Protegrity Alternative

Protegrity vs Protecto

Tokenizing PII without retaining context kills AI accuracy

Protecto is the only data privacy tool that offers smart tokenization to retain context, scan PII with full accuracy, and facilitate role based access control in AI systems.

Why Protegrity falls short in critical areas?

While Protegrity’s capability in identifying and masking sensitive data is satisfactory, it is not built for modern enterprise grade AI systems.

Lacks context: Built primarily for hybrid environments, Protegrity only retains the format, not the context. The result? AI hallucinations, compliance risks, poor quality analysis.
Lower integration flexibility: Tightly bound to Azure, Protegrity lacks the APIs which gives the flexibility to integrate easily in a multi cloud setup.
Missing contextual scanning: Scans PII by identifying each data rather than understanding the full context, resulting in false positives and negatives.
Lower security: Uses a vaultless, algorithmic token generation using static codebooks that is less secure compared to Protecto’s entropy based tokenization

Why Choose Protecto Over Protegrity?

The ideal tool built for enterprise data, optimized to scale

Simple APIs: Use sync and async APIs to integrate with any part: preprocessing, context data, prompt, or response.
Contextual scanning: Understands the full context of prompt to intelligently and accurately identify PII across structured and unstructured text.
Higher security: Uses intelligent, entropy based tokenization ensures strong anonymization to lower re-identification risk.
Context based access control: Mask and unmask data based on role and context so only the authorised users access data they are supposed to see.
Deterministic, consistent tokenization: Generates intelligent, entropy based tokens that consistently masks data throughout its life cycle, while giving AI context in the type of data.

COMPARISON

Protegrity vs. Protecto

Lets summarize the capabilities and features to help you make a decision.

Features	Protecto	Protegrity
API-first tokenization	REST APIs, flexible and easy to integrate	API tightly bound to Azure Functions
Format-preserving tokenization	Tokens retain context & format for AI/analytics	Retains format but lacks context for AI
Deterministic tokenization	Consistent tokens for same input	Deterministic supported, but context issues remain
Custom tokenization policies	Highly flexible (define token type, patterns)	Vaultless tokenization with less flexibility
Cloud-agnostic deployment	Containerized, works across any cloud or on-prem	Primarily optimized for Azure, less depth elsewhere
Unstructured data detection	Detects structured & unstructured data with context	Mainly structured data, weak on unstructured/context
Easy deployment & automation	Lightweight, quick to deploy, automation-friendly	Mainly structured data, weak on unstructured/context
Strong governance & RBAC	API-driven RBAC & policy enforcement	Strong enterprise RBAC, but complex to manage
Detailed audit logging	Logs every tokenization/detokenization call	Audit logging exists but requires extra setup
Scalability & performance	Cloud-native, scales with distributed processing	Highly scalable for enterprise & legacy workloads

Built for Enterprise. Optimized for Scale.

On-Premises or SaaS

Deploy Protecto on your servers or consume it as SaaS. Either way, get the full benefits including multitenancy.

Simple APIs

Use sync and async APIs to integrate with any part: preprocessing, context data, prompt, or response.

Auto-scale

Protecto's architecture scales to process billions of rows or runs lightweight on-edge devices, offering versatility and efficiency.

Trusted by Industry Leaders

See why businesses around the world choose Protecto for their AI data security and privacy needs.

"Protecto empowers us to protect data, bolster customer confidence, and make significant strides towards achieving full compliance across AI and non-AI applications"

Jagannath Gorti

Nokia's Head of IT Commercial Engineering