AI Security

7 Generative AI Security Risks & Prevention

7 Generative AI Security Risks and How to Defend Your Organization

Discover 7 generative AI security risks and how to prevent them. Learn to protect sensitive data, stop prompt injection, and secure AI systems....
Agentic AI security diagram showing data streams flowing through a context protection layer before reaching an AI agent

Agentic AI Security: Why Agent-as-a-Service Needs a New Control Layer

Agent as a service is reshaping enterprise software. Learn why agentic AI security demands context-aware data protection, not traditional perimeter defenses....
What is Data Masking

What is Data Masking

Understand how companies protect customer data, prevent AI leaks, and meet compliance requirements without slowing innovation....

Designing Tokens That Survive SQL, JSON, Logs, and Prompts with Protecto

Tools like Protecto enforce identity-aware tokenization across apps, data stores, and prompts. Learn how this is done. ...

How Protecto Delivers Format Preserving Masking to Support Generative AI

Protecto deploys a number of smart techniques to secure sensitive data in generative AI workflows, maintaining structure and referential integrity while preventing leaks or false semantics. Read on to know how. ...

Why Preserving Data Structure Matters in De-Identification APIs

Whitespace, hex, and newlines are part of your data contract. Learn how “normalization” breaks parsers and RAG chunking, and why idempotent masking matters....

Regulatory Compliance & Data Tokenization Standards

As we move deeper into 2025, regulatory expectations are rising, AI workloads are expanding rapidly, and organizations are under pressure to demonstrate consistent, trustworthy handling of personal data. Learn how tokenization reduces risk, simplifies compliance, and supports scalable data operations. ...

PII Detection in Unstructured Text: Why Regex Fails (And What Works)

Regex breaks down the moment PII appears in messy, unstructured text. Real-world conversations, notes, and documents require context-aware detection. In this article, we explore why regex fails, what modern NLP-based approaches do differently, and how teams can achieve reliable, audit-ready PII protection....
Overcoming the Challenges and Limitations of Data Tokenization

Overcoming the Challenges and Limitations of Data Tokenization

Analyze the most pressing challenges and known limitations in data tokenization, from technical hurdles to process complexity and scalability. Gain solutions and mitigation strategies to ensure effective and secure data protection deployments....
Best Practices for data tokenization

Best Practices for Implementing Data Tokenization

Discover the latest strategies for deploying data tokenization initiatives effectively, from planning and architecture to technology selection and integration. Detailed checklists and actionable insights help organizations ensure robust, scalable, and secure implementations....
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