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Overview

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connection-management.md content-analysis.md data-discovery.md data-profiling.md index.md job-management.md stored-info-types.md template-management.md

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tessl/pypi-google-cloud-dlp

Google Cloud Data Loss Prevention (DLP) API client library for discovering, classifying, and protecting sensitive data

Workspace: tessl
Visibility: Public
Created: 3 months ago
Last updated: 3 months ago
Describes: pkg:pypi/google-cloud-dlp@3.31.x

To install, run

npx @tessl/cli install tessl/pypi-google-cloud-dlp@3.31.0

Google Cloud DLP

Google Cloud Data Loss Prevention (DLP) API enables organizations to discover, classify, and protect sensitive data across their cloud and hybrid environments. It provides comprehensive content inspection, data transformation, risk analysis, and automated data discovery capabilities with extensive configuration options for compliance and privacy requirements.

Package Information

Package Name: google-cloud-dlp
Language: Python
Installation: pip install google-cloud-dlp

Core Imports

from google.cloud import dlp

For direct access to v2 API:

from google.cloud import dlp_v2

Basic Usage

from google.cloud import dlp

# Initialize the DLP client
client = dlp.DlpServiceClient()

# Basic content inspection
parent = f"projects/{project_id}/locations/global"
content_item = dlp.ContentItem(value="My SSN is 123-45-6789")

# Configure inspection
inspect_config = dlp.InspectConfig(
    info_types=[dlp.InfoType(name="US_SOCIAL_SECURITY_NUMBER")]
)

# Create request
request = dlp.InspectContentRequest(
    parent=parent,
    inspect_config=inspect_config,
    item=content_item,
)

# Inspect content
response = client.inspect_content(request=request)

# Process findings
for finding in response.result.findings:
    print(f"Found {finding.info_type.name}: {finding.quote}")

Architecture

The Google Cloud DLP API follows a service-oriented architecture with distinct functional areas:

Client Libraries: Synchronous and asynchronous clients for API interaction
Content Analysis: Real-time inspection, de-identification, and image redaction
Job Management: Long-running batch operations with triggers and scheduling
Data Discovery: Automated scanning and profiling of cloud data sources
Template System: Reusable configurations for inspection and transformation
Type System: Extensive type definitions for configuration and results

The API supports both immediate operations for small datasets and batch processing for enterprise-scale data protection workflows.

Capabilities

Content Analysis

Real-time analysis of text and images to detect, redact, and transform sensitive information. Supports immediate inspection with customizable info types and confidence levels.

def inspect_content(
    request: dlp.InspectContentRequest,
    *,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.InspectContentResponse: ...

def deidentify_content(
    request: dlp.DeidentifyContentRequest,
    *,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.DeidentifyContentResponse: ...

def redact_image(
    request: dlp.RedactImageRequest,
    *,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.RedactImageResponse: ...

Content Analysis

Template Management

Reusable configurations for inspection and de-identification operations. Templates standardize DLP policies across an organization and simplify repeated operations.

def create_inspect_template(
    request: dlp.CreateInspectTemplateRequest,
    *,
    parent: Optional[str] = None,
    inspect_template: Optional[dlp.InspectTemplate] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.InspectTemplate: ...

def create_deidentify_template(
    request: dlp.CreateDeidentifyTemplateRequest,
    *,
    parent: Optional[str] = None,
    deidentify_template: Optional[dlp.DeidentifyTemplate] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.DeidentifyTemplate: ...

Template Management

Job Management

Long-running batch operations for processing large datasets, including scheduled triggers, hybrid content inspection, and job lifecycle management.

def create_dlp_job(
    request: dlp.CreateDlpJobRequest,
    *,
    parent: Optional[str] = None,
    inspect_job: Optional[dlp.InspectJobConfig] = None,
    risk_job: Optional[dlp.RiskAnalysisJobConfig] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.DlpJob: ...

def create_job_trigger(
    request: dlp.CreateJobTriggerRequest,
    *,
    parent: Optional[str] = None,
    job_trigger: Optional[dlp.JobTrigger] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.JobTrigger: ...

Job Management

Data Discovery

Automated scanning and profiling of cloud data sources to understand data distribution, sensitivity, and compliance posture across BigQuery, Cloud Storage, Cloud SQL, and more.

def create_discovery_config(
    request: dlp.CreateDiscoveryConfigRequest,
    *,
    parent: Optional[str] = None,
    discovery_config: Optional[dlp.DiscoveryConfig] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.DiscoveryConfig: ...

Data Discovery

Data Profiling

Access to data profiles and insights generated by discovery scans, providing visibility into data sensitivity, distribution, and risk levels across projects, tables, columns, and file stores.

def get_project_data_profile(
    request: dlp.GetProjectDataProfileRequest,
    *,
    name: Optional[str] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.ProjectDataProfile: ...

def get_table_data_profile(
    request: dlp.GetTableDataProfileRequest,
    *,
    name: Optional[str] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.TableDataProfile: ...

def get_column_data_profile(
    request: dlp.GetColumnDataProfileRequest,
    *,
    name: Optional[str] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.ColumnDataProfile: ...

def get_file_store_data_profile(
    request: dlp.GetFileStoreDataProfileRequest,
    *,
    name: Optional[str] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.FileStoreDataProfile: ...

Data Profiling

Stored Info Types

Custom sensitive information detection patterns for organization-specific data types. Extends built-in detectors with custom dictionaries, regular expressions, and machine learning models.

def create_stored_info_type(
    request: dlp.CreateStoredInfoTypeRequest,
    *,
    parent: Optional[str] = None,
    config: Optional[dlp.StoredInfoTypeConfig] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.StoredInfoType: ...

Stored Info Types

Connection Management

External data source connections for accessing data outside Google Cloud, including database connections, cloud storage from other providers, and hybrid environments.

def create_connection(
    request: dlp.CreateConnectionRequest,
    *,
    parent: Optional[str] = None,
    connection: Optional[dlp.Connection] = None,
    retry: OptionalRetry = gapic_v1.method.DEFAULT,
    timeout: Union[float, object] = gapic_v1.method.DEFAULT,
    metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> dlp.Connection: ...

Connection Management

Core Types

Client Classes

class DlpServiceClient:
    """Synchronous client for Google Cloud DLP service operations."""
    
    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Optional[DlpServiceTransport] = None,
        client_options: Optional[ClientOptions] = None,
        client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
    ) -> None: ...

class DlpServiceAsyncClient:
    """Asynchronous client for Google Cloud DLP service operations."""
    
    def __init__(
        self,
        *,
        credentials: Optional[ga_credentials.Credentials] = None,
        transport: Optional[DlpServiceAsyncTransport] = None,
        client_options: Optional[ClientOptions] = None,
        client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
    ) -> None: ...

Core Data Types

class ContentItem:
    """Container for content to be inspected."""
    
    value: str
    table: Table
    byte_item: ByteContentItem

class InfoType:
    """Type of information detector."""
    
    name: str
    version: str
    sensitivity_score: SensitivityScore

class Finding:
    """Detected sensitive information."""
    
    info_type: InfoType
    likelihood: Likelihood
    location: Location
    quote: str
    quote_info: QuoteInfo

class InspectConfig:
    """Configuration for content inspection."""
    
    info_types: Sequence[InfoType]
    min_likelihood: Likelihood
    limits: InspectConfig.FindingLimits
    include_quote: bool
    exclude_info_types: bool

Transformation Types

class DeidentifyConfig:
    """Configuration for content de-identification."""
    
    info_type_transformations: InfoTypeTransformations
    record_transformations: RecordTransformations
    transformation_error_handling: TransformationErrorHandling

class PrimitiveTransformation:
    """Basic data transformation operations."""
    
    replace_config: ReplaceValueConfig
    redact_config: RedactConfig
    character_mask_config: CharacterMaskConfig
    crypto_replace_ffx_fpe_config: CryptoReplaceFfxFpeConfig
    fixed_size_bucketing_config: FixedSizeBucketingConfig
    bucketing_config: BucketingConfig
    replace_dictionary_config: ReplaceDictionaryConfig
    time_part_config: TimePartConfig
    crypto_hash_config: CryptoHashConfig
    date_shift_config: DateShiftConfig
    crypto_deterministic_config: CryptoDeterministicConfig

Enumeration Types

class Likelihood(proto.Enum):
    """Likelihood levels for detection confidence."""
    
    LIKELIHOOD_UNSPECIFIED = 0
    VERY_UNLIKELY = 1
    UNLIKELY = 2
    POSSIBLE = 3
    LIKELY = 4
    VERY_LIKELY = 5

class FileType(proto.Enum):
    """Supported file types for processing."""
    
    FILE_TYPE_UNSPECIFIED = 0
    BINARY_FILE = 1
    TEXT_FILE = 2
    IMAGE = 3
    WORD = 5
    PDF = 6
    AVRO = 7
    CSV = 8
    TSV = 9
    POWERPOINT = 11
    EXCEL = 12

Version

Tile

Files

tessl/pypi-google-cloud-dlp

To install, run

index.md.css-3qkkll{font-size:var(--chakra-font-sizes-sm);font-weight:var(--chakra-font-weights-normal);color:var(--chakra-colors-gray-300);}docs/

Google Cloud DLP

Package Information

Core Imports

Basic Usage

Architecture

Capabilities

Content Analysis

Template Management

Job Management

Data Discovery

Data Profiling

Stored Info Types

Connection Management

Core Types

Client Classes

Core Data Types

Transformation Types

Enumeration Types

index.mddocs/