Sold by: Philterd
Deployed on AWS
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Philter deidentifies and redacts sensitive information, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), in text.
Overview
Philter deidentifies and redacts sensitive information, such as Personally Identifiable Information (PII) and Protected Health Information (PHI),in text. Common uses of Philter include helping maintain HIPAA compliance, serving as an AI policy layer, redacting AI training data, and allowing for leveraging your text for valuable secondary purposes.
Philter can redact over 30 types of sensitive information, such as persons' names, ages, bitcoin addresses, cities, counties, credit cards, custom dictionaries, dates, drivers license numbers, email addresses, iban codes, ip addresses, mac addresses, passport numbers, phone/fax numbers, ssns and tins, shipping tracking numbers, states, urls, VINs, and zip codes. In addition, you can define your own types of sensitive information that Philter can then redact.
Philter can disambiguate between types of sensitive information allowing it to redact the information appropriately, and conditional statements allow you to redact only certain information, such as ages older or younger than a given value. Philter's Consistent Anonymization feature anonymizes information consistently across multiple documents to preserve meaning while removing personally identifiable information.
Philter is API based and open source client SDKs are available. The Philter Add-Ins for Microsoft Office provide redaction capabilities inside your Microsoft Word documents and Microsoft Excel documents.
Highlights
- Your data never leaves your cloud. Philter runs inside your VPC and requires no external connectivity.
- Sensitive information that is found can be redacted, masked, encrypted, tokenized, or anonymized. You can choose how and when each type of PII/PHI is modified.
- Philter's API accepts text and returns the redacted version allowing Philter to be integrated into custom applications and workflows.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu ubuntu-jammy-22.04-amd64-server-20240701
Deployed on AWS
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Pricing
Free trial
Try this product free for 30 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
m5.large Recommended | $0.49 |
m5.xlarge | $0.49 |
p3.2xlarge | $0.49 |
t3.large | $0.49 |
m5.2xlarge | $0.49 |
t3.2xlarge | $0.49 |
c5.2xlarge | $0.49 |
c5.large | $0.49 |
c5.xlarge | $0.49 |
p3.8xlarge | $0.49 |
Dimensions summary
You pay by the hour for each running instance, and the software rate is the same across every instance type. What changes between dimensions is the underlying compute size, not the software price. The t3, m5, and c5 families give you general-purpose and compute options at different vCPU and memory sizes. The p3 family adds GPU capacity for higher-volume workloads. You pick the instance that fits your throughput needs. Billing runs through your existing AWS account. An annual contract option can lower the effective per-hour rate.
Top-of-mind questions for buyers
Each dimension maps to an AWS instance size. A t3.large gives 2 vCPU and 8 GiB, suited to evaluation. Production redaction often runs on m5.xlarge (4 vCPU, 16 GiB) or c5.xlarge. The p3 GPU family fits high-volume workloads using the Healthcare lens.
The software meters running instance-hours only. A fully powered-off instance does not accrue software charges. Stopped instances may still incur underlying AWS storage fees from your own account, but the Philter software rate applies to running time.
No. The software rate per hour is the same across every supported instance type. Instance size affects throughput, not the software charge. You pay the underlying AWS compute cost for the larger instance, while the Philter software rate stays constant.
www.philterd.ai
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We do not currently offer refunds but you can cancel at any time.
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Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Minor fixes, dependency updates, and now built on Phileas 3.2.0. See the full list at https://github.com/philterd/philter/releases/tag/3.3.0 .
Additional details
Usage instructions
After launch, Philter will begin initializing. The initialization may take up to 3 minutes to complete. You can monitor Philter's status with the command:
curl -k https://<Public_IP>:8080/api/status
The response will indicate if Philter is ready to serve requests. Requests to filter text prior to initialization completion will return an HTTP 503 error. Once initialized, you can send a text file to Philter's API with the command:
curl -k -X POST https://<Public_IP>:8080/api/filter -d @file.txt -H Content-Type "text/plain"
Additionally, you can send text directly to Philter via its API:
curl -k -X POST https://<Public_IP>:8080/api/filter --data "The patient was John Smith with SSN 123-45-6789." -H "Content-type: text/plain"
The expected response will be "The patient was {{{REDACTED-entity}}} with SSN {{{REDACTED-ssn}}}."
The Philter UI can be accessed at https://<Public_IP>:9000.
Refer to the Getting Started guide at https://philter.mtnfog.com/introduction/quick-start for basic configuration and usage examples.
Philter's User's Guide is available at https://philter.mtnfog.com .
Resources
Vendor resources
Support
Vendor support
Email support at support@philterd.ai - please allow up to 48 hours for a response. Visit https://www.philterd.ai/support/ for product documentation and other resources.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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By Philterd
By Cloud Storage Security
By Immuta
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In Masking/Tokenization, Text, Data Preparation
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Sensitive Data Detection and Redaction
Detects and redacts over 30 types of sensitive information including names, ages, bitcoin addresses, credit card numbers, email addresses, IP addresses, passport numbers, phone numbers, SSNs, VINs, and zip codes with support for custom dictionary definitions.
Data Modification Methods
Supports multiple redaction techniques including redaction, masking, encryption, tokenization, and anonymization with configurable options for each type of sensitive information.
Consistent Anonymization
Preserves meaning across multiple documents by anonymizing information consistently while removing personally identifiable information.
Conditional Redaction Logic
Enables conditional statements to redact only specific information based on defined criteria, such as ages within specified ranges.
API-Based Architecture with VPC Deployment
Operates as an API-based service that runs within customer VPC with no external connectivity requirement, supported by open source client SDKs and Microsoft Office Add-Ins for Word and Excel integration.
Sensitive Data Classification
Identifies hundreds of sensitive data types including PII, financial data, healthcare information, and government ID numbers across multiple file types and 11 regional localizations
Automated Scanning Architecture
Deploys using serverless architecture with AWS Fargate Container and AWS CloudFormation template, supporting real-time and on-demand DLP scanning at petabyte scale with no file size limit
Data Quarantine and Tagging
Automatically quarantines objects and files identified as sensitive or containing PII across all S3 buckets and EC2 EBS volumes, with capability to tag flagged items
Multi-Channel Notifications and Integrations
Supports robust notifications and integrations with third-party ticketing systems, Slack, Microsoft Teams, Amazon Chime, SIEM solutions, Amazon SNS, AWS Security Hub, AWS CloudTrail, AWS Control Tower, and AWS Transfer Family
Bucket Configuration Assessment
Evaluates bucket attributes including public accessibility status and encryption configuration to cross-reference with classification findings and assess data risk exposure
Fine-Grained Data Access Control
Dynamic, fine-grained access control on AWS services including Amazon Redshift, Amazon S3, Amazon SageMaker, and Amazon EMR using de-identification, hashing, masking, and privacy enhancing technologies (PETs).
Sensitive Data Discovery and Classification
Automated sensitive data discovery capabilities to tag and classify sensitive data in AWS with highly accurate metadata generation for data security purposes.
Policy Management and Enforcement
Write data security policies once in plain language or as-code and enforce them consistently across multiple AWS data sources without copying or moving data.
Attribute-Based Access Control
Attribute-based access control (ABAC) implementation to simplify and streamline data security policy management across connected data sources.
Data Activity Monitoring
Real-time continuous monitoring of data and user query behavior to track activity, identify risky patterns, and enable proactive policy updates.
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