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What counts as sensitive data
In this lesson
What counts as sensitive data
Classify at least six categories of sensitive data (personal identifiable information, financial details, health records, confidential business data, security credentials, and legal documents) according to their…
You'll be able to
- Classify at least six categories of sensitive data (personal identifiable information, financial details, health records, confidential business data, security credentials, and legal documents) according to their disclosure risk in LLM application contexts, citing sector-specific regulatory frameworks where applicable [^3].
- Evaluate whether a given dataset or prompt input contains sensitive information that requires sanitization before entering an AI training pipeline or runtime orchestration layer, applying the principle of least privilege and data minimization [^3].
- Apply the NIST AI Risk Management Framework MAP function to characterize the likelihood and magnitude of harmful impacts from sensitive information disclosure, documenting expected use cases and past incident reports in similar AI system contexts [^5].
- Explain in plain language how processing of personal data under regulations such as GDPR exposes AI systems to data protection, privacy, and security risks, and communicate these risks to non-technical stakeholders including senior executives and government officials [^2][^6][^7].
- Create a risk-based data sanitization plan that integrates strict input validation, access controls, and transparency policies, ensuring the plan is outcome-focused and adaptable to cross-sectoral AI profiles without prescribing one-size-fits-all requirements [^2][^3].