Comprehensive Guide

AI Security 101

Everything security leaders need to know about protecting sensitive data in AI workflows. From understanding risks to implementing controls, this guide covers the fundamentals.

51 min total read5 chaptersFor security leaders

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Key Takeaways

The essential principles you will learn from this guide.

Data Exposure is Real

Every prompt sent to an AI system is potential data exposure. Understanding this is the first step to protection.

Defense in Depth

No single control is sufficient. Effective AI security requires multiple layers of protection.

Visibility is Critical

You cannot protect what you cannot see. Comprehensive monitoring and detection is foundational.

Risk-Based Approach

Not all data requires the same protection. Focus resources on your highest-risk data types.

Course Chapters

Work through each chapter to build a comprehensive understanding of AI security.

Understanding AI Data Risks

8 min

What happens to your data when it enters an AI system, and why that matters.

How LLMs process and store dataTraining data exposure risksPrompt injection vulnerabilitiesData residency concerns

Sensitive Data Types in AI Workflows

6 min

Identifying PII, PHI, and other sensitive data that requires protection.

PII categories and risk levelsPHI under HIPAAFinancial data considerationsIntellectual property risks

Common AI Security Threats

10 min

The primary attack vectors and accidental exposure scenarios in AI workflows.

Data leakage through promptsShadow AI usageModel output risksThird-party AI provider exposure

Protection Strategies

12 min

Technical and organizational controls to reduce AI data exposure.

Data masking and tokenizationAccess controls and policiesMonitoring and audit loggingVendor security assessment

Implementation Roadmap

15 min

Step-by-step guidance for deploying AI data protection in your organization.

Risk assessment first stepsQuick wins vs. strategic initiativesTeam roles and responsibilitiesMeasuring success

Common Pitfalls to Avoid

Learn from the mistakes others have made in AI security programs.

Relying on AI Provider Promises

Assuming "enterprise" tiers automatically solve security. Provider data policies vary widely and may change.

Implement your own data protection layer before data reaches any AI provider.

Ignoring Shadow AI

Focusing only on sanctioned tools while employees use dozens of unsanctioned AI applications.

Deploy discovery tools and establish clear policies for AI tool usage.

Blocking Instead of Enabling

Banning AI entirely, which drives usage underground and creates larger blind spots.

Provide secure AI access that meets user needs while maintaining controls.

One-Time Assessment

Treating AI security as a project rather than an ongoing program.

Establish continuous monitoring, regular reviews, and adaptive controls.

AI Security Checklist

Use this checklist to assess and improve your AI security posture.

Discovery

Inventory all AI tools in use (sanctioned and shadow)
Map data flows into and out of AI systems
Identify sensitive data types in AI workflows
Document AI provider security postures

Protection

Implement data detection before AI input
Deploy masking for sensitive data types
Configure access controls and policies
Enable audit logging for all AI interactions

Governance

Establish AI usage policies
Define acceptable use guidelines
Create incident response procedures
Set up regular security reviews

Continue Learning

Explore related guides and resources.

HIPAA Compliance Guide

Healthcare-specific guidance for AI data protection.

Read Guide

PII/PHI Detection Guide

Understanding sensitive data types and detection methods.

Read Guide

AI Security Glossary

Definitions for key terms in AI security and privacy.

Read Guide

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