, transparency, and compliance evidence matter.

Standard or regulation Exam meaning
ISO/IEC 42001:2023 AI management system standard for establishing, implementing, maintaining, and improving AI governance and management
ISO/IEC 23894:2023 Guidance for managing risk related to AI systems and integrating AI risk management into organizational activities
EU AI Act Risk-based AI regulation; bans unacceptable-risk systems and creates obligations for high-risk and certain transparency-sensitive systems
NIST AI Risk Management Framework Voluntary framework for managing AI risks through Govern, Map, Measure, and Manage functions
Algorithmic Accountability Act concept Proposed US legislation concept focused on impact assessments, transparency, and protection from unfair or unexplained automated decisions
GDPR / CCPA concepts Privacy laws that influence data governance, consent, retention, deletion, and personal data handling
SOC 2 Assurance report covering controls related to security, availability, processing integrity, confidentiality, and privacy
ISO 27001 Information security management standard; relevant to security governance and control management

EU AI Act Risk Pattern

For exam study, know the risk-based idea:

Risk level Practical meaning
Unacceptable risk Prohibited or banned because the use is incompatible with rights or safety
High risk Subject to stricter requirements such as risk management, data governance, documentation, logging, transparency, human oversight, and accuracy/security controls
Limited risk Transparency obligations, such as telling users they are interacting with AI in certain cases
Minimal or no risk Lighter obligations

Skill Builder examples of prohibited or high-risk themes:

  • Social scoring.
  • Scraping facial images to create facial recognition databases.
  • Emotion inference in workplaces or educational institutions.
  • Employment screening tools that rank applicants.

Exam shortcut:

Regulated or high-impact AI decision -> risk assessment + documentation + transparency + monitoring + human oversight + compliance evidence.

11. NIST AI RMF and Risk Management

AWS Skill Builder emphasizes risk as a combination of likelihood and severity.

Risk = likelihood of event x severity of consequence.

Risk Assessment Flow

  1. Identify the AI use case.
  2. Identify stakeholders affected by the system.
  3. Identify potentially harmful events.
  4. Estimate likelihood and severity for each event.
  5. Determine inherent risk before controls.
  6. Apply controls such as access control, monitoring, guardrails, testing, human review, or data governance.
  7. Determine residual risk after controls.
  8. Summarize the system's overall risk by focusing on the highest residual risks.
  9. Review and update the assessment on a defined cadence.

NIST AI RMF Functions

Function Exam meaning
Govern Establish policies, accountability, roles, culture, and risk management structure
Map Contextualize the AI system, use case, stakeholders, benefits, and risks
Measure Analyze, assess, benchmark, test, and monitor AI risks and trustworthiness
Manage Prioritize, respond to, mitigate, monitor, and communicate risks

Memory hook:

Govern = set rules.
Map = understand context.
Measure = assess risk.
Manage = act on risk.

12. Explainability, Transparency, and Bias in Compliance

Task 5.2 overlaps with Domain 4 because compliance questions often ask whether an AI system can be explained, audited, and monitored for bias.

Requirement What it means AWS service or practice
Disclose AI use Tell users when they interact with AI where required or appropriate UX disclosure, chatbot notice, transparency policy
Explain output Provide understandable reasons for model predictions or decisions SageMaker Clarify, model cards, documentation
Test bias Check whether model outputs unfairly affect groups SageMaker Clarify, subgroup analysis, human review
Monitor drift Watch whether bias or feature attribution changes over time SageMaker Clarify, SageMaker Model Monitor
Document intended use Record what the model is for and not for SageMaker Model Cards, AI service cards, internal documentation
Add human review Escalate uncertain or high-impact decisions Amazon A2I, business review process

Exam Pattern

If the question asks how to understand which variables influence a model's behavior or monitor for bias and feature attribution drift, choose Amazon SageMaker Clarify.

If the question asks how to document model purpose, risk, intended use, and evaluation, choose Amazon SageMaker Model Cards.

If the question asks how to meet transparency expectations for a chatbot, choose disclosure, documentation, logging, and clear user-facing boundaries.

13. Data Governance Strategy

AWS Skill Builder defines data governance as the combination of people, process, and technology used to manage the availability, usability, integrity, and security of enterprise data.

Three Major Parts

Part Meaning AI example
Curation Identify, manage, clean, and maintain valuable data sources Curate approved training or RAG documents
Discovery and understanding Make data findable and understandable through metadata and context Use a data catalog with owners, schemas, sensitivity, and lineage
Protection Balance privacy, security, and access Apply Lake Formation permissions, encryption, Macie scans, retention rules

Data Governance Roles

Role Responsibility
Data owner Executive or accountable business owner who makes policy decisions for a data domain
Data steward Business expert who understands the data and handles day-to-day data quality and usage details
IT / platform team Provides systems, tools, catalogs, access controls, monitoring, and automation
Security team Defines security controls, monitors threats, and validates control operation
Legal / compliance team Interprets laws, contracts, and regulatory obligations
Model owner Owns model risk, intended use, evaluation, and lifecycle decisions
Business owner Owns business outcome, acceptable risk, and user impact

Data Governance Vocabulary

Term Exam meaning
Data profiling Systematically examining data characteristics and detecting quality issues
Data quality Accuracy, completeness, consistency, timeliness, and fitness for use
Data catalog Metadata inventory of data sources, schemas, types, owners, and definitions
Data lineage Origin, movement, transformations, storage, and downstream use of data
Data lifecycle Creation or ingestion through storage, use, archive, retention, deletion
Data residency Requirement that data is stored or processed in specific geographic locations
Data retention How long data must be kept before archival or deletion
Observability Ability to understand system state from logs, metrics, traces, events, and monitoring
Monitoring Continuous checking of systems, models, data quality, compliance, drift, and bias

14. AWS Data Governance Services

Service Governance role Exam clue
AWS Glue Data Catalog Stores metadata such as data locations, schemas, data types, and table definitions "Catalog data sources"
AWS Glue crawlers Scan data sources and populate the Data Catalog "Automatically discover schemas"
AWS Glue Data Quality Evaluates data quality rules for objects in the Data Catalog "Recommend or run data quality rules"
AWS Glue DataBrew Visual data preparation, cleaning, normalization, profiling "Clean and normalize without writing code"
AWS Lake Formation Fine-grained governance for data lakes in S3 using the Glue Data Catalog "Column, row, or cell-level access control"
Amazon S3 lifecycle Transition, archive, or delete objects based on lifecycle rules "Retain training data for compliance at lower cost"
Amazon S3 storage classes Match storage cost to access pattern "Archive rarely accessed training data"
Amazon Macie Sensitive data discovery in S3 "Find PII before training or RAG"
AWS CloudTrail API activity logs "Audit who accessed or changed resources"
Amazon CloudWatch Metrics, logs, alarms, dashboards "Operational monitoring and observability"

Data Governance Service Patterns

Scenario Best answer
Data consumers need to find approved datasets AWS Glue Data Catalog or data cataloging strategy
Need to restrict analysts to specific rows and columns in a data lake AWS Lake Formation
Need to assess quality of a dataset before using it for model training AWS Glue Data Quality or DataBrew profiling
Need to clean and normalize a dataset visually AWS Glue DataBrew
Need to retain training data for 5 years and archive it cheaply S3 lifecycle rules to Glacier storage classes
Need to delete data after a retention period S3 lifecycle expiration rule
Need to know where a report field came from Data lineage
Need to know whether a dataset contains PII Amazon Macie or sensitive data detection

15. Data Lifecycle, Retention, and Residency

AI governance should treat data as a lifecycle asset, not as a one-time input.

Stage Governance questions
Create or collect What is the source? Is collection lawful? Is consent or notice required?
Classify Is the data public, internal, confidential, regulated, or personal?
Store Which Region? Which account? Which bucket? Which encryption key?
Catalog Who owns it? What schema? What business definition? What sensitivity?
Access Which roles can use it? Is access temporary? Is approval required?
Process What transformations occurred? Are quality rules passing?
Train, tune, or retrieve Is the data approved for AI use? Can it be used in prompts, RAG, or fine-tuning?
Monitor Are there access logs, quality checks, drift checks, and compliance checks?
Retain How long must it be kept for audit, legal, or business needs?
Archive Which S3 storage class fits access and retrieval requirements?
Delete When must it be expired? Are deletion requirements documented?

S3 Storage Class Exam Pattern

Access pattern Likely storage class
Frequently accessed training or inference data S3 Standard
Infrequently accessed but needs quick retrieval S3 Standard-IA or S3 One Zone-IA
Unknown or changing access patterns S3 Intelligent-Tiering
Rarely accessed archive for retention or compliance S3 Glacier storage classes
Very long-term archive with lowest storage cost and slower retrieval S3 Glacier Deep Archive

Exam shortcut:

Known retention timeline -> S3 lifecycle rule.
Unknown access pattern -> S3 Intelligent-Tiering.
Long-term compliance archive -> S3 Glacier storage classes.

16. Generative AI Security Scoping Matrix

The AWS Generative AI Security Scoping Matrix helps organizations understand responsibility based on how generative AI is consumed or built.

Scope Pattern Responsibility level
Scope 1 Consumer app Lowest: organization consumes a public third-party AI application
Scope 2 Enterprise app Low: organization consumes a managed enterprise AI application with administrative controls
Scope 3 Pre-trained model application Higher: organization builds an app using a pre-trained model
Scope 4 Fine-tuned model application Higher still: organization adapts a model with its own data
Scope 5 Self-trained model Highest: organization builds and trains its own model

Skill Builder exam takeaway:

Minimize scope when possible.
More ownership of model and data = more responsibility for governance, compliance, privacy, risk management, security controls, and resilience.

Left-to-Right Selection Pattern

When solving a business problem, Skill Builder recommends starting with the lowest-responsibility option that meets the requirement:

  1. Use a fully trained AWS AI service if it fits, such as Amazon Comprehend or Amazon Translate.
  2. Use a pre-trained foundation model through Amazon Bedrock if the use case needs generative AI.
  3. Add RAG through Amazon Bedrock Knowledge Bases if the model needs private knowledge.
  4. Fine-tune or customize a model only when lower-scope approaches do not meet requirements.
  5. Train a custom model only when necessary.

This reduces governance burden, legal/privacy risk, operational complexity, security-control ownership, and model-resilience responsibility.

17. Governance Protocol Process

A practical AI governance process looks like this:

  1. Identify scope of responsibility using the Generative AI Security Scoping Matrix.
  2. Define the use case, users, stakeholders, and business owner.
  3. Classify data and model risk.
  4. Identify applicable regulations, standards, contracts, and internal policies.
  5. Define policies for data governance, access, model transparency, logging, retention, human review, and incident response.
  6. Assign roles such as data owner, data steward, model owner, security owner, compliance reviewer, and business approver.
  7. Select controls and AWS services to enforce and monitor policies.
  8. Document the system, risks, controls, model purpose, and intended use.
  9. Train employees according to job role and access level.
  10. Monitor performance, compliance, security, data quality, bias, and drift.
  11. Review results on a defined cadence.
  12. Update policies, controls, and training as risks or business goals change.

Review Cadence Examples

Trigger Review needed
New model, dataset, tool, or agent capability Architecture, security, legal, and model-risk review
New regulation or contract requirement Compliance gap analysis
Failed Config rule or Inspector critical finding Remediation review
Increased hallucination, toxicity, bias, or drift Responsible AI and model-performance review
New data domain used for RAG or fine-tuning Data owner and privacy review
User complaint or incident Incident response and governance review
Scheduled quarterly or annual review Control effectiveness and policy refresh

18. Transparency Standards and Team Training

Governance protocols only work when people understand their responsibilities.

Transparency Standards

Standard What to document
User transparency When users interact with AI, limitations, escalation paths, and human review options
Model transparency Model purpose, intended use, limitations, risk rating, evaluation, monitoring, and known constraints
Data transparency Data sources, owners, lineage, sensitivity, allowed uses, and retention
Decision transparency How AI influences decisions, what humans review, and how decisions can be challenged
Control transparency Which controls exist, how they are monitored, and what evidence is retained

Training Requirements

Team Training focus
Business users Approved AI use, data handling, limitations, disclosure rules
Developers Secure AI design, logging, guardrails, least privilege, prompt injection risks
Data stewards Data quality, cataloging, lineage, sensitivity, retention
Model owners Evaluation, bias, drift, transparency, model cards, review cadence
Security team Threat modeling, logging, vulnerability management, incident response
Legal/compliance Regulations, evidence, audit readiness, data residency, privacy obligations
Support teams User escalation, incident reporting, AI output limitations

Exam shortcut:

Governance is not only tooling. It also includes policies, owners, reviews, and training.

19. Bedrock Guardrails in Governance Context

Amazon Bedrock Guardrails appears in Task 5.1, but it can also show up in governance scenarios because it implements application-specific responsible AI policies.

Governance need Guardrails feature
Block harmful categories Content filters
Avoid restricted business topics Denied topics
Detect or redact PII Sensitive information filters
Reduce unsafe prompt behavior Prompt attack filters
Check support from source context Contextual grounding checks
Provide consistent blocked-message behavior Custom blocked input and response messages

Exam pattern:

Policy says chatbot must not provide investment advice -> Bedrock Guardrails denied topic.
Policy says chatbot must not expose PII -> Guardrails sensitive information filter plus data governance controls.
Policy says responses must be grounded in retrieved sources -> RAG plus grounding checks and citations.

20. Common Exam Traps

Trap Correct thinking
"Compliance is fully handled by AWS" False. AWS handles compliance of the cloud; customers handle compliance in the cloud
"Artifact proves my workload is compliant" Artifact provides AWS reports. Your workload compliance needs your evidence
"CloudTrail evaluates compliance rules" CloudTrail records API activity. Config evaluates resource configuration compliance
"Inspector checks data quality" Inspector checks vulnerabilities and exposure. Glue Data Quality checks data quality
"Trusted Advisor creates audit reports" Trusted Advisor recommends best-practice actions. Audit Manager creates assessment reports
"Data retention means keep everything forever" Retention includes keep, archive, and delete according to policy and law
"Fine-tuning is only a model-quality decision" Fine-tuning with company data increases data governance, privacy, compliance, and model-risk responsibility
"A model can enforce data permissions by itself" Access control must be enforced before retrieval and prompt construction
"Transparency only means publishing model code" Transparency can include disclosure, documentation, explanations, logs, model cards, and review paths
"Governance frameworks replace controls" Frameworks organize controls; teams still implement and verify them

21. Scenario Decision Table

Scenario question says... Choose...
"The auditor asks for AWS SOC 2 report" AWS Artifact
"The auditor asks for evidence that your S3 training buckets are encrypted" AWS Audit Manager with evidence from AWS Config
"The security team needs to know who changed a model endpoint configuration" AWS CloudTrail
"The compliance team needs to know whether resources drifted from policy" AWS Config
"The platform team wants a reusable compliance baseline across accounts" AWS Config conformance packs
"The application team needs to find critical CVEs in container images" Amazon Inspector
"The operations team wants recommended actions for security and service limits" AWS Trusted Advisor
"A data owner needs to approve which datasets can be used for fine-tuning" Data governance policy and data owner approval
"A business user needs to find trusted customer datasets" AWS Glue Data Catalog or data catalog
"A team must prevent analysts from seeing sensitive columns" AWS Lake Formation fine-grained access control
"Training data must be retained for five years at low cost" S3 lifecycle rules to Glacier storage classes
"A chatbot must disclose it is AI" Transparency standard and user-facing disclosure
"A high-impact model must be reviewed for bias" SageMaker Clarify, human review, governance process
"The team is choosing between fully managed AI service and custom model" Use Generative AI Security Scoping Matrix and minimize scope

22. Mini Flashcards

Question Answer
What is Domain 5 worth? 14% of scored AIF-C01 content
What is the main Task 5.2 theme? Governance and compliance regulations for AI systems
What AWS service provides AWS compliance reports? AWS Artifact
What AWS service collects audit evidence and creates assessment reports? AWS Audit Manager
What AWS service records API calls and user activity? AWS CloudTrail
What AWS service tracks resource configuration history and compliance? AWS Config
What AWS feature packages Config rules and remediation actions? Conformance packs
What AWS service scans for vulnerabilities and unintended network exposure? Amazon Inspector
What AWS service gives best-practice recommendations across cost, performance, resilience, security, operations, and limits? AWS Trusted Advisor
What AWS service governs fine-grained data lake permissions? AWS Lake Formation
What stores metadata about data locations, schemas, and table definitions? AWS Glue Data Catalog
What evaluates data quality rules in Glue? AWS Glue Data Quality
What service helps clean and normalize data visually? AWS Glue DataBrew
What manages S3 archival and deletion over time? S3 lifecycle rules
What is data lineage? The origin, movement, transformation, storage, and downstream use of data
What is residual risk? Risk remaining after controls and mitigations
What are the NIST AI RMF functions? Govern, Map, Measure, Manage
What happens as GenAI scope increases? Customer responsibility for governance, compliance, security, privacy, and resilience increases

23. Last-Minute Checklist

Before the exam, make sure you can say yes to each statement:

  • I know Domain 5 is 14% of the scored exam.
  • I know Task 5.2 is about governance, compliance, data governance, and governance protocols.
  • I can distinguish AWS Artifact, Audit Manager, Config, CloudTrail, Inspector, and Trusted Advisor.
  • I know Artifact is AWS-side reports, while Audit Manager helps collect customer-side evidence.
  • I know CloudTrail records API activity, while Config evaluates resource configurations.
  • I know Inspector is for vulnerability management, not audit reports.
  • I know Trusted Advisor provides best-practice recommendations.
  • I know data governance includes curation, discovery and understanding, and protection.
  • I know data governance includes lifecycle, logging, residency, monitoring, observability, and retention.
  • I can explain data owner vs data steward.
  • I can identify Glue Data Catalog, Glue Data Quality, DataBrew, Lake Formation, and S3 lifecycle patterns.
  • I can explain inherent risk vs residual risk.
  • I know the NIST AI RMF functions: Govern, Map, Measure, Manage.
  • I understand that EU AI Act questions are risk-based: unacceptable risk is prohibited; high-risk systems have stronger obligations.
  • I know governance protocols require policies, owners, review cadences, transparency standards, and training.
  • I know the Generative AI Security Scoping Matrix: more model/data ownership means more responsibility.

Sources