Data Discovery

• 1: Introduction
• 2: What's New
• 3: General Architecture
• 4: APIs
• 4.1: Classify
• 4.1.1: Classify Text API
• 4.1.2: Classify CSV API
• 4.2: Transform
• 4.2.1: Label Text API
• 4.2.1.1: Handling Overlapping Conflicts
• 4.2.1.2: Sample Response Default
• 4.2.1.3:
• 4.2.1.4:
• 4.3: Input Validation
• 4.4: Harmonizing Provider Outputs
• 4.5: Supported Classification Entities
• 4.6:
• 4.7:
• 4.8:
• 4.9:
• 5: Performance and Accuracy

1 - Introduction

In an era where data privacy is paramount, safeguarding sensitive information in unstructured data has become critical—especially for organizations leveraging AI and machine learning technologies. Data Discovery is a powerful, developer-friendly product designed specifically to address this challenge.

Data Discovery’s Classification Service specializes in the detection of Personally Identifiable Information (PII), Protected Health Information (PHI), Payment Card Information (PCI) within free-text (unstructured) and table-based (structured. CSV) inputs. Unlike traditional data tools, it excels in dynamic, unstructured environments such as chatbot conversations, call transcripts, and Generative AI (Gen AI) outputs.

Harnessing a hybrid detection engine that combines machine learning and rule-based algorithms, Data Discovery offers unparalleled accuracy and flexibility. It empowers teams to perform the following:

• Automate chatbot redaction to ensure compliance with privacy regulations.

• Perform transcript cleanup for customer service, healthcare, and financial industries.

• Enhance GenAI applications by proactively mitigating the risks associated with leaking sensitive information.

Built for developers, architects, and privacy engineers, Data Discovery seamlessly integrates into AI/ML pipelines and Gen AI workflows. Deployment is fast and flexible, with support for both Docker containers and AWS EKS clusters, and interaction via robust, intuitive REST APIs.

Whether you’re building next-generation AI applications or enhancing existing systems to meet evolving data privacy standards, Data Discovery equips you with the tools to discover, classify, and protect sensitive information at scale.

2 - What's New

3 - General Architecture

The main components of the Protegrity Data Discovery product are as follows:

• Classification service: The Classification Service serves as the primary access point for all classification-related interactions. It orchestrates various back-end components known as Providers, which are responsible for executing the actual classification tasks.

• Pattern and Context classification providers: The Providers function as specialized modules in identifying and classifying Personally Identifiable Information (PII). They analyze input data to detect, classify, and locate sensitive information.

The Pattern classification provider is a rule-based system that identifies PII using predefined patterns and heuristics. It is fast, customizable, and suitable for structured data with known formats.

The Context classification provider is an LLM based designed within Protegrity. A machine learning model that detects PII using context and semantics. It is flexible, effective with unstructured data, and adapts to varied patterns.

The general architecture is illustrated in the following figure.

4 - APIs

4.1 - Classify

4.1.1 - Classify Text API

POST http://{Host Address}/pty/data-discovery/v1.1/classify

Query Parameters

score_threshold

• Type: float
• Description: Optional. Exclude results with a score lower than this threshold.
• Values: Minimum 0, Maximum 1.0
• Default: 0.00

Body

• Content type must be a plain text and in an UTF-8 format.

• Length of the body is limited to 10K Bytes.

Sample Request

• Sample Request
• Curl
• Python
• Postman

curl -X POST "http://<SERVER_IP>/pty/data-discovery/v1.1/classify?score_threshold=0.85" \
          -H "Content-Type: text/plain" \
          --data "You can reach Dave Elliot by phone 203-555-1286"

import requests
    
    url = "http://<SERVER_IP>/pty/data-discovery/v1.1/classify"
    params = {"score_threshold": 0.85}
    headers = {"Content-Type": "text/plain"}
    data = "You can reach Dave Elliot by phone 203-555-1286"
    
    response = requests.post(url, params=params, headers=headers, data=data, verify=False)
    
    print("Status code:", response.status_code)
    print("Response JSON:", response.json())

URL: POST `http://<SERVER_IP>/pty/data-discovery/v1.1/classify`
   Query Parameters:
   -score_threshold (optional), float between 0.0 and 1.0, default: 0.
   Headers:
   -Content-Type: text/plain
   Body:
   -You can reach Dave Elliot by phone 203-555-1286

Sample Response

{
    "providers": [
        {
            "name": "Pattern Classification Provider",
            "version": "1.1.1",
            "status": 200,
            "elapsed_time": 0.028261899948120117,
            "config_provider": {
                "name": "Pattern",
                "address": "http://pattern_provider_service:8051",
                "supported_content_types": []
            }
        },
        {
            "name": "Context Classification Provider",
            "version": "1.1.1",
            "status": 200,
            "elapsed_time": 0.040960073471069336,
            "config_provider": {
                "name": "Context",
                "address": "http://context_provider_service:8052",
                "supported_content_types": []
            }
        }
    ],
    "classifications": {
        "PERSON": [
            {
                "score": 0.9238499879837037,
                "location": {
                    "start_index": 14,
                    "end_index": 25
                },
                "classifiers": [
                    {
                        "provider_index": 0,
                        "name": "SpacyRecognizer",
                        "score": 0.85,
                        "original_entity": "PERSON",
                        "details": {}
                    },
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9976999759674072,
                        "original_entity": "NAME",
                        "details": {}
                    }
                ]
            }
        ],
        "PHONE_NUMBER": [
            {
                "score": 0.9995999932289124,
                "location": {
                    "start_index": 35,
                    "end_index": 47
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9995999932289124,
                        "original_entity": "PHONE",
                        "details": {}
                    }
                ]
            }
        ]
    }
}

Response Fields Description

Providers Section

Classifications Section

Response Codes

4.1.2 - Classify CSV API

POST http://{Host Address}/pty/data-discovery/v1.1/classify

Query Parameters

score_threshold

• Type: float
• Description: Optional. Exclude results with a score lower than this threshold.
• Values: Minimum 0, Maximum 1.0
• Default: 0.00

has_headers

• Type: boolean
• Description: Optional. Indicates whether the first row represents the column header.
• Values: true/false
• Default: true

column_delimiter

• Type: char
• Description: Optional. Delimiter to separate the columns.
• Values: , |
• Default: ,

quote_char

• Type: char
• Description: Optional. Character to quote fields containing special characters, such as, the column_delimiter or new-line characters.
• Values: ""

Body

• Content type should be text/csv and in UTF-8 format.

• Body size is limited to 10K Bytes

Sample Request

• Sample Request:
• Curl
• Python
• Postman

curl -X POST "http://<SERVER_IP>/pty/data-discovery/v1.1/classify?score_threshold=0.85" \
     --header 'Content-Type: text/csv' \
     --data-raw 'Social Security Number,Credit Card Number,IBAN,Phone Number
     589-25-1068,349384370543801,FR43 9255 4858 47BG 3EBG U4OK O18,(483) 9440301
     636-36-3077,4041594844904,AL50 8947 4215 KAEY GAPM NLYC FNZG,(113) 5143119
     748-82-2375,3558175715821800,AT34 4082 9269 0841 5702,(763) 5136237
     516-62-9861,560221027976015000,FR22 0068 7181 11FB UG8H ECEM 306,(726) 6031636
     121-49-9409,374283320982549,DK37 5687 8459 8060 79,(624) 9205200
     838-73-3299,5558216060144900,CR54 8952 8144 6403 4765 0,(356) 9479541
     439-11-5310,5048376143641900,RS76 6213 4824 0184 8983 74,(544) 5623326
     564-06-8466,3543299511845640,EE51 6882 3443 7863 4703,(702) 6093849
     518-54-5443,3543019452249540,IT65 D000 3874 2801 Z15I LNLL OOX,(584) 8618371'

import requests
    
    url = "http://<SERVER_IP>/pty/data-discovery/v1.1/classify"
    params = {"score_threshold": 0.85}
    headers = {"Content-Type": "text/csv"}
    data = """Social Security Number,Credit Card Number,IBAN,Phone Number
    589-25-1068,349384370543801,FR43 9255 4858 47BG 3EBG U4OK O18,(483) 9440301
    636-36-3077,4041594844904,AL50 8947 4215 KAEY GAPM NLYC FNZG,(113) 5143119
    748-82-2375,3558175715821800,AT34 4082 9269 0841 5702,(763) 5136237
    516-62-9861,560221027976015000,FR22 0068 7181 11FB UG8H ECEM 306,(726) 6031636
    121-49-9409,374283320982549,DK37 5687 8459 8060 79,(624) 9205200
    838-73-3299,5558216060144900,CR54 8952 8144 6403 4765 0,(356) 9479541
    439-11-5310,5048376143641900,RS76 6213 4824 0184 8983 74,(544) 5623326
    564-06-8466,3543299511845640,EE51 6882 3443 7863 4703,(702) 6093849
    518-54-5443,3543019452249540,IT65 D000 3874 2801 Z15I LNLL OOX,(584) 8618371
    """
    
    response = requests.post(url, params=params, headers=headers, data=data, verify=False)
    
    print("Status code:", response.status_code)
    try:
        print("Response JSON:", response.json())
    except ValueError:
        print("Response Text:", response.text)
    

URL: POST `http://<SERVER_IP>/pty/data-discovery/v1.1/classify`
      Query Parameters:
      -score_threshold (optional), float between 0.0 and 1.0, default: 0.
      -has_headers (optional), Indicates whether the first row represents the column header.
      -column_delimiter (optional), Delimiter to separate the columns.
      -quote_char (optional), Character to quote fields containing special characters, such as, the column_delimiter or new-line characters.
      Headers:
      -Content-Type: text/csv
      Body:
      -Social Security Number,Credit Card Number,IBAN,Phone Number
     589-25-1068,349384370543801,FR43 9255 4858 47BG 3EBG U4OK O18,(483) 9440301
     636-36-3077,4041594844904,AL50 8947 4215 KAEY GAPM NLYC FNZG,(113) 5143119
     748-82-2375,3558175715821800,AT34 4082 9269 0841 5702,(763) 5136237
     516-62-9861,560221027976015000,FR22 0068 7181 11FB UG8H ECEM 306,(726) 6031636
     121-49-9409,374283320982549,DK37 5687 8459 8060 79,(624) 9205200
     838-73-3299,5558216060144900,CR54 8952 8144 6403 4765 0,(356) 9479541
     439-11-5310,5048376143641900,RS76 6213 4824 0184 8983 74,(544) 5623326
     564-06-8466,3543299511845640,EE51 6882 3443 7863 4703,(702) 6093849
     518-54-5443,3543019452249540,IT65 D000 3874 2801 Z15I LNLL OOX,(584) 8618371
   

Sample Response

{
    "providers": [
        {
            "name": "Pattern Classification Provider",
            "version": "1",
            "status": 200,
            "elapsed_time": 0.31273603439331055,
            "config_provider": {
                "name": "Pattern",
                "address": "http://pattern_provider_service:8051",
                "supported_content_types": []
            }
        },
        {
            "name": "Context Classification Provider",
            "version": "1.1.1",
            "status": 200,
            "elapsed_time": 1.1383004188537598,
            "config_provider": {
                "name": "Context",
                "address": "http://context_provider_service:8052",
                "supported_content_types": []
            }
        }
    ],
    "classifications": {
        "SOCIAL_SECURITY_ID": [
            {
                "score": 0.9994888835483127,
                "rows_processed": 9,
                "location": {
                    "column_name": "Social Security Number",
                    "column_index": 0
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "rows_with_classification": 9,
                        "total_classifications": 9,
                        "score": 0.9994888835483127,
                        "details": {}
                    }
                ]
            }
        ],
        "CREDIT_CARD": [
            {
                "score": 0.9986333317226834,
                "rows_processed": 9,
                "location": {
                    "column_name": "Credit Card Number",
                    "column_index": 1
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "rows_with_classification": 9,
                        "total_classifications": 9,
                        "score": 0.9986333317226834,
                        "details": {}
                    }
                ]
            }
        ],
        "BANK_ACCOUNT": [
            {
                "score": 0.7901234567901234,
                "rows_processed": 9,
                "location": {
                    "column_name": "IBAN",
                    "column_index": 2
                },
                "classifiers": [
                    {
                        "provider_index": 0,
                        "name": "IbanRecognizer",
                        "rows_with_classification": 8,
                        "total_classifications": 8,
                        "score": 0.8888888888888888,
                        "details": {}
                    }
                ]
            }
        ],
        "PHONE_NUMBER": [
            {
                "score": 0.9961333341068692,
                "rows_processed": 9,
                "location": {
                    "column_name": "Phone Number",
                    "column_index": 3
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "rows_with_classification": 9,
                        "total_classifications": 9,
                        "score": 0.9961333341068692,
                        "details": {}
                    }
                ]
            }
        ]
    }
}

Response Fields Description

Providers Section

Classifications Section

Response Codes

4.2 - Transform

4.2.1 - Label Text API

POST http://{Host Address}/pty/data-discovery/v1.1/transform/label

Query Parameters

score_threshold

• Type: float
• Description: Optional. Label results where the score is greater than this threshold.
• Values: Minimum 0, Maximum 1.0
• Default: 0.7

include_providers

• Type: binary
• Description: Optional. Include details of the service providers in the response.
• Values: Yes / No
• Default: No

include_classification_details

• Type: binary
• Description: Optional. Include classification details in the response.
• Values: Yes / No
• Default: No

Body

• Content type must be text/plain and in UTF-8 format.

• Body size is limited to 10K Bytes

Sample Request

• Sample Request:
• Curl
• Python
• Postman

curl -X POST "http://<SERVER_IP>/pty/data-discovery/v1.1/transform/label?score_threshold=0.85" \
          -H "Content-Type: text/plain" \
          --data "Jake lives at 15 Main st, Hamden 06517, Connecticut."

import requests
    
    url = "http://<SERVER_IP>/pty/data-discovery/v1.1/transform/label"
    params = {"score_threshold": 0.85}
    headers = {"Content-Type": "text/plain"}
    data = "Jake lives at 15 Main st, Hamden 06517, Connecticut."
    
    response = requests.post(url, params=params, headers=headers, data=data, verify=False)
    
    print("Status code:", response.status_code)
    print("Response JSON:", response.json())

URL: POST `http://<SERVER_IP>/pty/data-discovery/v1.1/transform/label`
   Query Parameters:
   -score_threshold (optional), float between 0.0 and 1.0, default: 0.
   Headers:
   -Content-Type: text/plain
   Body:
   -Jake lives at 15 Main st, Hamden 06517, Connecticut.

Sample Responses

• Sample Response:
• Default

{
    "transform": {
        "text": "[PERSON] lives at [LOCATION] [LOCATION], [LOCATION] [LOCATION], [LOCATION]."
    },
    "providers": [
        {
            "name": "Pattern Classification Provider",
            "version": "1.1.1",
            "status": 200,
            "elapsed_time": 0.011328935623168945,
            "config_provider": {
                "name": "Pattern",
                "address": "http://pattern_provider_service:8051",
                "supported_content_types": []
            }
        },
        {
            "name": "Context Classification Provider",
            "version": "1.1.1",
            "status": 200,
            "elapsed_time": 0.03895401954650879,
            "config_provider": {
                "name": "Context",
                "address": "http://context_provider_service:8052",
                "supported_content_types": []
            }
        }
    ],
    "classifications": {
        "LOCATION": [
            {
                "score": 0.85,
                "location": {
                    "start_index": 17,
                    "end_index": 24
                },
                "classifiers": [
                    {
                        "provider_index": 0,
                        "name": "SpacyRecognizer",
                        "score": 0.85,
                        "original_entity": "LOCATION",
                        "details": {}
                    }
                ]
            },
            {
                "score": 0.9240000128746033,
                "location": {
                    "start_index": 26,
                    "end_index": 32
                },
                "classifiers": [
                    {
                        "provider_index": 0,
                        "name": "SpacyRecognizer",
                        "score": 0.85,
                        "original_entity": "LOCATION",
                        "details": {}
                    },
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9980000257492065,
                        "original_entity": "CITY",
                        "details": {}
                    }
                ]
            },
            {
                "score": 0.9244499981403351,
                "location": {
                    "start_index": 40,
                    "end_index": 51
                },
                "classifiers": [
                    {
                        "provider_index": 0,
                        "name": "SpacyRecognizer",
                        "score": 0.85,
                        "original_entity": "LOCATION",
                        "details": {}
                    },
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9988999962806702,
                        "original_entity": "STATE",
                        "details": {}
                    }
                ]
            },
            {
                "score": 0.9958999752998352,
                "location": {
                    "start_index": 14,
                    "end_index": 16
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9958999752998352,
                        "original_entity": "BUILDING",
                        "details": {}
                    }
                ]
            },
            {
                "score": 0.9983999729156494,
                "location": {
                    "start_index": 33,
                    "end_index": 38
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.9983999729156494,
                        "original_entity": "ZIPCODE",
                        "details": {}
                    }
                ]
            }
        ],
        "PERSON": [
            {
                "score": 0.8819000124931335,
                "location": {
                    "start_index": 0,
                    "end_index": 4
                },
                "classifiers": [
                    {
                        "provider_index": 1,
                        "name": "context",
                        "score": 0.8819000124931335,
                        "original_entity": "NAME",
                        "details": {}
                    }
                ]
            }
        ]
    }
}

Response Codes

4.2.1.1 - Handling Overlapping Conflicts

While classifying data, the providers may label an identical text under two different entities. This distinction arises from the detection strategies the classifiers adopt. Data Discovery handles these conflicts by applying certain rules on these conflicting entities.

The rules for handling the conflicting entities are as follows:

• No overlap: If the two entities do not conflict, retain the results in the original form.

  For example, Jake Filbert lives in Connecticut. If only Jake Filbert is identified, the result will be labeled as [NAME] lives in Connecticut.

• Full overlap: If both the entities overlap, the following logic will be applied:

  • Select the entity with a higher confidence score.
  • If both the entities contain the same confidence score, select the first entity.

  For example, Jake Filbert lives in Connecticut. Here, the name is recognized as [USER] with a score 0.7 and [NAME] with a score 0.9. As [NAME] has a higher score, the result will be labeled as [NAME] lives in Connecticut.

• One entity contained in other: If one entity is completely contained in the other, select the entity with the longer text.

  For example, jake@email.com. Here, the classifiers may recognize the text as [NAME] and [EMAIL]. As [EMAIL] is the longer text, the result will be labeled as [EMAIL].

• Partial intersection. If the two entities overlap partially, the result will be a combination of both.

  For example, 092-33445. Here, the classifiers may recognize the text as [PHONE_NUMBER] and [SSN]. The result will be labeled as [PHONE_NUMBER&SSN].

4.2.1.2 - Sample Response Default

The fields are described as follows:

4.2.1.3 -

4.2.1.4 -

4.3 - Input Validation

The Classification service in Data Discovery offers an input validation security feature that rejects invalid input data. Data that is malformed, non-normalized, containing homoglyphs, hieroglyphs, mixed Unicode variants, or control characters is considered as unsanitized or invalid data. These are rejected and will not be classified.

The following are few examples of data that will be rejected:

• Ⅷ
• 𝓉𝑒𝓍𝓉
• Ｐｅｐ

Before invoking the Classification endpoint, ensure that the input text is normalized. Replace invalid characters by their corresponding normalized plaintext characters. If the input text contains any invalid character, a status code of 422 and a message Untrusted input is returned.

For security purposes, the application rejects unsanitized data by default. It is recommended that this feature remains enabled. However, to override this feature, perform the following steps.

• On Docker Compose
• On EKS

- SECURITY_SETTINGS={"ENABLE_ALL_SECURITY_CONTROLS":false}
  

docker compose down
  

docker compose up -d
  

securitySettings:
    ENABLE_ALL_SECURITY_CONTROLS: false
  

helm uninstall data-discovery-classification --namespace default --wait
  

helm install data-discovery-classification . \
    --namespace default \
    --create-namespace \
    --wait \
    --wait-for-jobs \
    --timeout 900s \
    -f values-override.yaml
  

4.4 - Harmonizing Provider Outputs

Based on the detection logic, the Pattern and Context classification providers might classify the same data in different labels. The classification service standardizes provider outputs into a unified response.

Consider the example, You can visit our office located in New York City.

• Context provider might categorize New York City as CITY.
• Pattern provider might categorize New York City as LOCATION.

This can cause an inconsistency in the outputs generated across the providers.

Data Discovery ensures standardization of responses by aggregating similar outputs of the providers under a common classification name. In the example shown, the classification service will categorize New York City under the category LOCATION.

For a complete reference, see the supported classification entities and their harmonization categories.

Harmonization Process

The following pointers illustrate the harmonization process in detail.

Providers Mapping Entities

Each provider is responsible for mapping its identified entities to harmonized classification entities that are consistent with those used by other providers. This ensures that the classification service can accurately aggregate and interpret responses across multiple providers. When a provider’s classification is harmonized, the response must include the originally identified entity alongside the harmonized classification.

The following snippet shows how the Context classification provider initially classified the entity as CITY, which was then harmonized into the category LOCATION.

{
  "providers": "...",
  "classifications": {
    "LOCATION": [
      {
        "score": 0.9222000122070313,
        "location": {
          "start_index": 36,
          "end_index": 49
        },
        "classifiers": [
          {
            "provider_index": 0,
            "name": "SpacyRecognizer",
            "score": 0.85,
            "original_entity": "LOCATION",
            "details": {}
          },
          {
            "provider_index": 1,
            "name": "context",
            "score": 0.9944000244140625,
            "original_entity": "CITY",
            "details": {}
          }
        ]
      }
    ]
  }
}

Grouping by Matching Indexes

The entities are grouped together only if the responses shared by the providers contain the same start_index, end_index, and similar classification entity. If the start_index and end_index differ, the entities will not be grouped together.

As shown in the following snippet, the Context and Pattern providers classify the data as IT_IDENTITY_CARD and ID_CARD respectively. These are then grouped under the NATIONAL_ID category by the classification service.

{
  "providers": ...,
  "classifications": {
    "NATIONAL_ID": [
      {
        "score": 0.9236000061035157,
        "location": {
          "start_index": 14,
          "end_index": 25
        },
        "classifiers": [
          {
            "provider_index": 0,
            "name": "pattern_classification",
            "score": 0.85,
            "original_entity": "IT_IDENTITY_CARD" 
          }, {
            "provider_index": 1,
            "name": "context_classification",
            "score": 0.9972000122070312,
            "original_entity": "ID_CARD" 
          }
        ]
      }
    ]
  }
}

Non-Matching Indexes

If the responses for start_index and end_index differ, the entities will not be grouped together. However, the entities will appear under a common classification name.

The following table illustrates a common classification name for multiple providers.

The following snippet illustrates the sample.

{
  "providers": "...",
  "classifications": {
    "LOCATION": [
      {
        "score": 0.9236000061035157,
        "location": {
          "start_index": 0,
          "end_index": 35
        },
        "classifiers": [
          {
            "provider_index": 0,
            "name": "pattern_provider",
            "score": 0.85,
            "original_entity": "LOCATION"
          }
        ]
      },
      {
        "score": 0.9236000061035157,
        "location": {
          "start_index": 0,
          "end_index": 17
        },
        "classifiers": [
          {
            "provider_index": 1,
            "name": "context_provider",
            "score": 0.9972000122070312,
            "original_entity": "STREET"
          }
        ]
      },
      {
        "score": 0.9236000061035157,
        "location": {
          "start_index": 20,
          "end_index": 22
        },
        "classifiers": [
          {
            "provider_index": 1,
            "name": "context_provider",
            "score": 0.9972000122070312,
            "original_entity": "BUILDING"
          }
        ]
      },
      {
        "score": 0.9236000061035157,
        "location": {
          "start_index": 25,
          "end_index": 31
        },
        "classifiers": [
          {
            "provider_index": 1,
            "name": "context_provider",
            "score": 0.9972000122070312,
            "original_entity": "ZIP_CODE"
          }
        ]
      }
    ]
  }
}

4.5 - Supported Classification Entities

Supported Entity Types

PII entities supported by Data Discovery with their Harmonized Categories.

4.6 -

4.7 -

4.8 -

1. Navigate to the docker_compose directory.

2. Edit the docker-compose.yaml file.

3. Under the environment section of classification_service, append the security parameter as follows.

- SECURITY_SETTINGS={"ENABLE_ALL_SECURITY_CONTROLS":false}

4. Save the changes.

5. If the application is already running, stop the containers first:

docker compose down

6. Start the application with your configuration changes following the Docker Compose deployment guide:

docker compose up -d

4.9 -

1. Navigate to the /eks/helm/classification_app directory.

2. Create a values-override.yaml file with the required custom configuration.

securitySettings:
  ENABLE_ALL_SECURITY_CONTROLS: false

3. Save the changes.

4. If the application is already deployed, uninstall using the following command.

helm uninstall data-discovery-classification --namespace default --wait

5. Run the following installation command.

helm install data-discovery-classification . \
  --namespace default \
  --create-namespace \
  --wait \
  --wait-for-jobs \
  --timeout 900s \
  -f values-override.yaml

5 - Performance and Accuracy

Introduction

Performance and accuracy are critical metrics for data discovery tools. These ensure that large datasets can be processed swiftly and sensitive information is correctly identified. High performance minimizes latency and maximizes productivity, while accuracy reduces the risk of data breaches and ensures compliance with regulatory standards like GDPR and CCPA.

Together, these qualities are essential for maintaining data integrity and security in environments where unstructured data flows through various systems..

Performance Evaluation

The evaluation included Data Discovery deployed on Amazon EKS using a Helm Chart. The primary goal was to validate the application’s scalability and the infrastructure’s ability to handle varying loads under real-world conditions. Nevertheless, performance will vary between applications due to confounding variations in customer use cases. The key findings are as follows:

• Scalability: The application and infrastructure configurations can efficiently scale to meet usage demands and support parallel service calls.

• Instance Type: The m5.large8 instance was identified as a well-balanced choice for performance and cost.

  • If the priority is Faster Response Times: Splitting messages into smaller chunks and processing them in parallel is more cost-effective with multiple weaker instance types.
  • If the priority is Maximizing Processing Efficiency: Merging content into a single, larger request and using more powerful instance types is better for maximizing Processing Efficiency (characters processed per second).

• EKS Auto Mode: Running EKS in auto mode offers a fully managed Kubernetes cluster with minimal maintenance. This enables the service to self-regulate by automatically scaling up or down based on demand.

• Optimized CPU Usage: Maintain low CPU reservation for accurate measurement and effective self-regulation via the Horizontal Pod Autoscaler (HPA) that adjusts based on CPU usage percentage, balancing throughput, and idle time.

Detection Accuracy

Protegrity Data Discovery employs sophisticated Machine Learning (ML) and Natural Language Processing (NLP) technologies to achieve high accuracy in identifying sensitive data. The system processes English text inputs, with an NLP model pinpointing text spans within the document that correspond to various PII elements. The output includes text span as a PII entity, along with the entity type, entity position (start and end), and a confidence score. This confidence score reflects the likelihood of the text span being a PII entity, ensuring precise detection.

Dataset

Diverse datasets containing PII data, which differ based on demographic composition (volume and diversity), variations in data characteristics, types of labels, and other influencing factors were utilized. For example, labels such as “PERSON” and “PHONE_NUMBER” are used. The overall accuracy for detecting various PII data combinations in the dataset was measured with detection rate exceeding 96%.

Accuracy

Defined as an average of detection rates across sentences in a given text data.

Detection Rate = Valid Detections/Ground Truth

Where, Valid Detections is the number of correctly detected PII and Ground Truth is the total number of PIIs.

The variability in customer applications introduces differences in performance, meaning detection accuracy may fluctuate based on the quality of input text. Error rates in identifying PII are influenced not just by the detection service but also by customer workflows and evaluation datasets. It is recommended that customers assess and validate accuracy according to their specific use cases and requirements. It is also pertinent to note that the detected score of the input text may vary negligibly from user to user based on their underlying hardware configuration.