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Data in Plain Sight: Finding, Classifying, and Protecting Sensitive Data Before AI Exposes It
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Publisher: Heather Case-Hall
Pages: 446
Weight: 1.68
Height: 0.90 Width: 7.50 Depth: 9.25
Language: English
Data security used to be about locking down systems. Now AI can expose the data everyone forgot was there.
Written by Heather Case-Hall, a cybersecurity leader, Army veteran, and senior cybersecurity solutions architect with more than two decades of IT and security experience, Data in Plain Sight is a practical field guide for protecting sensitive data before AI, copilots, automation, and over-permissioned users turn hidden risk into a very public problem.
Organizations are adopting AI faster than their data governance, identity controls, privacy operations, and security programs can keep up. Sensitive data now lives across cloud platforms, SaaS applications, collaboration tools, databases, data lakes, file shares, backups, developer repositories, and AI-connected workflows. The old model of protecting systems is no longer enough.
This book explains how to build a modern data security program before buying another tool. It breaks down Data Security Posture Management, also known as DSPM, and shows how it connects to DLP, IAM, privacy operations, governance, remediation, reporting, AI readiness, and executive risk management.
Written for practitioners and leaders, this guide helps readers:
- Identify and classify sensitive data across complex environments
- Build the business case for DSPM and data security investment
- Connect data security to identity, privacy, DLP, retention, and AI governance
- Prioritize risk using exposure, access, sensitivity, and business context
- Remediate overexposed, orphaned, stale, and excessive-access data
- Prepare data environments before connecting them to AI systems
- Report progress to executives and boards in language that matters
- Build a practical 30, 60, and 90-day data security program
- Evaluate DSPM platforms without letting the tool define the program
This is not a vendor comparison guide or a legal manual. It is a clear, practical resource for anyone responsible for reducing data risk, enabling safer AI adoption, and building a defensible data security operating model.
The future of AI, privacy, cybersecurity, and trust depends on whether organizations can govern data with enough clarity, humility, and discipline to use it safely. This book shows where to start.
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