Classify Communications Automatically

Classify communications as they are created to ensure consistent governance across supervision, retention, and discovery workflows.
Top Three Reasons to Classify Data.
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Solution Overview
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What Is Communications Classification?

Communications classification is the process of categorizing business communications by applying labels, tags, and metadata based on defined policies. It evaluates content and context at ingestion to ensure consistent classification at creation, identifying attributes such as risk indicators, regulatory relevance, and business context.

By mapping these outcomes to supervision, retention, and discovery workflows, classification enables compliance with regulatory requirements within the Arctera Unified Platform.

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What Arctera Classification Does

check-circle: Applies policy-driven classification to communications during ingestion across email, messaging, voice, and collaboration channels.

check-circle: Uses 250+ preconfigured policies aligned to global regulations including GDPR, HIPAA, PCI, and financial services requirements.

check-circle: Detects sensitive data and behaviors using 1,400+ pre-trained patterns for PII, misconduct, and regulatory risk signals.

check-circle: Enriches communications with tags and metadata that drive supervision, retention, and discovery processes.

check-circle: Combines natural language processing, sentiment analysis, and language detection to classify content beyond keywords.

check-circle: Maintains explainable classification with transparent policies, confidence scoring, and validation tools, ensuring consistent outcomes across workflows without reprocessing data

A circular radar interface with various communication icons (email, chat, video) being scanned by a green radar beam, with a large orange warning triangle at the top.
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How Classification Works

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Step 1: Ingest Communications for Classification
Ingest communications from Capture as they enter the platform, including email, messaging, voice transcripts, and collaboration content, with associated metadata such as participants, timestamps, and attachments.
Step 2: Extract Content and Contextual Attributes
Analyze content and metadata to extract attributes such as language, sentiment, entities, participants, and message direction used for classification and downstream governance.
Step 3: Apply Classification Policies and Models
Evaluate communications against preconfigured and custom policies using patterns, keywords, proximity logic, and machine learning models to detect regulatory signals and risk indicators.
Step 4: Assign Classification Tags and Metadata
Apply classification tags and metadata when policy conditions are met, including categories, risk indicators, and relevance scoring with visibility into how classifications were determined.
Step 5: Enable Downstream Governance Workflows
Make classification results available across the platform to support archiving, surveillance, and eDiscovery workflows using the same underlying data without reprocessing.
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How Archive Works

Business Impact

Reduce manual review effort and noise

Focus reviewers on higher-risk communications by reducing irrelevant content through policy-driven classification and relevance scoring, cutting manual review effort by up to 67%.

Improve consistency of classification across the organization

Apply standardized policies, patterns, and tags across channels, business units, and regions to ensure communications are categorized consistently.

Strengthen regulatory defensibility

Provide transparent, policy-driven classification with visible logic and audit trails to support supervision, retention, and discovery decisions.

Enable policy-driven retention and data control

Classify communications by content and context to apply retention policies consistently, reduce over-retention, and manage data in line with regulatory requirements.

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Consistent Classification Across Policies and Data Sources

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Comprehensive Policy Coverage

Arctera Classification applies a policy-driven model with preconfigured and customizable policies aligned to global regulations and corporate standards. Coverage includes financial misconduct, data privacy (GDPR, HIPAA, PCI), and corporate compliance risks such as harassment, bribery, and intellectual property exposure.

Transparent, Intelligent Classification

Policies combine patterns, keywords, and proximity logic with NLP techniques such as sentiment analysis, language detection, and entity recognition. Transparent policies expose classification logic, allowing teams to review, adjust, and validate outcomes.

Unified Classification Across All Sources

A single taxonomy of policies, patterns, and tags is applied across email, messaging, voice, collaboration platforms, and file-based content. By combining rules-based and machine learning approaches, classification remains consistent without separate systems or reprocessing.

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Frequently Asked Questions

What is communications classification in a governance platform?
Communications classification is the process of categorizing communications by applying labels, tags, and metadata based on defined policies. Within a governance platform, classification evaluates both the content and context of communications, such as participants, channel, and message attributes, to determine how they should be governed. These classifications are used to support supervision, retention, and eDiscovery. Consistent classification ensures communications are handled in accordance with regulatory and organizational requirements across all channels.
How does Arctera classify communications and content?
Arctera classifies data by evaluating them against a library of preconfigured and custom policies. Each policy is composed of patterns, keywords, and proximity logic, and can incorporate natural language processing techniques such as sentiment analysis and language detection. When a communication meets the conditions of a policy, classification tags and metadata are applied. This process occurs as communications are ingested, enabling consistent and scalable classification across communication types. Both rules-based logic and machine learning models are used to improve accuracy and coverage.
What types of policies and risks can be detected?
Classification policies cover a wide range of regulatory and corporate risk scenarios. These include financial misconduct such as insider trading, market abuse, and off-channel communications, as well as data privacy requirements under regulations like GDPR, HIPAA, and PCI. Policies also address corporate compliance risks such as harassment, bribery, intellectual property exposure, and confidential information handling. Organizations can use built-in policies or create custom policies to address specific regulatory obligations or internal governance requirements.
What are transparent policies and why do they matter?
Transparent policies provide visibility into the logic used to classify communications, including the keywords, patterns, and conditions that trigger a classification. This allows organizations to inspect, validate, and modify classification criteria as needed. Transparent policies improve control over classification outcomes and help reduce false positives or missed signals. They also support defensibility by allowing organizations to demonstrate how classification decisions are made and maintained over time.
How are classification decisions audited and validated?
Classification decisions can be audited through visibility into policy logic and system activity. The platform maintains audit logs that track changes to policies, patterns, and tags, including timestamps and user activity. Organizations can review how specific classifications were applied and what conditions triggered them. This provides a clear record of classification behavior and supports regulatory reporting and internal validation processes.
How do policy or taxonomy updates affect existing data?
Classification is typically applied as communications are ingested, so new or updated policies apply to data moving forward. Existing data can be reclassified through reprocessing or reindexing workflows if needed. This allows organizations to apply updated policies or taxonomy changes to historical communications when requirements change. By separating classification logic from stored data, organizations can update governance policies without disrupting existing records or workflows.
Who defines and manages classification policies?
Classification policies are typically defined and managed by compliance, legal, and risk teams, with support from IT for configuration and deployment. Compliance teams often define policies based on regulatory obligations and risk scenarios, while legal teams may contribute requirements related to discovery and retention. IT teams ensure policies are implemented correctly within the platform and integrated across communication sources. The platform supports both preconfigured policies and custom policy creation, allowing organizations to tailor classification to their specific regulatory and business needs.
How does classification reduce false positives?
Classification reduces false positives by evaluating content using policy-driven logic and machine learning, rather than relying on keywords alone. It analyzes context through patterns, proximity logic, sentiment, and metadata such as participants and message direction to filter out common noise like disclaimers and newsletters. By categorizing content at ingestion, it enables more targeted review and improves signal quality.
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Apply Consistent Classification Across Your Communications

Understand how policy-driven classification is applied at ingestion to support supervision, retention, and discovery workflows.

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