The Best Tools for Securing PII: A Vendor-by-Vendor Comparison
Personally identifiable information (PII) moves through email, endpoints, cloud storage, SaaS applications, databases, collaboration tools, and AI platforms. Protecting it requires visibility into where data resides and controls that prevent unauthorized access, sharing, and exposure.
The best PII protection tools address different parts of this challenge. Some specialize in discovery and classification, while others focus on email security, databases, Microsoft 365, SaaS applications, AI, or enterprise DLP. This guide compares leading vendors by use case, capabilities, strengths, and deployment model.
What Is PII?
PII is information that can identify an individual directly or when combined with other data. Examples include names, email addresses, Social Security numbers, financial account details, biometric records, government-issued identifiers, and combinations of demographic or behavioral information.
The sensitivity of PII varies. A person’s name may be relatively low risk by itself, while a Social Security number, financial account number, or biometric identifier typically requires stronger safeguards.
What Does PII Stand for in Cybersecurity?
In cybersecurity, PII covers information that can distinguish or trace an individual’s identity, either on its own or when combined with other information. Organizations generally protect PII through discovery, classification, encryption, access controls, data loss prevention, and secure handling policies.
Is a Social Security Number Considered PII?
Yes. A Social Security number is sensitive PII because it uniquely identifies an individual and may be used for identity theft, fraud, or unauthorized account access. Organizations should apply strong access controls, encryption, monitoring, and data loss prevention policies wherever Social Security numbers are stored, processed, or transmitted.
PII Protection Tools at a Glance
The best PII protection tool depends on the environment and risk being addressed. Fortra is a strong overall choice for organizations seeking connected discovery, classification, and enforcement across hybrid environments. Other vendors stand out in specialized areas, including BigID for discovery, Imperva for database security, Proofpoint for email, and Nightfall AI for APIs and AI workflows.
| Vendor | Best For | Key PII Capabilities | Deployment | Ideal Customer Profile |
|---|---|---|---|---|
| Fortra | Overall enterprise data protection | DLP, DSPM, data classification, email and web controls, AI data security | Cloud, hybrid, and on-premises options | Mid-market to enterprise |
| BigID | Data discovery and DSPM | Sensitive-data discovery, classification, risk prioritization, remediation workflows | Cloud-native with hybrid and on-premises coverage | Enterprise |
| Imperva | Database and data security | Discovery, classification, database activity monitoring, policy enforcement, data masking | Cloud, hybrid, and on-premises options | Mid-market to enterprise |
| Proofpoint | Email PII protection and DLP | Email content inspection, DLP policies, encryption, common classifiers | Cloud and gateway-based options | Mid-market to enterprise |
| Metomic | SaaS and Microsoft 365 PII | SaaS discovery, classification, exposure analysis, redaction, access remediation | Cloud SaaS | SMB to mid-market |
| Nightfall AI | AI, GenAI, and API PII detection | Machine-learning-based detection, API scanning, GenAI protection, automated remediation | Cloud and API | Startups to mid-market |
| Microsoft Purview | Microsoft 365 and Azure environments | Classification, sensitivity labels, DLP, Microsoft 365 and endpoint coverage | Microsoft cloud ecosystem | Organizations invested in Microsoft 365 |
| Forcepoint | Behavioral and enterprise DLP | Risk-adaptive DLP, behavioral analytics, policy enforcement across major channels | Cloud, hybrid, and on-premises options | Enterprise |
Deployment options and product packaging can change. Buyers should validate current availability, licensing, integrations, and regional support directly with each vendor.
How We Selected These Tools
These tools were selected based on their ability to discover, classify, and protect PII across modern environments. We evaluated detection accuracy, protection and remediation capabilities, deployment flexibility, coverage across data channels, and overall fit for specific use cases.
Our evaluation considered six areas:
PII discovery and detection: Whether the product can identify common and organization-specific types of personal information.
Classification and context: Whether it can classify PII and add context such as sensitivity, ownership, access, location, or business use.
Protection and enforcement: Whether it can block, encrypt, redact, quarantine, label, or otherwise control sensitive information.
Coverage: Whether protection extends across email, endpoints, networks, cloud services, SaaS applications, databases, APIs, or AI tools.
Deployment flexibility: Whether the product supports cloud, hybrid, on-premises, or API-based use cases.
Best-fit use case: The specific environment or security challenge where the vendor offers the clearest value.
No single tool is automatically the best choice for every organization. Each vendor below is identified by the use case in which its capabilities are most relevant.
The Best PII Protection Tools by Use Case
The strongest PII protection products include Fortra, BigID, Imperva, Proofpoint, Metomic, Nightfall AI, Microsoft Purview, and Forcepoint. The best choice depends on whether the organization needs broad enterprise protection or deeper specialization in discovery, databases, email, SaaS, AI, Microsoft environments, or behavioral DLP.
1. Fortra: Best Overall for Enterprise Data Protection
Fortra is designed for organizations that want to connect sensitive data discovery, classification, and enforcement across hybrid environments. Its portfolio combines DSPM, data classification, DLP, and AI data security capabilities to help organizations discover PII, understand its context, and apply controls across endpoints, email, networks, cloud services, SaaS applications, and AI workflows.
Key PII features
Sensitive data discovery across cloud, SaaS, on-premises, file-share, and database environments
Persistent classification metadata that can remain associated with emails, documents, and files
DLP controls across endpoint, network, email, web, cloud, and other data-movement channels
Context-aware classification using rules-based and AI-assisted detection
Controls for sensitive information submitted to generative AI websites and applications
Cloud, hybrid, SaaS, managed-service, and on-premises deployment options, depending on the component
| Pros | Considerations |
|---|---|
| Connects DSPM, classification, and DLP to support discovery-to-enforcement workflows | The breadth of the portfolio may exceed the requirements of very small teams |
Covers hybrid environments and multiple data-movement channels | Organizations should confirm which capabilities, consoles, and integrations are currently available for their selected products |
Persistent classification metadata can help policies follow sensitive content | Enterprise deployments may require planning, tuning, and stakeholder coordination |
Offers multiple delivery and service models for different operational requirements | Product packaging and deployment options vary by capability |
Ideal use case: Fortra is well suited to mid-market and enterprise organizations that need to protect PII across a heterogeneous environment rather than focusing on a single cloud, application, or communication channel.
Explore Fortra Data Loss Prevention, Fortra Data Security Posture Management, Fortra Data Classification, and Fortra AI Data Security.
2. BigID: Best for Data Discovery and DSPM
BigID suits enterprises focused on discovering, classifying, assessing, and remediating sensitive data at scale. Its DSPM platform covers cloud, SaaS, AI, hybrid, and on-premises environments.
It links data sensitivity with exposure, access, ownership, activity, and business context, then supports policy-driven labeling, masking, redaction, retention, and deletion.
Key PII features
Structured, semi-structured, and unstructured data discovery
ML- and NLP-based classification
Access and entitlement analysis
Contextual risk prioritization
Masking, redaction, labeling, retention, and deletion workflows
| Pros | Considerations |
|---|---|
Deep discovery and classification across a broad range of data sources | Enterprise-scale capabilities may be more than smaller organizations require |
Strong DSPM, privacy, and data-risk context | Buyers should map desired preventive controls to native features and integrations |
Supports automated, policy-driven remediation workflows | Architecture and deployment design may require coordination across data owners |
Covers cloud, SaaS, AI, hybrid, and on-premises environments | Licensing should be evaluated against the number and types of data sources involved |
Ideal use case: BigID is a strong fit for large organizations that need to inventory PII across a diverse data estate, understand exposure and ownership, and coordinate remediation with data, privacy, security, and governance teams.
3. Imperva: Best for Database and Data Security
Imperva fits organizations with significant PII risk in databases and structured repositories. It discovers sensitive data, monitors access and activity, enforces policies, and flags risky behavior.
Across legacy, cloud, and hybrid environments, Imperva centralizes discovery, classification, activity monitoring, threat analytics, and enforcement. Static masking replaces production values with realistic substitutes for development, testing, and analytics.
Key PII features
Database and repository discovery and classification
Activity monitoring and auditing
Policy-based alerts and blocking
Risk analytics and suspicious-activity detection
Static masking, tokenization, encryption, and anonymization
| Pros | Considerations |
|---|---|
Strong focus on database and structured-data protection | Organizations seeking endpoint-first or email-first DLP may need additional controls |
Combines activity monitoring with data and threat context | The architecture can involve multiple components depending on the use case |
Supports legacy, cloud, and hybrid data environments | Broader PII protection should also address SaaS, collaboration, endpoint, and AI channels |
Data masking supports safer development, testing, and analytical use | Deployment and policy design can require database and security expertise |
Ideal use case: Imperva is a good fit for organizations with substantial PII in business-critical databases, especially when database access monitoring, compliance auditing, and non-production data masking are priorities.
4. Proofpoint: Best for Email PII and DLP
Proofpoint remains one of the strongest choices when outbound email is the primary path through which PII could be exposed, but its capabilities now extend beyond email into broader information protection. Its email DLP and encryption capabilities inspect messages and attachments, apply policy-driven controls, and automatically encrypt qualifying communications, while adjacent offerings can help organizations address sensitive-data risks across additional channels.
The platform provides built-in identifiers for privacy, financial, healthcare, and other sensitive-data categories. Proofpoint also supports granular DLP and encryption policies at global, group, and user levels.
Key PII features
Inspection of outbound email messages and attachments
Built-in sensitive-data identifiers and dictionaries
Policy-based encryption
Granular policies by organization, group, or user
Integration with broader Proofpoint information-protection capabilities
| Pros | Considerations |
|---|---|
Deep specialization in email DLP and encryption | Email-specific products address only one part of the PII lifecycle |
Built-in identifiers for common regulated-data categories | Broader endpoint, SaaS, database, and AI protection may require additional products |
Centralized policy enforcement and incident visibility | Buyers should evaluate integration with their existing email architecture |
Can extend into broader DLP coverage within the Proofpoint portfolio | Licensing and functionality vary by product and package |
Ideal use case: Proofpoint is well suited to organizations that identify outbound email as a major PII exposure channel and need inspection, enforcement, and encryption closely integrated with email security.
5. Metomic: Best for SaaS and Microsoft 365 PII
Metomic is designed for organizations that need visibility into sensitive information spread across SaaS applications and collaboration platforms. It discovers and classifies content within messages, pages, tickets, files, and records, then helps teams remediate risky exposure.
Its capabilities include revoking inappropriate access, redacting sensitive content, quarantining files, and applying labels or retention policies. Metomic also offers an integration for Microsoft 365.
Key PII features
Deep content scanning within SaaS applications
Built-in detection for PII, PHI, payment data, credentials, and intellectual property
Custom classifiers for organization-specific data
Risk scoring and exposure prioritization
Automated access revocation, redaction, quarantine, labeling, and retention actions
| Pros | Considerations |
|---|---|
Built specifically for SaaS-centric data exposure | Less suitable as the only control for heavily on-premises environments |
Can inspect content inside collaboration and productivity applications | Coverage depends on the supported integrations relevant to the customer |
Provides direct remediation for sharing and access risks | Organizations may still require separate endpoint, network, database, or email DLP |
Cloud delivery can reduce infrastructure requirements | Buyers should verify which remediation actions are available for each connected application |
Ideal use case: Metomic fits SaaS-enabled SMB and mid-market organizations that need to discover PII in collaboration tools and cloud productivity applications, particularly when risky sharing and excessive access are primary concerns.
6. Nightfall AI: Best for AI, GenAI, and API PII Detection
Nightfall AI is well suited to developers and security teams that need to detect sensitive information in applications, APIs, SaaS platforms, and generative AI workflows. Its developer APIs and SDKs allow organizations to add DLP scanning programmatically to applications, data pipelines, datasets, and AI services.
Nightfall provides detectors for PII, protected health information, payment data, secrets, and sensitive images. Its APIs can be integrated into GenAI applications and data stores to scan sensitive content before it is exposed or transmitted.
Key PII features
Machine-learning-based PII, PHI, payment-data, and secret detection
APIs and SDKs for programmatic DLP
Scanning for GenAI applications, models, and datasets
SaaS and collaboration-platform integrations
Automated remediation and workflow support
| Pros | Considerations |
|---|---|
Strong API- and AI-oriented detection model | Less aligned with organizations that require predominantly on-premises or legacy deployment |
Suitable for embedding PII detection into applications and data pipelines | API implementation requires development and testing resources |
Context-aware detection can reduce dependence on simple pattern matching | Its cloud-native approach may not replace a full enterprise DLP architecture |
Useful for fast-moving SaaS and GenAI environments | Buyers should evaluate coverage for all required data channels and repositories |
Ideal use case: Nightfall AI is a strong fit for cloud-native companies, application teams, and security programs that want to add PII protection through APIs or control sensitive information entering GenAI tools and workflows.
7. Microsoft Purview: Best for Native Microsoft 365 and Azure Environments
Microsoft Purview is a natural option for organizations deeply invested in Microsoft 365. It provides built-in classification, sensitivity labeling, and DLP capabilities across Microsoft services and can extend protection to endpoints, on-premises repositories, and supported non-Microsoft applications.
Purview DLP can identify, monitor, and protect sensitive information in locations including Exchange, SharePoint, OneDrive, Teams, Office applications, Windows and supported macOS devices, Microsoft Fabric, Power BI, and Microsoft 365 Copilot.
Key PII features
Sensitive-information types for data such as Social Security and financial account numbers
Manual, pattern-based, exact-data-match, and trainable classification options
Sensitivity labels and document-protection controls
DLP policies across Microsoft 365 applications and services
Endpoint and supported non-Microsoft application coverage
| Pros | Considerations |
|---|---|
Native integration with Microsoft 365 applications and services | Licensing, configuration, and feature availability can be complex |
Common classification and labeling capabilities across Microsoft workloads | Organizations with highly heterogeneous environments should validate non-Microsoft coverage carefully |
DLP spans email, collaboration, devices, cloud applications, and AI scenarios | Effective rollout may require coordination among security, compliance, IT, and data owners |
Familiar administration environment for Microsoft-centric organizations | Policy tuning is important to manage false positives and user disruption |
Ideal use case: Microsoft Purview is best suited to organizations already standardized on Microsoft 365 and Azure that want to use native classification, labeling, and DLP before adding a separate enterprise data-protection platform.
8. Forcepoint: Best for Behavioral and Enterprise DLP
Forcepoint is a strong option for enterprises that want DLP enforcement informed by user behavior and risk. Its Risk-Adaptive Protection capabilities monitor user activity and can adjust policy responses based on the assessed risk associated with a user.
Forcepoint DLP covers cloud, web, email, endpoints, networks, and AI use cases. Its risk-adaptive model combines DLP events with behavioral analytics so enforcement can respond differently as user risk changes.
Key PII features
Enterprise DLP across endpoint, email, web, cloud, network, and AI channels
Behavioral analytics and user-risk profiling
Risk-adaptive policy enforcement
Policy and classifier templates for regulatory requirements
Cloud, hybrid, and on-premises deployment options
| Pros | Considerations |
|---|---|
Mature enterprise DLP coverage across major channels | Implementation can require significant policy planning and operational resources |
Behavioral context helps tailor enforcement to user risk | Some behavioral deployment models may require dedicated infrastructure |
Supports cloud, hybrid, and on-premises environments | Smaller teams may not need the full depth of enterprise functionality |
Strong fit for insider-risk and user-driven data-loss scenarios | Organizations should validate the architecture for their selected behavioral and DLP components |
Ideal use case: Forcepoint is best suited to large enterprises that need mature DLP and want enforcement decisions to reflect user behavior, activity, and changing risk.
How to Choose the Right PII Protection Tool
Choose a PII protection tool by identifying where personal data resides, how it moves, which exposures present the greatest risk, and what enforcement actions the organization needs. Feature volume matters less than how well the controls match the environment.
1. Identify your data and where it resides
Identify the PII the organization collects, processes, stores, and transmits—including common identifiers and organization-specific records—and where it resides, such as endpoints, email, file shares, databases, cloud storage, SaaS and Microsoft 365 applications, development environments, APIs, and AI models, prompts, agents, and datasets. Match the tool to the environment: SaaS-first companies may favor Metomic or Nightfall AI, database-heavy organizations may prioritize Imperva, and heterogeneous enterprises may benefit from broader platforms such as Fortra, Forcepoint, BigID, or Microsoft Purview.
2. Assess discovery, classification, and enforcement
Finding PII is only the first step. Evaluate whether a tool can accurately classify varied data using dictionaries, exact-data matching, document fingerprinting, contextual analysis, machine learning, custom classifiers, and persistent metadata—and then act by labeling, restricting, blocking, encrypting, redacting, quarantining, correcting permissions, alerting administrators, coaching users, or triggering remediation. Organizations can integrate discovery-led platforms with enforcement tools or choose a vendor that connects discovery, classification, and DLP directly.
3. Consider the deployment model
Cloud-native tools can be faster to introduce in modern SaaS environments. Hybrid and on-premises support remains important for organizations with legacy systems, regulated workloads, data-residency requirements, or sensitive intellectual property.
4. Review operational requirements
Assess who will administer the product, investigate incidents, tune policies, manage exceptions, and respond to findings. A technically sophisticated platform will not reduce risk if the organization lacks the resources to operate it.
5. Test with real workflows
A proof of concept should reflect real data types and business processes. Test whether the tool detects relevant PII, produces manageable alert volumes, applies the correct actions, and avoids interrupting legitimate work.
How to Protect PII: Best Practices
PII is protected through layered discovery, classification, access control, encryption, DLP, monitoring, and secure data-lifecycle practices. No single technology can protect personal information across every repository, user action, and transfer path.
Discover and classify PII. Scan cloud services, SaaS applications, endpoints, databases, file shares, collaboration tools, and unsanctioned repositories. Classify data by sensitivity and context, distinguishing routine contact information from high-risk identifiers, financial and health records, credentials, and biometric data.
Minimize collection and retention. Collect personal information only for a defined business purpose and securely delete it when retention is no longer required.
Restrict access and sharing. Apply least-privilege access, review excessive permissions, and remove access as roles change. Correct public links, exposed repositories, and cloud misconfigurations before they lead to data loss.
Encrypt or tokenize sensitive information. Protect PII at rest and in transit, and replace sensitive values with tokens where appropriate. Learn more.
Apply DLP across key channels. Monitor and control PII moving through email, endpoints, cloud applications, browsers, networks, uploads, removable media, and AI tools.
Protect AI workflows. Inspect prompts, responses, retrieved content, training data, tool calls, and logs. Redact, block, or restrict PII before it reaches unauthorized models or services.
Monitor and respond to threats. Combine data sensitivity with user, device, location, and behavioral context to prioritize abnormal activity. Establish escalation, investigation, notification, and recovery processes for suspected exposure. Reviewing incidents.
PII and Compliance
PII protection helps organizations support privacy, security, and breach-prevention requirements across regulations and industry standards, but no tool guarantees compliance by itself. Technology must be combined with governance, documented policies, employee responsibilities, legal guidance, risk assessment, and incident-response processes.
Depending on the organization and data involved, relevant requirements may include:
GDPR: Protects personal data and establishes obligations for organizations processing information related to individuals in applicable jurisdictions.
CCPA and CPRA: Establish privacy rights and business obligations involving the personal information of California consumers.
GLBA: Requires covered financial institutions to protect customer information.
HIPAA: Establishes safeguards for protected health information handled by covered entities and applicable business associates.
PCI DSS: Defines security requirements for environments that store, process, or transmit payment card data. Read more.
When evaluating a product, ask how its discovery, classification, reporting, retention, access-control, encryption, and DLP capabilities support the organization’s specific legal and contractual obligations.
Bring PII Protection Together with Fortra
PII can move from a database to a spreadsheet, an endpoint, an email, a SaaS application, or an AI prompt in minutes. Protecting it requires visibility into where sensitive data resides, context about what it means, and controls that remain effective as the data moves.
Fortra connects data discovery, persistent classification, and policy enforcement to help organizations protect sensitive information across cloud, hybrid, and on-premises environments.
