Secure Agentic AI Workflows, Models, and Tooling
Proven at Global Scale
Protecting trillions of transactions and billions of people across the largest global organizations in Finance, Healthcare, Retail, and Governments.
Centralized Policy.
Unified Control.
Distributed Enforecement.
As organizations scale AI, analytics, and data flows across clouds, warehouses, and models, Protegrity AI Enterprise Edition applies consistent policies for data discovery, protection, privacy, and semantic guardrails – improving precision, increasing trust, and lowering risk without slowing performance.
Policy Management
Define and enforce consistent rules for redaction, masking, encryption, tokenization, and AI guardrails across data systems. Policies follow your data wherever it moves—cloud, on-prem, or model pipeline—ensuring consistent control.
Discovery & Classification
Automatically detect and label sensitive information such as PII, PHI, or financial data across structured and unstructured sources. Context-aware classifiers identify risk early, before data enters analytics or AI workflows.
Tokenization & Encryption
Protect sensitive values with high-speed vaultless tokenization that preserves format and performance. When controlled re-identification is required, optional vault-based tokens maintain context while keeping real data secure.
Anonymization
Apply privacy-preserving transformations using statistical methods such as k-anonymity, l-diversity, and t-closeness. These techniques enable analytics on protected data while minimizing re-identification risk.
Synthetic Data Generation
Control how large language models handle data by enforcing runtime policies on prompts and outputs. Guardrails detect and redact sensitive content automatically, ensuring that GenAI systems remain compliant and trustworthy.
Policy Management
Define and enforce consistent rules for redaction, masking, encryption, tokenization, and AI guardrails across data systems. Policies follow your data wherever it moves—cloud, on-prem, or model pipeline—ensuring consistent control.
Audit & Insight
Every protection event is logged and visualized for full transparency. Dashboards show what was protected, how it was used, and when—simplifying audits and helping teams demonstrate compliance across environments.
Kubernetes-Based &
Enterprise Ready
Deployment Architecture
Deploy Protegrity AI Team Edition using infrastructure-as-code templates—Terraform provisions your AWS environment and Kubernetes cluster (EKS, ECS, or Docker Compose), while Helm Charts configure and install the platform’s microservices. Ingress, TLS management, routing, and audit logging are built in. Updates are applied by refreshing container images—no patching or appliance maintenance required.
One Platform for Developers, Teams,
and Enterprise
Protegrity
AI Enterprise Edition
Protegrity AI Enterprise Edition extends the platform for organizations that need to scale policy and protection across clouds, environments, and teams. It introduces centralized policy management, broader entitlement coverage—including hybrid and on-prem deployment—and support for HSM-based key management. AI Enterprise Edition also includes formal SLA commitments and integration pathways aligned to procurement and compliance needs.
Protegrity
AI Team Edition
Protegrity AI Team Edition is a self-contained deployment running in your AWS environment. It's built for individual teams that need to secure sensitive data fast—without complex infrastructure or central dependencies. Entitlements include local policy control and one protector per family, covering tools like Redshift, Java, and Databricks. While current deployment is AWS-specific, the architecture is built to support additional platforms in future releases.
Ways organizations deploy Protegrity AI Enterprise Edition
Enterprise-Scale
Protection in Practice
USE CASE 1
Tokenized Analytics and Data Sharing
Challenge
Sensitive data limits collaboration and analytics. Analysts often need to query or share data containing personal or financial identifiers. Traditional encryption breaks joins and slows performance, forcing teams to choose between insight and compliance
Protegrity AI Enterprise Edition Solution
Protegrity AI Enterprise Edition applies high-speed vaultless tokenization across data warehouses and BI tools. It keeps relationships intact, so analysts can run joins, aggregates, and reports while the data remains protected end-to-end.
USE CASE 2
AI Guardrails and Discovery for GenAI
Challenge
Sensitive data can leak through GenAI pipelines. AI teams often handle prompts, embeddings, and outputs that contain regulated or private information. Without proper detection and controls, that data can surface in model responses or logs.
Protegrity AI Enterprise Edition Solution
Protegrity AI Enterprise Edition uses built-in discovery to identify sensitive data before it enters AI models. During runtime, semantic guardrails inspect and filter prompts and outputs, automatically blocking or redacting regulated content without disrupting model performance.
USE CASE 3
Privacy-Ready Model Training
Challenge
Model development requires realistic data without privacy risk. Data scientists often need full-fidelity datasets for training and validation, but using live customer or patient records creates compliance and security exposure.
Protegrity AI Enterprise Edition Solution
Protegrity AI Enterprise Edition creates statistically accurate synthetic datasets or applies anonymization methods—such as k-anonymity, l-diversity, and t-closeness—so teams can train and test AI models safely while maintaining analytic accuracy and privacy compliance.
Visibility & proof of
protection
Audit & compliance
Every discovery, tokenization, and policy event is logged to OpenSearch and exportable to enterprise SIEM systems. Dashboards provide at-a-glance compliance status for audits such as SOC 2 and HIPAA. Data lineage can be traced from source to model output.
Connect to data warehouses, AI Pipelines, & Enterprise Systems
BROAD INTEGRATION ACROSS
DATA & AI STACKS
Application
Protectors
Support for Java, .NET, and Python applications allows developers to apply data protection directly within business logic and APIs—without changing application behavior.
Analytics &
Big Data
Native protectors for Databricks SQL, Amazon EMR, and Cloudera/CDP integrate protection directly into large-scale analytics pipelines. Sensitive data stays governed even as it moves through distributed compute frameworks.
Cloud Data
Warehouses
Connectors for Snowflake, Amazon Redshift, and Amazon Athena secure queries and transformations without altering schemas or requiring ETL rewrites.
Web & Application
Layers
The WebApp Protector enforces data policies in real time at the user interface level, giving administrators fine-grained control over what data can be viewed or unprotected inside web applications.
Mainframe &
File Systems
File and Mainframe Protectors extend modern data protection to legacy platforms, ensuring consistent security and compliance across decades of infrastructure.
Vector & AI
Pipelines
Policy-driven tokenization for embeddings, retrieval, and LLM input/output secures sensitive data used in GenAI and machine learning workflows. REST APIs and SDKs provide integration points for any model pipeline.
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