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    PII Detector Model

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    Sold by: Sigmodata 
    Deployed on AWS
    Free Trial
    SageMaker model for detecting personally identifiable information - names, emails, phone numbers, SSNs, credit cards, IBANs, IP addresses, and more - and applying a configurable action per entity type. Text is processed inside your own AWS account; nothing is sent to a third-party API.

    Overview

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    This SageMaker model package detects and anonymizes personally identifiable information (PII) in text. Built on Microsoft Presidio (Analyzer + Anonymizer) with spaCy en_core_web_lg, it identifies around 18 entity types by default - person names, email addresses, phone numbers, US Social Security numbers, credit card numbers, IBAN codes, IP addresses, URLs, crypto wallet addresses, and more - and lets you choose a different action for each: replace with a placeholder, mask with asterisks, redact entirely, hash (one-way, optionally with a caller-supplied salt for a stable join key), or encrypt (reversible, with a caller-supplied key that is never stored server-side).

    Unlike a hosted PII API, this model runs entirely inside your own AWS account and VPC - the text you send it is never transmitted to an external service. That in-VPC data residency is the reason to run it here. GPU is not used; deploy on a CPU instance.

    This listing is the SageMaker path (API only). For a self-hosted HTTP API, a point-and-click portal, CSV upload, an S3 batch queue, and POST /decrypt, use the PII Detector Server AMI.

    Supports real-time inference and SageMaker batch transform, with JSON and CSV input/output. Common uses include redacting PII from support tickets, logs, and data exports before they reach a data warehouse or an LLM, and compliance workflows where sending customer text to a third-party PII API is not an option.

    We welcome your feedback at aws-support@sigmodata.com .

    Model and training data

    Stock Presidio Analyzer + Anonymizer (MIT) with spaCy en_core_web_lg. Sigmodata did not fine-tune a detector for this version. The spaCy model is the public English large pipeline; regex and checksum recognizers (email, SSN, credit card, IBAN, IP, URL) are Presidio's defaults. The image runs in network isolation on SageMaker, so no data leaves your account.

    Known limitations

    • English only. Other languages are not supported.
    • Organization, location, and date/time detection use general-purpose spaCy NER and are noisier than the checksum-backed types.
    • Full postal-address detection is not a built-in capability.
    • Phone recall on mixed international formats is weaker than on US-format numbers.
    • Encrypt is supported, but this endpoint does not decrypt. Keep the caller-supplied key and the rewrites array and run Presidio's DeanonymizeEngine yourself, or use the PII Detector Server AMI (POST /decrypt).

    Measured performance

    • Evaluated on 1,505 held-out synthetic texts generated with presidio-evaluator (PresidioSentenceFaker). A prediction counts as correct when its character span overlaps the gold span of the same type (not an exact-span match).
    • Strongest types (F1): EMAIL_ADDRESS 1.00, IBAN_CODE 1.00, US_SSN 0.97, IP_ADDRESS 0.94, URL 0.91, CREDIT_CARD 0.86, PERSON 0.81.
    • Weaker types, not gated for this release: ORGANIZATION 0.52, LOCATION 0.48, DATE_TIME 0.37.
    • Local CPU container of this image: about 180 texts per second at a batch size of 25 (6 ms median for a single short sentence).
    • Live SageMaker realtime on ml.m5.large (single instance, sequential client, us-east-2): median 226 ms per short text (p90 234 ms); about 51 texts per second at a batch size of 25. Confirm on your endpoint if latency is tight.

    Highlights

    • Detect ~18 PII entity types - names, emails, SSNs, credit cards, IBANs, IPs, and more
    • Choose replace, mask, redact, hash, or encrypt independently per entity type
    • Runs entirely inside your VPC - text never leaves your AWS account

    Details

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    Pricing

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    Try this product free for 5 days according to the free trial terms set by the vendor.

    PII Detector Model

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    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.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (7)

     Info
    Dimension
    Description
    Cost/host/hour
    ml.m5.large Inference (Batch)
    Recommended
    Model inference on the ml.m5.large instance type, batch mode
    $0.10
    ml.m5.large Inference (Real-Time)
    Recommended
    Model inference on the ml.m5.large instance type, real-time mode
    $0.10
    ml.c5.large Inference (Real-Time)
    Model inference on the ml.c5.large instance type, real-time mode
    $0.10
    ml.c5.xlarge Inference (Batch)
    Model inference on the ml.c5.xlarge instance type, batch mode
    $0.10
    ml.c5.xlarge Inference (Real-Time)
    Model inference on the ml.c5.xlarge instance type, real-time mode
    $0.10
    ml.m5.xlarge Inference (Batch)
    Model inference on the ml.m5.xlarge instance type, batch mode
    $0.10
    ml.m5.xlarge Inference (Real-Time)
    Model inference on the ml.m5.xlarge instance type, real-time mode
    $0.10

    Vendor refund policy

    No refunds offered but you may cancel at any time

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    Usage information

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    Delivery details

    Amazon SageMaker model

    An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.

    Deploy the model on Amazon SageMaker AI using the following options:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    Initial release.

    • Detect ~18 default PII entity types (Microsoft Presidio + spaCy)
    • Per-entity-type operator: replace, mask, redact, hash, or encrypt
    • JSON and CSV input/output, real-time and batch transform
    • CPU only - no GPU instance tier

    Additional details

    Inputs

    Summary

    A JSON object with a "texts" array of strings to scan (or a CSV file, one text per line, for batch transform). Optional "entities" allowlist, "operators" map, and "language".

    Input MIME type
    application/json, text/csv
    {"texts": ["My name is Jamie Rivera, email test.user@example.com, phone 212-555-0100."]}
    My name is Jamie Rivera, email test.user@example.com, phone 212-555-0100.

    Input data descriptions

    The following table describes supported input data fields for real-time inference and batch transform.

    Field name
    Description
    Constraints
    Required
    texts
    List of text strings to scan for PII
    -
    Yes
    entities
    Optional allowlist of entity type names (for example ["EMAIL_ADDRESS", "US_SSN"])
    -
    No
    operators
    Optional per-entity-type action map (replace, mask, redact, hash, encrypt)
    -
    No
    language
    Optional language code (default en)
    -
    No

    Support

    Vendor support

    Support contact: Email: aws-support@sigmodata.com  Support URL: https://www.sigmodata.com 

    Support description: Support via email for deployment, configuration, and troubleshooting.

    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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