Search <book_title>...

Veritas Alta™ Surveillance User Guide

Last Published: 2024-07-22

Product(s): Veritas Alta Surveillance (1.0)

Introducing Veritas Alta Surveillance
1. About Alta Surveillance
2. Alta Surveillance multi-tier architecture
3. System requirements
4. Sampling support for content sources
5. AI-based label predictions support for efficient review process
6. About Alta Surveillance system security
Getting started
1. Signing in to Alta Surveillance
2. Signing out from Alta Surveillance
3. Launching Veritas Alta Archiving applications
4. Resetting a forgotten password
Working with dashboard widgets
1. Understanding the Dashboard page
2. Viewing status summary of recently reviewed departments
3. Pinning and unpinning departments to view review status
4. Changing the order of pinned departments
5. Viewing the review status summary of escalated items
6. Viewing a summary of searches and exports
Managing employee groups
1. Managing employee groups
Managing departments
1. About departments
2. Understanding the Departments page
3. Searching departments
4. Creating departments
5. Moving existing departments under other departments
6. Adding monitored employees and employee groups to departments
7. Editing monitoring policies
8. Editing department details and monitoring policy
9. Managing exception employees
10. Designating employees as exception employee
11. Assigning further exception reviewers to an exception employee
12. Removing exception status
13. Removing exception reviewers
Managing department users
1. Assigning users to departments
2. Removing users from departments
3. Adding new roles for users
4. Removing roles
5. Managing role assignment for a user in departments
  1. Assigning departments and exceptions to specific users
  2. Removing a specific role to users in one or more departments and exceptions
Managing department-level searches
1. About department-level searches
2. Guidelines for effective searches
3. Creating and running department-level searches
4. Disabling scheduled searches
5. Using proximity searches
6. Using fuzzy searches
7. Previewing search results
8. Accepting search results
9. Rejecting a search result
10. Resubmitting a search
Managing department-specific hotword sets
1. Overview
2. Creating department-specific hotword sets
3. Editing department-specific hotwords and hotword sets
4. Deleting department-specific hotword sets
Managing department-specific labels
1. Searching department-specific labels, label groups, and single choice groups
2. Managing department-specific labels
3. Managing department-specific label groups
4. Managing department-specific single choice label groups
Managing department-specific trash rules
1. Overview
2. Creating department-specific trash rules
3. Activating department-specific trash rules
4. Deactivating department-specific trash rules
5. Propagating department-specific trash rules
6. Unpropagating department-specific trash rules
Managing department-specific allowlist rules
1. Overview
2. Creating department-specific allowlist rules
3. Editing department-specific allowlist rules
Managing department-specific review comments
1. About department-level review comments
2. Adding department-level review comments
3. Editing department-level review comments
4. Deleting department-level review comments
5. Updating order of department-level review comments
Viewing employees associated with departments
1. Viewing employee association history
Managing users, roles, and permissions
1. Overview
2. Predefined user roles and permissions
3. Adding new roles for users (employees) and employee groups
4. Editing user roles and permissions
5. Deleting user roles
6. Assigning Alta Surveillances to users (employees) and employee groups
7. Restricting users to use hotwords in searches
8. Removing a user role
Managing application-level searches
1. About application-level searches
2. Viewing existing application-level searches
3. Creating and running application-level searches
4. Editing application-level searches
5. Excluding departments from application searches
6. Reinstating the excluded department for application searches
Managing application-specific hotword sets
1. Overview
2. Creating application-specific hotword sets
3. Editing application-specific hotwords and hotword sets
4. Deleting application-specific hotword sets
Managing application-specific labels
1. Searching application-specific labels, label groups, and single choice groups
2. Managing application-specific labels
3. Managing application-specific label groups
4. Managing application-specific single choice label groups
Managing application-specific trash rules
1. Overview
2. Creating application-specific trash rules
3. Activating application-specific trash rules
4. Deactivating application-specific trash rules
5. Propagating application-specific trash rules
6. Unpropagating application-specific trash rules
Managing application-specific allowlist rules
1. Overview
2. Creating application-specific allowlist rules
3. Editing application-specific allowlist rules
Managing application-specific review comments
1. About application-level review comments
2. Adding application-level review comments
3. Editing application-level review comments
4. Deleting application-level review comments
5. Updating order of application-level review comments
Managing data requests
1. About data request
2. Creating a new data request
Managing search schedules
1. Overview
2. Setting up new search schedules
3. Setting up one-time search schedules
4. Example of a one-time search schedule
5. Setting up recurring search schedules
6. Example of a recurring search schedule
7. Editing search schedules
8. Deleting search schedules
Managing export operations
1. About exporting items
2. Performing export runs
Managing reviews
1. About reviewing with Alta Surveillance
2. Limitations on reviewing certain types of Skype for Business content
3. Understanding the Review page
4. Changing the Preview pane position
5. Rearranging columns in the item list pane
6. Filtering the items in the Review pane
7. Viewing dynamic review item counts on the calendar
8. Reviewing searched items
9. Adding or removing text for machine learning
10. Assigning review status to items
11. Viewing hotwords highlighting
12. Viewing hotwords in collaboration message
13. Viewing tags highlighting
14. Viewing predicted labels of review items
15. Viewing the full content in a new window
16. Adding comments to items
17. Escalating the review items
18. Applying labels to items
19. Viewing history of items
20. Printing and downloading the items and attachments
21. Viewing Intelligent Review Details
Working with reports
1. About Alta Surveillance reports
2. Predefined reports
3. Enhanced reporting
4. Managing Power BI templates for reporting APIs
Managing Audit Settings
1. Audit Settings Overview
2. Editing the Audit Settings
Working with Audit viewer
1. About Audit viewer
2. Performing a search for audit records

Viewing Intelligent Review Details

The Intelligent Review Details section provides the facts of why the item is classified as Unreviewed Relevant or Unreviewed Irrelevant. It shows the Relevant and Irrelevant labels (links) and the respective contribution.

During review, the Intelligent Review Details section appears on the Preview tab only if the departments you want to review are enabled for Intelligent Review, and the Show Intelligent Review Details in Review permission is enabled for the logged-in user. This permission is by default enabled for the Department Reviewer, Escalation Reviewer, Compliance Supervisor, Exception Reviewer, and Passive Reviewer roles, where either the Review Messages or the Review Escalations permissions are enabled. By default, Intelligent Review Details section is collapsed. Users can expand and collapse it as required.

The total relevant and irrelevant contribution value is shown besides the respective labels. These values (between 0 to 100) are factor of relevant and irrelevant contributions found inside the item. When you click the Relevant and Irrelevant labels, the corresponding details appear which shows the factors that have contributed towards relevant or irrelevant. The calculated values of a contribution of each factor are mentioned so that a reviewer can understand the reason behind the item being relevant or irrelevant.

If the contribution value of the relevant factors is more than the contribution value of the irrelevant factors, the item will have higher relevance score (greater than 50). If the contribution value of the irrelevant factors is more than the contribution value of the relevant factors, the item will have lower relevance score (less than 50). If the contribution values of the relevant and irrelevant factors are almost similar, the item will have the relevance score around 50.

The factors that contribute are Author, Recipient, Subject, Content, Direction, Tags, and Department influence. Department Influence shows the extent to which prediction is inclined in favor of or against the marking based on the department's learning. If reviewers in a department favor marking items as irrelevant, then Department Influence will contribute towards irrelevance and vice versa. If factors do not have values, they can still contribute as relevant or irrelevant. It happens when the algorithm weighs the lack of details (such as the presence of zero tags or absence of content) as a contributing factor to its learning.

If the contribution of any factor is insignificant, but not absolutely zero (0), then value can be rounded to and displayed as zero (0). If the factor does not contribute at all, then this factor is not displayed. The amount of contribution of each factor is also shown with the color legend.

The flag icon, representing AI-based Label Predictions, shows AI-predicted labels and their count for each review item if the Enable AI Predictions option is selected for active labels and label groups.

Refer to the sample images below.

Figure: Preview pane showing the IR details, hit highlighting, managing text for learning, and AI-predicted labels

Figure: Contributing factors and their contribution

Following are some circumstances when the Intelligent Review Details are not available, and the application displays different messages for users.

Circumstance	Displayed message
Item is not processed by the Intelligent Review engine	Details not available
Data for machine learning is inadequate	Intelligent Review is still learning. Details are not yet available.
Technical problem during loading Intelligent Review Details	Error loading Intelligent Review Details