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AI Security

  • AI Privacy, AI Security, Data De-Identification, Data Masking

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....
  • 5 min read
  • AI Privacy, AI Security, AI Trust

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. ...
  • 7 min read
  • AI Privacy, AI Security

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....
  • 7 min read
Overcoming the Challenges and Limitations of Data Tokenization
  • AI Privacy, AI Security

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....
  • 8 min read
Best Practices for data tokenization
  • AI Privacy, AI Security, AI Trust

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....
  • 5 min read
  • AI Guardrails, AI Privacy, AI Security

Stop Gambling on Compliance: Why Near‑100% Recall Is the Only Standard for AI Data

AI promises efficiency and innovation, but only if we build guardrails that respect privacy and compliance. Stop leaving data protection to chance. Demand near‑perfect recall and choose tools that deliver it....
  • 6 min read
Enterprise PII Protection Approaches to Limit Data Proliferation
  • AI Privacy, AI Security

Enterprise PII Protection: Two Approaches to Limit Data Proliferation

Learn how tokenization, centralized identity models, and governance strategies safeguard sensitive data, reduce compliance risks, and strengthen enterprise privacy frameworks in today’s evolving digital landscape....
  • 4 min read
How Enterprise CPG Companies Can Safely Adopt LLMs
  • AI Compliance, AI Guardrails, AI Privacy, AI Security, AI Trust

How Enterprise CPG Companies Can Safely Adopt LLMs Without Compromising Data Privacy

Learn how publicly traded CPG enterprises overcome data privacy barriers to unlock LLM adoption. Discover how Protecto's AI gateway enables safe AI implementation across marketing, analytics, and consumer experience. ...
  • 7 min read
  • AI Agent, AI Privacy, AI Security, AI Trust, Alternatives

Comparing Best NER Models for PII Identification

Enterprises face a paradox of choice in PII detection. This guide compares leading models - highlighting strengths, limitations, and success rates to help organizations streamline compliance and anonymization workflows....
  • 12 min read
Entropy vs. Encryption: Which Tokenization is Better?
  • AI Privacy, AI Security

Entropy vs. Encryption: Which Tokenization is Better?

Compare encryption-based and entropy-based tokenization for protecting sensitive data in AI systems. Explore how entropy-based methods offer faster performance, reduced risk, and better compliance, making them ideal for modern AI pipelines and privacy-focused architectures....
  • 5 min read
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