Cloud Compliance for Salesforce Features
What are the features of Cloud Compliance for Salesforce?
Security
- Compliance Monitoring
- Anomoly Detection
Compliance
- Data Governance
- Sensitive Data Compliance
Top Rated Cloud Compliance for Salesforce Alternatives
(40)
4.7 out of 5
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Cloud Compliance for Salesforce Categories on G2
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Data Masking
Sensitive Fields | The ability to create fields that automatically mask data such as credit card numbers or passwords. | Not enough data | |
Dynamic Masking | The ability to mask data in real time as it is entered. | Not enough data | |
Static Masking | The ability to apply or remove masking after data has been entered. | Not enough data | |
Consistent Masking | Tools to mask data using a consistent ruleset. | Not enough data | |
Random Masking | Tools to mask data using random characters and data. | Not enough data |
Security
Compliance Monitoring | Based on 14 Cloud Compliance for Salesforce reviews. Monitors data quality and sends alerts based on violations or misuse. | 89% (Based on 14 reviews) | |
Anomoly Detection | Constantly monitors acivity related to user behavior and compares activity to benchmarked patterns. This feature was mentioned in 14 Cloud Compliance for Salesforce reviews. | 87% (Based on 14 reviews) |
Compliance
Data Governance | As reported in 11 Cloud Compliance for Salesforce reviews. Ensures user access management, data lineage, and data encryption. | 91% (Based on 11 reviews) | |
Sensitive Data Compliance | Based on 12 Cloud Compliance for Salesforce reviews. Supports compliance with PII, GDPR, HIPPA, PCI, and other regulatory standards. | 90% (Based on 12 reviews) | |
GDPR compliant | Meets GDPR requirements for pseudonymisation under the Data Protection by Design and by Default requirements. | Not enough data | |
CCPA compliant | Meets de-identification requirements under the CCPA. | Not enough data |
Functionality
Data Subject Access Requests | Data Subject Access Request (DSAR) functionality helps companies comply with user access and deletion requests. | Not enough data | |
Identity Verification | Identity verification functionality validates a person's identity prior to a company complying with a data subject access request. | Not enough data | |
Data Mapping - automated | Data mapping functionality, which helps companies understand how data flows throughout their organization, is achieved through automated machine learning. | Not enough data | |
Data Discovery | Data discovery features collect and aggregate data from a variety of sources and prepares it in formats that both people and software can easily use it to then run analytics. | Not enough data | |
Data Classification | Data classification features tag the discovered data to make it easy to search, find, retrieve, and track. | Not enough data | |
De-identification/pseudonymization | De-identification or pseudonymization features replace personally identifiable information with artificial identifiers, or pseudonyms to comply with privacy regulations. | Not enough data | |
Breach notification | Data Breach Notification features help companies automate their breach response to stakeholders. | Not enough data | |
Consent management | Consent management features help companies obtain and manage user consent when collecting, sharing, buying, or selling a user's data. | Not enough data | |
Data access governance | Data Access Governance functionality helps limit the number of people who have access to data unless they are permissioned to do so. | Not enough data | |
Static pseudonymization | Offers traditional static de-identification (also known as consistent replacement), where the pseudonymized data uses the same pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox and the Robert Fox name is used multiple times. This type of pseudonymization carries some risks of re-identification if paired with enough datasets. | Not enough data | |
Dynamic pseudonymization | Offers dynamic de-identification (also known as random replacement), where the pseudonymized data uses different pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox once, and then the next time the data is used the name changes to Michael Jones. This type of pseudonymization carries lesser risk of re-identification if paired with many datasets. | Not enough data | |
Batch de-identification | Offers methods to de-identify large volumes of data using batch files. | Not enough data | |
Identity Verification | Identity verification functionality validates a person's identity prior to a company complying with a data subject access request. | Not enough data | |
Workflow | Offers workflows to process Data Subject Access Requests to enable multiple departments to assist when complying with user access and deletion requests. | Not enough data | |
DSAR Portal | Offers a user-facing portal for data subjects to request access to their data. | Not enough data | |
Reporting and logs | Has reporting and log functionality to prove that companies are in compliance with mandated response time, per privacy laws such as GDPR, CCPA, and others. | Not enough data | |
Centralized platform | Has a centralized view of data breach notification functions including any tasks that are at risk of falling behind mandated reporting timelines. | Not enough data | |
Automated response | Provides tools such as auto-discovery to assist companies in automating their breach notification response. | Not enough data | |
Breach notification law compliance | Provides functionality to help companies comply data breach notification timelines, as determined by various regulatory laws. | Not enough data | |
Workflow | Offers workflows to enable multiple departments to collaborate on data breach notification tasks | Not enough data | |
Reporting | Has reporting and analytics functionality to show compliance with data breach notification laws. | Not enough data | |
Dashboard | Offers a dashboard to capture, store, and manage granular user consents | Not enough data | |
Reporting | Provide reporting functions showing granular data to demonstrate compliance to regulators | Not enough data | |
Integrations | Integrates with marketing software and other analytical tools | Not enough data | |
End-user management | Allows end-users to manage their preferences online | Not enough data | |
Audit trails | Shows audit trails of how user consent preferences have changed | Not enough data | |
APIs | Offers APIs to link to your data | Not enough data | |
Mobile SDK | Offers a mobile SDK to use consent management tools on mobile apps | Not enough data | |
Customizable design | Offers customizeable designs to match corporate branding | Not enough data | |
Server-side storage | Offers server-side storage of consent, not client-side, for compliance reporting purposes | Not enough data | |
Structure type | Searches structured, semi-structured, and unstructured data for sensitive data. | Not enough data | |
Contextual search | Offers contextual search functions to understand factors such as file type, sensitivity, user type, location, and other metadata. | Not enough data | |
Template algorithms | Has template rules and pattern matching algorithms (PII, PCI, PHI and more) | Not enough data | |
Custom algorithms | Offers the option to create custom templates and rules based on department, user type and data type. | Not enough data | |
Multiple file and location types | Search multiple file types (images, PDFs, etc.) and repository locations (such as on-premise databases, cloud storage, email servers, websites, etc.) | Not enough data | |
Dashboard | Offers a dashboard showing specific location of sensitive data. | Not enough data | |
Compliance | Facilitates compliance and enables adherence to common industry regulatory standards such as GDPR, CCPA, HIPAA, PCI DSS, ISO, and others. | Not enough data | |
Reporting | Offers reporting functionality. | Not enough data |
Connectivity
Web services APIs | Offers APIs to connect products. | Not enough data |
