Guide to Data Privacy Solutions

• Data privacy solutions combine governance, security, consent, and compliance tools to protect sensitive data.
• Different data privacy management solutions address different needs, from discovery and encryption to consent and compliance, and more.
• The best data privacy solutions integrate automation, AI, and continuous monitoring across the data lifecycle.

Data privacy solutions are any technology, tools, and processes used to control, monitor, and protect personal and sensitive data. These solutions may include data masking, encryption, access management, and automated compliance tools that enable companies to handle client, employee, and operational data ethically and securely. Data privacy solutions help manage how data is collected, processed, stored, and shared along the full data lifecycle, from collection to deletion.

As consumer and organizational data continues to grow in size and complexity, and as regulations regarding its handling become more stringent and complex, there's naturally an associated need for solutions that enable companies to comply with internal and external requirements. Additionally, today’s more-informed customers expect greater data transparency from organizations and greater control over their own data.

Data privacy solutions help support regulatory compliance and build customer trust, and often complement data security tools.

There are some key differences and nuances when discussing data privacy versus data security. The two are interrelated but can differ in important ways in their purpose and approach.

Data security is fundamentally focused on protecting systems, platforms, and data from unauthorized third party access. Data security helps protect against cyberattacks, phishing, and other forms of exploitation.

Data privacy concerns people’s rights, consent, and appropriate or ethical use of data by employers, businesses, and other organizations that collect and use people’s data. Data privacy determines who has authorization to access someone’s data and governs the collection and usage of any personal information. The right data privacy approach can also help empower individuals to manage their own data.

Strong data privacy solutions require both strict privacy controls and robust security measures.

There are several categories of data privacy solutions. In some cases, a particular tool or solution may offer capabilities that overlap with one or more other categories. However, for clarity, we are going to categorize and examine the major types in six groups. To keep things organized, let’s break them down by their respective capabilities and uses.

The first major category is tools or solutions designed to enable data discovery, mapping, and classification.

The primary tasks of these types of tools are to:

• Scan structured and unstructured data sources
• Detect personal data such as names, email addresses, health data, and financial records
• Build a centralized inventory of sensitive data

These types of tools may be used in situations such as:

• Automatically scanning cloud and on-premise storage to map data flows, ensuring compliance with regulations like GDPR or CCPA
• Identifying “dark data” (unknown, unmanaged data) and classifying it to reduce Redundant, Outdated, and Trivial (ROT) information, which lowers storage costs and risk
• Spotting sensitive data stored in unprotected locations

All businesses benefit from robust data privacy protocols. Specifically, these tools:

• Improve visibility into unknown or shadow data
• Help prioritize risk and compliance efforts
• Support downstream privacy and security controls

A balanced view of these tools reveals some potential caveats. Specifically:

• They generally require ongoing updates as systems change
• They can generate false positives or incomplete classifications
• Large data environments may require significant setup and fine tuning

Learn more about data classification.

The next type is enterprise-level data privacy solutions that centralize privacy workflows and governance across the organization.

Dedicated, enterprise-tier platforms:

• Connect to multiple data sources and business systems
• Centralize privacy policies, controls, and requests
• Automate routine privacy operations across departments
• Typically include features such as consent and preference management, data subject request workflows, policy management, audit trails, risk assessments, and reporting

These types of platforms may offer multiple functions and benefits, but their primary uses include:

• Managing data subject rights requests across the organization
• Maintaining audit trails for regulatory reviews
• Coordinating privacy governance across different business units and geographies

Enterprise-level data privacy platforms benefit businesses by:

• Creating a unified view of privacy operations
• Simplifying cross-functional collaboration and reporting across teams, organizations, and locations
• Scaling becomes much easier than when managing manual privacy processes

Learn more about Salesforce Privacy Center.

There are some potential downsides or caveats of large, enterprise-capable data privacy platforms, including:

• They can be expensive and complex to implement, and in some cases may require dedicated technicians to operate and keep optimized
• May require complicated integration with many systems, though the better platforms will have robust API/system integration
• Some organizations may only use a small portion of a platform’s capabilities, and may not need the added cost or complexity associated with unused functions

Read more in our enterprise security guide.

These solutions are designed specifically to help organizations manage user consent and communication preferences. Let’s look at their capabilities and uses.

As the name implies, consent and preference management solutions:

• Collect and store user consent choices
• Manage opt-ins, opt-outs, and communication preferences
• Automatically track and record consent history for audit and compliance purposes

These types of solutions are widely used to facilitate:

• Website cookie banners and tracking preferences
• Email and SMS marketing permissions
• Region-specific consent requirements under varying privacy laws

As customer consent issues become increasingly more important, these solutions benefit businesses by:

• Building trust through transparency and user control
• Reducing compliance risk for marketing and communications
• Making consent easier to manage across channels and all customer touchpoints

Since consent and preference management solutions are fairly specific in their application, they present some possible issues, including:

• They can create fragmented experiences if systems are disconnected or not kept updated as technologies evolve
• Rules regarding the legal use of these tools can vary by region and regulation, and customer consent laws are frequently in flux
• As a result, the solution may require regular updates as laws and user behaviors change

Data encryption and tokenization solutions are becoming vital for any organization handling payment information, health records, financial data, and other sensitive information that requires high levels of security and regulatory compliance.

• Encryption converts data into unreadable formats, applying protection to data at rest, in transit, and in use
• Tokenization replaces sensitive data with non-sensitive placeholders, commonly used for payment and customer information
• Both approaches limit exposure of readable sensitive data across systems

• Protecting customer payment information in commerce systems
• Securing health records and financial data in cloud storage
• Meeting encryption mandates required by regulations such as HIPAA and PCI-DSS

Encryption and tokenization solutions help businesses and other organizations dealing with sensitive data by:

• Greatly reducing exposure if systems are breached
• Supporting compliance requirements for regulations and governance
• Helping secure data across both cloud-based and on-premises environments

Read more about Shield: Platform Encryption.

Powerful encryption and tokenization solutions may have somewhat specific capabilities, and the following caveats should be considered:

• Their high resource requirements may impact system performance and usability
• Due to increasingly more powerful hacking attacks, these solutions require careful management of encryption keys and tokens
• Solutions built specifically for tokenization and encryption may not address data collection or consent functionality, which may require multiple solutions if not integrated well

Data Loss Prevention (DLP) tools are built specifically to prevent sensitive data from leaving approved environments. Let’s dig in a little deeper.

DLP solutions and tools are developed to:

• Monitor data movement across email, cloud storage, applications, and endpoints
• Detect unauthorized sharing or transfer of sensitive data
• Automatically block, alert, and/or quarantine risky activity using policy-based controls
• Perform file scanning, content inspection, and endpoint/cloud monitoring

These solutions shine in hybrid, remote, and/or distributed workforces, where employees may unintentionally (or intentionally) expose sensitive data to bad actors, including:

• Preventing employees from sharing sensitive files via personal email or cloud storage
• Detecting accidental exposure of customer records in outbound communications
• Enforcing data handling policies across remote and distributed workforces

• They reduce risk of accidental or intentional data leaks
• They improve visibility into risky behavior so it can be addressed through training or disciplinary measures
• They support compliance, incident response, and auditing

Some possible downsides of DLP solutions include:

• They can create false positives that interrupt workflows
• They require well-defined policies, tuning, and regular updates as permissions and rules change
• They may frustrate legitimate users if settings are overly restrictive, leading to employees seeking less secure but more user-friendly options

Learn more about data loss prevention solutions.

These tools and solutions specialize in protecting sensitive data across testing, analytics, and development environments, in unique ways that still preserve the availability of relevant data from unsecured or third-party sources when needed.

• Data masking tools replace or obscure sensitive values while preserving realistic formats used in development, testing, and training environments (similar to redacting a classified government report, for example)
• Data anonymization removes identifying details so data can no longer be tied to an individual, and it can then be used for analytics and research purposes
• Both approaches allow organizations to make use of realistic data without exposing personal information

• Providing realistic but safe datasets to development and QA teams
• Sharing anonymized data with third-party vendors or researchers
• Running analytics on accurate customer data without exposing any personally identifiable information

These types of solutions benefit businesses by:

• Reducing risk when using data outside production systems
• Supporting privacy without eliminating data utility
• Enabling development and third-party sharing

• Some methods of data masking or anonymization can still be reversed or re-identified with the right approach
• They may reduce the usefulness of some data for analytics
• They require consistent application across environments, or security breaches may occur

Organizations looking for data privacy solutions should learn what to ask and which certifications they might consider must-haves when comparing platforms and vendors. Let’s go over some key areas for evaluation.

There are different methods and organizations that can determine whether a particular data privacy solution can claim to be “certified.” These can include third-party audits and security certifications awarded by industry organizations, compliance with recognized standards and frameworks, and documented policies (on the part of the vendor) for privacy, security, and risk management. Let’s look at some relevant certifications and industry standards you might want to look for, depending on your organization’s area of focus.

International Organization for Standardization (ISO) 27001: The ISO 27001 certification is an internationally recognized statement or certification that an organization’s Information Security Management System (ISMS) complies with the ISO/IEC 27001 standard. It verifies that the organization has implemented a structured, risk-based approach to secure data, covering policies, procedures, and controls to manage risks to confidentiality, integrity, and availability.

Service Organization Control (SOC) 2: Service Organization Control (SOC) 2 is an auditing procedure developed by the AICPA (American Institute of Certified Public Accountants) that ensures service providers manage client data securely, adhering to the AICPA’s Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy. It’s become a voluntary, though widely adopted, compliance standard for technology and cloud computing companies storing data in the cloud.

Industry-specific certifications and regional standards: There are many other security-related standards and certifications that may be relevant to your business or organization. Depending on your purpose, location, and other factors, you may wish to ensure any proposed solution conforms to one or more of the following industry or region-specific data/privacy standards.

1. General Data Protection Regulation (GDPR) (EU Regional/Cross-Industry)
2. Health Insurance Portability and Accountability Act (HIPAA) (Healthcare Industry)
3. Payment Card Industry Data Security Standard (PCI DSS) (Financial Industry)
4. California Consumer Privacy Act (CCPA) / CPRA (California Regional)
5. Family Educational Rights and Privacy Act (FERPA) (Education Industry)

After you’ve familiarized yourself with which standards and certifications you want to prioritize, you can reach out to the data privacy solution providers you’re considering and get some more specific details to help you decide which platform would be the best fit for you. These may include:

• What regulations and frameworks does the solution support?
• How does the solution integrate with our existing systems?
• What automation and reporting capabilities are included?
• How is sensitive data protected and monitored?
• Are there any other relevant industry-specific or regional compliance standards or certifications we should be looking for?

Over the past few years, AI has fundamentally changed how companies manage tasks, processes, and workflows.

AI can be used to optimize or perform multiple tasks where appropriate, legal, and desired. This can include the ability to:

• Automatically classify and label sensitive data
• Detect unusual behavior and security risks, including compliance/governance issues
• Recommend remediation steps and policy updates

In addition to the above, AI is commonly used in the data privacy space to:

• Automate data subject requests
• Identify unknown/uncategorized but sensitive data
• Proactively predict compliance gaps and privacy risks based on proposed scenarios

Learn about Agentforce in Privacy Center.

AI-enabled solutions have the potential to help streamline or improve multiple areas of a business. Specifically, they can:

• Reduce manual effort and speed up privacy operations
• Improve accuracy and consistency across large environments
• Help organizations scale privacy programs

AI systems have become nearly ubiquitous, but there are some potential shortcomings if they are not carefully chosen and maintained. These may include:

• AI systems require high-quality data and oversight. The oft-cited GIGO (“garbage in, garbage out”) programming axiom still applies here
• Automated decisions may create transparency concerns if the boundaries and decision limitations of AI systems and agents are not properly defined
• Organizations must still validate outputs and maintain governance and overwatch, particularly for high-security or high-risk situations

It’s helpful to go over a defined process that can help you evaluate which combination of data security tools and strategies best fit your needs. In addition to the question to ask your vendor/representative provided above, consider following these steps:

Identify specifically which sensitive data you collect and where it resides. Prioritize the most significant privacy and compliance risks, but identify all of them.

Go through the main types or categories of data privacy solutions above, and narrow your selection based on exactly what you need and expect from a solution. For example, you might look for a solution that specializes in:

• Discovery and mapping for data privacy visibility, detecting personal data in an existing database, and building a centralized inventory of sensitive data
• DLP and encryption for protection and monitoring data movement, detecting unauthorized sharing or transfer of sensitive data, and blocking risky activity
• Consent and compliance tools for governance, managing customer privacy-related interactions, etc.
• Other desired privacy functions relevant to your unique circumstance

Obviously, any solution will only be useful if it integrates and “plays well” with your existing systems. To that end:

• Choose tools that work across cloud-based, on-premises, and third-party systems
• Make sure your selections can integrate well with current workflows and processes without needless customizations or technical oversight
• Ensure solutions can scale as data and regulations change and grow

• Evaluate the ease of implementation and ongoing management (how much work will need to be dedicated to keeping data privacy solutions functional and current)
• Balance strong controls with the need for a positive employee and customer experience

Salesforce has data privacy solutions for multiple types of businesses and organizations. Salesforce Trusted Services includes several products that help automate and enhance your data privacy:

• Privacy Center: Automates data privacy management by streamlining "Right to be Forgotten" requests and data access requests.
• Shield Data Detect: Helps identify sensitive data across your org, a key first step in protecting private data.
• Data Mask & Seed: Anonymizes sensitive data in sandbox environments, ensuring private data doesn't get exposed during development or testing.

See a demo of Privacy Center to learn more.