This skill helps organizations achieve robust data governance, mitigate compliance risks.

  • Discover Data Sources
  • Map Constraints and Classify Data
  • Generate Governance Plan

Free Diagnostic Checklist

7 Warning Signs Your AI Data Governance is Failing

This diagnostic helps AI product managers and data teams identify critical gaps in their data constraint mapping and governance practices, preventing compliance issues and poor AI model performance.

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  1. 1 Work through this checklist by hand on your own work today.
  2. 2 Identify potential gaps in your AI data constraint mapping and governance.
  3. 3 Use the results to create an action plan for improvement.

The checklist

  1. 1 Undocumented Data Flows Critical
    What it looks like
    Undocumented data flows lead to hidden compliance risks, data quality issues, and potential privacy breaches.
    What to check
    Cross-reference discovered data sources with application code dependencies, infrastructure configurations, and network traffic logs to ensure comprehensive coverage.
    Ask
    Have all data sources consumed by your AI application been identified and documented?
    If it is present
    Implement a process to comprehensively identify and document all data sources, including code dependencies and infrastructure configurations.
  2. 2 Incorrect Sensitive Data Classification Critical
    What it looks like
    Over-classification can hinder legitimate data use, while under-classification can result in severe privacy violations and regulatory fines.
    What to check
    Apply a combination of pattern matching, dictionary lookups, and machine learning models to data samples.
    Ask
    Are sensitive data fields (e.g., PII) correctly classified and protected according to your policies?
    If it is present
    Review and validate your sensitive data classification methods, using automated tools and cross-validation with organizational policies.
  3. 3 Generic Data Retention Policies
    What it looks like
    Data is either deleted prematurely, leading to loss of valuable historical context, or retained too long, increasing storage costs and compliance liabilities.
    What to check
    Map each data field to its specific regulatory (e.g., GDPR, HIPAA) and business retention requirements.
    Ask
    Are data retention policies applied consistently and specifically to each data store based on regulatory and business needs?
    If it is present
    Develop granular data retention policies that align with specific regulatory and business requirements for each data field.
  4. 4 Misaligned Data Access Controls Critical
    What it looks like
    Unauthorized access to sensitive data, leading to data breaches, or legitimate users being blocked, causing operational delays.
    What to check
    Compare the proposed access control matrix with actual IAM roles and permissions configured in data stores and APIs, flagging any discrepancies for review.
    Ask
    Do your current data access controls align with your identified data governance plan?
    If it is present
    Regularly audit and compare actual IAM roles and permissions against your defined access control matrix to ensure alignment.
  5. 5 Overlooked Data Quality Issues
    What it looks like
    Poor data quality (e.g., missing values, inconsistencies, outliers) directly degrades AI model performance, leading to inaccurate predictions and unreliable outcomes.
    What to check
    Implement automated data profiling and validation checks (e.g., completeness, uniqueness, consistency).
    Ask
    Are data quality issues (e.g., completeness, consistency) in source data identified and addressed before use by AI models?
    If it is present
    Establish automated data profiling and validation checks for all ingested data, enforcing defined schema and business rules.
  6. 6 Lack of Continuous Monitoring
    What it looks like
    The data constraint map and governance plan quickly become outdated, leading to new compliance gaps, data quality degradation, and increased risk exposure over time.
    What to check
    Establish automated monitoring triggers on code repositories, infrastructure configurations.
    Ask
    Is there continuous monitoring for changes in data schemas, code, or regulatory landscapes?
    If it is present
    Implement automated monitoring on code repositories, infrastructure, and regulatory databases to detect changes and trigger re-evaluation.

Your action plan

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This diagnostic checklist provides a general assessment of potential data governance issues. It is not a substitute for legal advice or a guarantee of compliance. Always consult with legal and compliance professionals for specific guidance related to your organization's data handling practices.

Questions and answers

How does this skill ensure data privacy compliance?

The skill analyzes data content to classify sensitive information like PII and maps it against relevant data privacy regulations such as GDPR or HIPAA. It then generates a governance plan with appropriate access controls and retention policies, and verifies your current setup for discrepancies.

Can this skill modify my data or infrastructure?

No, this skill operates strictly in a read-only mode. It inspects your code, configurations, and data sources to identify issues and propose solutions, but it will never modify your data, code, or infrastructure directly. All suggested changes require your explicit approval and manual implementation.

What if my data sources are not easily discoverable or accessible?

The skill will report any inaccessible repositories or configurations and mark affected data sources as unverified. For data with unresolvable schemas or lineage, it will flag them for manual review, ensuring you are aware of any gaps in the automated mapping process.

How does the skill handle changes in regulations or my application?

The skill establishes automated monitoring on code repositories, data sources, and relevant regulatory databases. Upon detecting changes, it re-evaluates the data constraint map and governance plan, flags new risks, and suggests necessary adjustments to maintain continuous compliance and data quality.

AI Data Constraint and Governance Mapper$170.00 one time
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