The Complete Guide to Product Privacy Management

Product is the critical gap in privacy programs today. 

In our tech-driven world, software products like websites and apps generate the most privacy risk by controlling how personal data is processed, yet privacy teams lack product visibility.  

In 2024, Privado scanned the top 100 websites in the US and Europe and found 75% were not privacy compliant because they shared personal data without proper consent. The situation is similar with mobile apps. Why is that?

The only tools privacy teams have to mitigate risk in products are manual assessments and processes that rely on trust. Privacy teams have had to trust that consent management platforms were properly configured for websites to block third-party cookies when users opt out. Trust-based privacy has proven to break at scale. 

Now that privacy regulation, enforcement, and consumer expectations have increased globally, we need evidence-based privacy solutions focused on the primary source of risk: product. 

By monitoring how user-facing and backend software products process personal data, companies can obtain the necessary evidence to create complete data maps, proactively remediate privacy risks, and generate accurate compliance reporting. We call this practice product privacy management.

Product privacy management enables comprehensive and continuous privacy governance across an organization. Complete product privacy management means privacy teams can govern personal data in real-time across websites, mobile apps, connected TV apps, backend software, and third-party applications. This level of data visibility provides the evidence for privacy teams to verify compliance and engineering teams to proactively remediate privacy risks. Product privacy management is critical for maintaining compliance across privacy regulations, including GDPR, CCPA, CPRA, CIPA, MHMDA, the FTC, and HIPAA.

In this comprehensive guide to product privacy management, we will delve further into:

• What product privacy management is
• How product privacy management compares with existing practices
• The current approaches to product privacy management
• Use cases for product privacy management
• What impact product privacy management can have on your organization 

What is product privacy management?

Product privacy management is the practice of monitoring software products to mitigate privacy risk. Because software products control how data is processed in today’s tech-driven world, product privacy management can enable complete data visibility and continuous privacy governance at scale across an organization. By monitoring how websites, mobile apps, connected TV apps, backend software, and third-party applications process personal data, organizations can create complete data maps, proactively remediate privacy risks, and generate accurate compliance reporting. 

Product privacy management is valuable for any B2C or B2B company processing large amounts of personal data on their websites, mobile apps, or other software products. Companies running digital ads in financial or health related industries are typically most at risk of privacy lawsuits, but companies in any industry processing personal data without proper consent are still at risk. 

The current approaches to product privacy management

• Code scanning analyzes the code running any user-facing or backend software product to mitigate privacy risk during software development and after products are live
• Live product scanning provides additional risk mitigation for user-facing products such as websites and mobile apps. By simulating user behavior, live product scanning can monitor data flows based on user consent actions and identify additional data flows not specified in the code. 
• Manual assessments. Because most privacy solutions today don’t monitor software products, most companies rely on manual privacy risk assessments.

Product privacy management enables complete risk mitigation at scale

Best-in-class product privacy management solutions that scan code and live products enable true Privacy-by-Design and compliance at scale by integrating evidence-based privacy controls across the product development lifecycle from planning through development and maintenance.

With best-in-class product privacy management, privacy teams can eliminate the manual assessments that were missing most privacy risks and slowing down the business. By proactively mitigating privacy risk, product privacy management can also turn privacy teams into business-enablers instead of blockers.

How product privacy management compares with existing practices

Traditional privacy management focuses on privacy operations, not risk

Traditional privacy management focuses on the operations needed for privacy compliance, but it does not focus on mitigating privacy risk in software products. Privacy regulations in Europe, US, and around the world require companies to implement consent banners, publish privacy policies, offer data subject access requests (DSAR), and report data breaches. 

Today’s privacy management solutions are designed primarily to meet those operational privacy needs, and privacy teams must trust that these solutions are used in a compliant manner. These technology vendors often specialize in consent management and DSAR automation solutions. Consent management platforms (CMPs) represent the key difference between traditional privacy management and product privacy management. CMPs are critical for managing consent banners and limiting data processing across websites and mobile apps, but CMPs do not monitor websites and mobile apps to verify whether the CMP and third parties have been implemented properly to eliminate privacy risk. 

Data discovery solutions are designed for privacy operations and data governance 

Traditional privacy management also overlaps with data governance. To execute privacy operations such as DSARs and reporting, organizations need to document what data they have and where it is stored. Traditional privacy management vendors offer data discovery solutions that inventory data in storage that primarily support privacy operations and data governance. Once these vendors identify personal data in storage by scanning databases and some third-party tools, the DSAR automation solutions offered by the same vendors can be set up to better meet data deletion requests from users. Data discovery solutions also support data access management and data retention needs. 

Traditional privacy solutions rely on trust-based, manual assessments to complete data maps and mitigate risk

What data discovery and data governance solutions do not adequately address is privacy risk. Inventorying data in storage does not identify what personal data is shared without proper consent or who it is shared with. Inventorying data in storage does not identify where personal data is collected or how it is being used. The vast majority of privacy risk comes from how software products like websites and apps collect, use, and share personal data, and yet, traditional privacy management vendors offer minimal support to address these risks. 

To complete data maps and privacy risk assessments, most privacy teams today send out questionnaires and conduct interviews. Traditional privacy management vendors offer tools to support this manual data gathering process such as digitized assessment forms and templates, but these manual tools have proved to be ineffective and inefficient. 

Product privacy management focuses on monitoring software products to mitigate privacy risk at scale across an organization. Traditional privacy management focuses on executing privacy operations but isn’t equipped to monitor or remediate privacy risk in the process. 

What are the current approaches to product privacy management?

Live product scanning

Live product scanning monitors data flows and assesses risk that is live in user-facing products such as websites, mobile apps, and connected TV apps. 

Websites and mobile apps are subject to the most visible privacy risks that lead to lawsuits, and any website or app at risk sharing personal data without proper consent needs continuous monitoring. Live product scanning simulates user behavior on websites and apps for each consent action, records data flow activity, and identifies risks based on the applicable privacy requirements. No technical implementation is required to scan live websites or apps. Only the URL or app store file is needed.

Many consent management platforms today can run scans to identify cookies, pixels, scripts, and SDKs, but they don’t offer the capabilities to identify privacy risks based on consent, sensitive data sharing, or cross-border transfers. 

Because privacy requirements and regulations vary significantly by location, it is critical that user behavior can be simulated in each relevant location and analyzed against each location’s privacy requirements. Most notably, websites and apps in Europe under GDPR must verify that no cookies or personal data is shared unless users opt in. In California, CCPA/CPRA compliance checks are needed to ensure no cookies or personal data is shared for users who opt out. 

To properly mitigate privacy risk by location, live product scanning solutions should be able to identify all third party activity based on consent, flag sensitive data elements shared, and confirm banners and links display correctly. Since websites and apps are updated monthly if not weekly with new data flows, scans should be run on a regular basis to prevent new risks.  

For third-party activity, all cookies, pixels, scripts, tag managers, and SDKs should be identified, and activity should be tracked for each possible consent action: opt in, opt out, or no action. The location of each third-party destination should also be tracked to flag potential cross-border transfer violations. For each risk identified, the exact third-party code and network activity can be automatically documented as evidence for quick validation. 

To prevent sensitive data sharing, each data element shared needs to be identified and classified. Live product scanning can detect data elements shared automatically when third-party tools like pixels and SDKs load. These data elements typically include device characteristics like device model and browser version that may not be detected by code scanning. Live product scanning can also detect additional data elements based activity from certain user actions such as button clicks. Together with code scanning, all personal data elements shared can be detected and the source of the sensitive data sharing can be pinpointed for immediate remediation. 

Live product scanning also enables visual checks to ensure consent banners and regulatory links display correctly in each location. Regulators are now on the lookout for privacy dark patterns in consent banners that make it difficult for users to opt out or understand what they are consenting to. Live product scans can take screenshots to provide quick evidence that banners and links are compliant. 

Code scanning

Code scanning, more specifically privacy code scanning, analyzes the code running any user-facing or backend software product to mitigate privacy risk during software development and after products are live. Privacy code scanning solutions are used to scan the code written by a company’s engineering teams for the websites, apps, and other software they develop internally.

Because code contains the logic for how data is collected, used, shared, and stored, privacy code scanning enables comprehensive and real-time data mapping across websites, apps, backend software, and third parties.  

With complete visibility for how personal data is collected, used, shared, and stored, privacy assessments like RoPAs, PIAs, and DPIAs can be auto-populated with real-time information. Additionally by scanning code for live products and for products/features in development, privacy risks such as sensitive data processing can be detected and remediated before any harm is done. 

Because each instance of personal data processing is linked to the exact code within an application, privacy code scanning automatically provides engineers the evidence to quickly validate and remediate risks. When data processing violates privacy policies, issues are linked to the exact code causing the violation, and engineers can quickly resolve the issue.

Privacy code scanning typically requires just one integration with a source code management tool, which stores a company’s entire codebase. Once this is done, privacy code scans can be seamlessly integrated into DevOps processes to prevent risks early on. Each time a developer submits new code for review can trigger a scan that identifies any privacy risks that should be addressed. This approach is similar to how many application security tools scan code to identify security vulnerabilities. 

Manual assessments

Since most privacy solutions today don’t monitor software products, manual assessments are the predominant approach to create data maps, assess risk, and document compliance.

After companies using a data discovery tool inventory most of their data in storage, they still have to conduct manual assessments to find out how the data is collected, used, and shared. Visibility into the full data lifecycle is needed to complete RoPAs, DPIAs, PIAs, etc. and ensure compliance. 

To get this visibility, most privacy teams send questionnaires and interview requests to teams that may know how personal data is being processed including, product management, engineering, data analytics, and marketing. This even includes privacy teams who already completed a 6-12 month implementation of a data discovery tool because data discovery tools can only identify what data is stored, not how it is used or shared. 

If the privacy team asks the engineering team what personal data their websites and applications process, they would attempt to manually do what privacy code scanning does automatically, review their code. 

Before doing that, engineering leads would struggle to find all the engineers with the knowledge of how their software processes personal data. Because some engineers have left the company and the engineering leads likely don’t know or don’t have the time to find the right owner for every part of the code for every application, a handful of engineers without full context or privacy expertise will attempt to answer the privacy team’s questionnaires for all applications. 

After first waiting weeks or even months to look at the questionnaires because they’re busy meeting engineering sprint deadlines, each engineer will need to spend hours asking other engineers, reviewing documentation, and reviewing the code itself to complete questionnaires for each application. 

Even for the engineers, the code is the best place to find the answers to the privacy questionnaires. The issue is it’s impossible for any one person to manually review a company’s entire codebase. 

On top of that, the codebase is constantly changing as many engineering organizations now ship software updates at least once a week. 

For companies that try to employ a Privacy by Design approach, they may do privacy reviews for new product changes at the design stage. While this is possible for top-down planned features, many features are built bottoms-up after the design stage. Even if design reviews are conducted for all new changes, development can still deviate from the original design, causing privacy gaps and issues to emerge. 

Most companies today use a consent management platform (CMP) to handle consent on websites and mobile apps. Because CMPs don’t monitor whether data flows honor consent in accordance with privacy regulations like CPRA or GDPR, manual assessments or audits are required to verify compliance. These manual audits expend valuable resources and still leave many risks undetected. 

If privacy teams are lucky, they will have an internal privacy engineer regularly spend hours manually testing websites and apps to check that CMPs and third-parties are set up to honor consent. Some privacy teams will contract consultants to do manual audits while many privacy teams don’t have the resources to do any audits at all. Instead, many privacy teams don't have any visibility into the privacy risks on their websites and apps until there is a lawsuit claim or investigation.

The bottom line is that manual assessments do not scale and yield imprecise, out-of-date outputs. As a result, manual assessments open up companies to many unknown privacy risks while dramatically slowing down engineering and privacy teams. 

Use cases for product privacy management

Data Mapping: Automate data maps for websites, apps, backend software, & third parties 

Data maps are the foundation of a best-in-class privacy function. Comprehensive data maps record personal data elements processed by a company and track how each data element is collected, used, shared, and stored. With this level of visibility, privacy teams can accurately assess privacy risk and create accurate compliance reporting, including RoPAs, PIAs, and DPIAs.

Data maps have been notoriously difficult for privacy teams to create and maintain. The most common method is through manual questionnaires and interviews. Data owners such as engineering teams are asked to set aside time to manually review code and documentation to try to provide extensive information regarding data usage they’re likely not experts about. This process costs some organizations thousands of hours annually and yields inaccurate results that are quickly out-of-date. With most software now getting updated weekly if not monthly, data flows are constantly changing and creating new privacy risks. 

Data discovery solutions have attempted to help automate data mapping processes, but they only identify data in storage and still require manual input to map how data is collected, used, and shared. 

Product privacy management solutions can fully automate data mapping by directly monitoring data processing at the source: the software products themselves. Privacy code scanning is the most effective and efficient method to generate a data map and provides more than enough visibility for most companies that develop software products in-house such as websites, apps, and backend software. Privacy code scanning identifies all personal data processing that is directed by in-house software products, which is nearly all data processing for some companies. The code in-house developers write or import from elsewhere contains the logic for how data is collected, used, shared, and stored. 

Some code deployed to implement third-parties like ad or analytics partners automatically collects data that is not specified in the code. Ad partner pixels often contain JavaScript, or scripts, that automatically collect device characteristics such IP address, browser version, device model, etc. These device characteristics can be used together to identify a user without a cookie or device ID. This approach is known as fingerprinting and is still not compliant when used without proper consent. 

Product privacy management solutions typically use live product scanning to identify this data processing that third-party tools collect automatically and may not be indicated in the code. Live product scanning simulates live user behavior and records data flow activity. By simulating each consent action and other activity on websites and apps, all other data flows can be identified.

Scanning code and live products together enables complete data mapping coverage and eliminates all manual data mapping processes. Privacy teams and product stakeholders no longer need to spend hours on questionnaires and interviews. In addition, data maps will automatically be linked to evidence. Each instance of data processing is tied back to code or raw data flow activity, which provides objectivity and enables quick remediation.  

Digital Tracking Governance: Monitor consent and prevent non-compliant data sharing for websites, apps, and backend software

In the US, the largest privacy risk right now is non-compliant data sharing with marketing partners. Since 2023, the FTC has fined at least 15 companies for improperly sharing personal health data to marketing partners like Meta and Google. 

In 2024, enforcement launched for six US state privacy laws, most notably for the California Privacy Rights Act (CPRA) amendment to CCPA, Washington state’s My Health My Data Act (MHMDA), and the Texas Data Privacy And Security Act (TDPSA). Just in Q1 of 2025, these three new laws led to charges against Honda (CPRA), Amazon (MHMDA), and Allstate (TDPSA). Each new US state regulation puts more onus on companies to collect, track and uphold consent before sharing user data.

Meanwhile, the EU’s General Data Protection Regulation (GDPR) remains the strictest law governing personal data sharing, requiring opt-in consent before data is collected or shared. 

These new laws and increased enforcement require a new approach to stay compliant called digital tracking governance. Digital tracking governance is responsibly managing personal data shared with third parties, particularly marketing partners, by honoring user preferences. Product privacy management enables best-in-class digital tracking governance by: 

• Identifying all third parties: Build a real-time inventory of all 3rd parties receiving personal data via pixels, scripts, cookies, tag managers, and SDKs from your websites, apps, and backend integrations/APIs
• Tracking data flows: Gain full visibility by continuously monitoring how all data elements are collected and shared from your websites, apps, backend software, and third-party tools
• Verifying consent compliance: Continuously audit websites and apps to ensure consent banners limit data sharing according to regulations and user preferences

Prevent data sharing without consent

US state laws (e.g., CPRA) and GDPR in Europe require that companies honor user consent before sharing personal data with third parties. 

CPRA requires companies to let users opt out of selling any personal data or sharing personal data for advertising purposes. Most notably, this law prohibits websites from dropping advertising third-party cookies for users who opt out so that advertising third parties cannot target those users with personalized ads elsewhere. In addition, this law prohibits websites, mobile apps, connected TV apps from sharing any personal data advertising third parties for users who opt out. To comply, companies need to regularly monitor all third-party pixels, SDKs, tag managers, and customer data platforms to ensure they are not collecting personal data from websites and apps when users opt. 

GDPR on other hand requires users to opt in before personal data can be shared with any third parties. That means companies operating in countries under GDPR must run additional checks on their websites and apps to verify that no personal data is shared if users opt or take no action on the consent banner.    

Prevent sensitive data sharing

Personal data that regulations have classified as sensitive must follow stricter requirements. Privacy laws in the US and GDPR give special consideration to health, financial, and location data that can be tied to individuals. In general, sensitive data cannot be shared at all unless the user explicitly opts into the specific data being shared and the purposes it’s being shared for. For users to explicitly opt into sensitive data sharing, companies need to clearly disclose the specific data being shared and the purposes of sharing. 

Although the US does not have a comprehensive federal privacy law, sensitive data sharing is prohibited by the HIPAA for healthcare companies and the FTC for all other companies. Between 2022-2024, three healthcare systems paid large fines for sensitive health data sharing without consent: Mass General Brigham, Novant Health, Palm Beach Health, and New York Presbyterian Hospital. In 2023 and 2024, the FTC issued fines for the same reason to the following six non-healthcare companies: BetterHelp, GoodRx, Easy Healthcare (Premom app), Flo Health, Monument, and Cerebral.

Sensitive data sharing fines are expected to increase in the US after the California Attorney General announced in March 2025 that they will launch an ongoing investigation of mobile apps, ad networks, & data brokers for non-compliant sharing of users’ location data. 

Prevent non-compliant cross-border data transfers

In today’s ever evolving global privacy landscape, many countries now have laws that restrict cross-border transfers of personal data. Most notably, the EU’s GDPR, the US DOJ new cross-border data rule, and China’s Personal Information Protection Law (PIPL) restrict what data can be sent where and under what circumstances.

The largest-ever GDPR fine, $1.3B, was issued to Meta in 2023 for improperly transferring personal data from Ireland to the US. Uber also received one of the largest-ever GDPR fines for improperly transferring personal data from the Netherlands to the US when they were fined $317M in 2024.

In January 2025, the US Department of Justice (DOJ) adopted a new rule restricting cross-border transfers of personal data to people and entities with certain connections to China, Cuba, Iran, North Korea, Russia, or Venezuela. In particular, the rule restricts websites and apps from sending sensitive personal data (i.e., biometric, health, location, financial, or user identifiers) in bulk to companies that are at least 50% owned by entities based in the one of the countries listed. Most notably, the rule restricts personal data sharing with advertising partners based in China such as TikTok. The rule goes into effect on April 8, 2025. 

Product privacy management can prevent non-compliant data sharing across borders both internally and externally. Third parties and internal destinations can be categorized by location, and policy workflows can be set up to limit what personal data is sent where. 

Prevent sharing sensitive data with AI applications

With AI application development and adoption at an all-time high, AI governance couldn’t be more important to privacy teams. AI applications are built with and fine-tuned with data that may include sensitive personal data. Users also input data into certain AI applications that may need to be filtered out for privacy or other reasons. 

For data that engineering teams send to internal or external AI applications, privacy code scanning can ensure that no sensitive personal data is shared. Policy workflows can be set up to restrict select or all personal data elements from being sent to applications flagged as AI. 

RoPAs: Auto-populate Record of Processing Activities for GDPR compliance

GDPR requires that all processors and controllers of personal data for people in the EU must regularly maintain a live Record of Processing Activities or RoPA. RoPAs require privacy teams to list each processing activity, identify what categories of data are being used, and describe the purpose of each activity. 

By leveraging its full lifecycle data maps, product privacy management solutions can automate RoPA reporting to the point the engineers don’t need to do any questionnaires or interviews. Instead of waiting months to hear back from engineers, privacy teams can complete RoPAs in a matter of days.  

In addition, RoPAs can be automatically updated each time there’s a software update that changes data flows. Because RoPAs typically take several months to complete, they are usually only updated once a year. 

When over 42% of engineers release software at least once a month and over 69% release at least once every six months, most RoPAs are out-of-date before they’re even done. In addition, the RoPAs built from subjective questionnaires are likely to have missing or inaccurate information.

Product privacy management can eliminate compliance risks from inaccurate RoPA reporting by automatically generating reports based on real-time data flows. 

Privacy by Design: Integrate evidence-based privacy controls across the software product development lifecycle

Run automated privacy checks during software development to remediate privacy risks before they go live 

By scanning code during the software development process, privacy code scanning can help prevent privacy risks before they even go live. Similar to code scanning solutions for application security, privacy code scanning solutions can integrate with a company’s source code management or continuous integration / continuous delivery (CI/CD) pipeline tool to run a scan each time new code is submitted for review. Developers and privacy teams can then be immediately alerted so that risks are resolved before the code is pushed live.

Even when privacy assessments are done during the design phase of a new product or feature update, privacy risks often still arise because software commonly changes and evolves during the development process. 

Instead of privacy teams only finding out about a software change after a privacy incident occurs, privacy code scanning can ensure non-compliant software updates don’t launch if they deviate from the latest privacy assessments or violate any privacy policies.

Furthermore, integrating privacy code scanning in the development process can eliminate additional manual assessments and even accelerate product launches. If the privacy team is informed of software changes affecting privacy after the design phase, this will typically trigger manual privacy assessments that may take weeks. Only once the assessment is complete will the product team be informed of changes they need to make, causing the product launch to be delayed even further.

With privacy code scanning, the privacy and product teams are both immediately alerted of privacy risks as the product is developed. This approach shifts privacy left in the process and enables developers to eliminate privacy risks before they cause further delays or issues.  

Additionally, assessments like PIAs and DPIAs can be updated or created automatically and instead of running a lengthy questionnaire or interview based process. With the comprehensive data maps generated by code scanning, product privacy management solutions can auto-populate PIAs and DPIAs with how personal data is collected, used, shared, and stored.  

For example, if the marketing team wants to integrate a new marketing partner’s SDK into a mobile app, a relatively quick third-party assessment can be done that approves this SDK to collect certain data with consent. Instead of running a lengthy PIA that involves manually reviewing the SDK’s documentation and code to verify that privacy requirements will be met, a code scan can automatically identify all data processing and auto-populate a PIA. Additionally, when the SDK is included in the next code review and a code scan is triggered, the developer can be notified immediately that the SDK shares sensitive data and that certain code should be changed.   

Continuously monitor live software products to identify and remediate privacy risks

Product privacy management is also incredibly valuable for mitigating privacy risks in live software products, including websites, apps, and backend software. 

The most visible privacy risks lie on live websites and mobile apps. When Privado scanned the top 100 websites in the US and Europe, they found 75% were not privacy compliant because they shared personal data without proper consent. Initial research on mobile apps has found a similar rate of non-compliance so far.

Because data flows and third parties are constantly being changed on websites and mobile apps, they need continuous oversight to ensure third parties and CMPs are set up correctly. By scanning live products and simulating user behavior for each consent action, product privacy management solutions can continually verify that all third parties honor consent and that no sensitive data is shared. 

Additionally, the code for all user-facing and backed products can be scanned to identify and remediate privacy risks across a company’s tech stack. Privacy code scanning can provide full coverage for all sensitive data processing and cross-border transfers that present a privacy risk. Even with privacy code scanning running on products during development, the entire codebase should be scanned regularly to provide a complete picture of personal data flows and ensure nothing slips through the cracks. 

Auto-populate privacy assessments with real-time evidence

The most common privacy assessments are Data Protection Impact Assessment or DPIAs and Privacy Impact Assessments (PIAs), and the bulk of information they attempt to gather is related to data maps automatically generated by privacy code scanning. 

Manual privacy assessments dramatically slow down privacy and engineering teams, and they should only be initiated for more complex situations such as building a new personal health app. Product privacy management can save an enormous amount of time by auto-populating assessments with real-time data and reducing the number of assessments needed for minor product updates or assessment refreshes.

Standard or custom assessments can be built within product privacy management platforms to automatically pull in the required data map information such as what personal data is processed, how it is used, where it is sent to, etc. In addition, the auto-populated responses can be linked to objective evidence in the code showing each instance of data processing. Instead of having to spend hours manually reviewing code or documentation, developers can immediately validate the findings already linked to code. The automated reporting can be combined with standard and custom questionnaires to fill in any remaining information. 

For companies operating in the EU, GDPR requires a DPIA for high-risk projects involving personal data. GDPR provides guidelines for how to conduct a DPIA and for when a DPIA is needed. It is typically up to the company’s Data Protection Officer or DPO to determine exactly how and when DPIAs are conducted. Regulators typically only review DPIAs if a company is being investigated for a GDPR violation.   

The other most common privacy assessment is a PIA. PIAs are similar to DPIAs except they are conducted when DPIAs are not required by GDPR, most often in the US where GDPR does not typically apply. PIAs are less standardized than DPIAs, but they are used for similar high-risk projects and collect similar information such as how personal data is used and shared. 

Product privacy management platforms can be set up to automate DPIAs and PIAs that are custom to the needs of each company and even project. Some DPIAs or PIAs may need to be refreshed annually or when major product changes are being made. With product privacy management, the most time-consuming information to update regarding data flows can be automatically refreshed with each regular code scan. Therefore, 90%-100% of the manual refresh effort can be eliminated.  

App Store Privacy Reports: Auto-populate Apple Privacy Manifest & Google Play Data Safety reports 

Apple and Google both require app owners to submit privacy reports for apps to be published in their respective app stores: the App Store and Google Play. The reports for both app stores require information that privacy code scanning gathers automatically: what personal data is collected, who it is shared with, and for what purpose. 

Apple requires privacy manifest reports each time a new app or app update is submitted to the App Store for approval and requires app owners to maintain accurate Privacy Nutrition Labels. Privacy manifests are designed for Apple to determine privacy compliance when approving an app for the App Store while Privacy Nutrition Labels are designed to transparently communicate the app’s data privacy practices to users.  

The Google Play Store requires app owners to complete their data safety form that is similar to Apple’s Privacy Nutrition Labels; the form is used to populate the data safety section that tells users how personal data is processed for each app in the Google Play Store.  

To accurately complete these reports for each app, developers have to manually review their app’s code or documentation or wait for third parties to complete questionnaires explaining how they process personal data. Utilizing privacy code scanning, these reports can be generated automatically so that they simply need to be double-checked, saving an enormous amount of time while providing more accurate, up-to-date information.  

M&A Due Diligence: Assess and mitigate privacy risk for products from mergers & acquisitions 

When acquiring or merging with another company, product privacy management solutions can quickly assess their privacy risk profile and identify how to address compliance issues. Different companies have different privacy policies and practices, and typically the company with higher privacy standards has to spend months assessing the other company’s risk by reviewing documentation, conducting interviews, and/or waiting on teams to complete questionnaires.

Product privacy management can eliminate the vast majority of those manual assessment activities. By scanning the new company’s entire codebase, a full inventory of personal data elements and potential privacy risks can be generated without any manual effort. Live websites and apps can be immediately scanned for any consent compliance risks. Additionally, more comprehensive and accurate privacy assessments can be completed in days that would normally take months. 

After an acquisition is completed, it can also take months if not years for the new company to adopt the acquiring company’s privacy standards. Product privacy management can rapidly accelerate this integration process.

The acquiring company’s privacy standards can be easily converted into privacy checks that identify exactly what code is violating which policy. Instead of new products and features getting dramatically delayed for not meeting the privacy standards, the automated checks can be built into the software development process, enabling developers to build with privacy in mind. As code moves to the code review stage, privacy checks can alert developers how to address deviations from the privacy standards. 

Product Privacy Card: Generate transparent privacy reporting for software vendors to expedite vendor assessments

B2B sales can take a long time, especially when enterprise companies evaluate a new software vendor. B2B enterprise software cycles typically take 6-12 months and drain a lot of resources from the buyer and vendor in the process. 

A privacy review of a vendor is one of many things that can slow down a deal along with reviewing security, technical feasibility, ethical practices, etc. What if the vendor could provide the buyer with an unbiased, objective report that enables the buyer to skip the privacy review altogether? This could save both sides many hours from reviewing privacy practices and completing and evaluating RFP questionnaires. 

Product privacy management solutions can automatically create such a report for software vendors. This way software vendors could come to each deal with a standard report that may preemptively answer all of the buyer’s privacy questions.

For example, these reports could show data maps with all personal data the vendor’s product collects, uses, stores, and shares. Depending on the buyer’s policies, the report could be tailored to include additional automated checks for each privacy regulation and standard required by the buyer. To enable quick validation, each finding in the report could be linked to each instance in the codebase where the data processing originates.  

Key advantages of product privacy management

• Complete data mapping coverage: Privacy teams can autogenerate data maps for all websites, apps, backend software, and third parties showing how all personal data elements are collected, used, shared, and stored. No questionnaires needed. 
• Real-time data flow visibility: Data maps, RoPAs, PIAs, and DPIAs will always stay up-to-date as software products are continuously scanned for data flow changes.
• Continuous and comprehensive privacy risk governance: Proactively detect all potential violations to privacy policies and applicable regulations across websites, apps, backend software, and third parties. Flag risks in live products and during development. Prevent data sharing without consent, sensitive data processing, and cross-border transfers.
• Preserves data security: No personal data is ever scanned or accessed; only live software products and code are scanned. Code is also never stored or shared and is never used to train AI models.
• Rapid time to value: Immediately identify and remediate privacy risks on websites and mobile apps without any technical implementation. Build complete org-wide data maps in a matter of weeks, not months 

Key capabilities of best-in-class product privacy management solutions

Dynamic Data Maps

• Complete inventory of all personal data elements processed and third parties receiving personal data across websites, apps, and backend software
• Data classification and sensitivity tagging by applicable regulation
• Data flow mapping showing each data element sent to each third party and internal destination via pixels, scripts, tag managers, SDKs, customer data platforms, APIs, etc. 
• Autogenerated descriptions of all processing activities
• Code-level evidence for each instance of personal data processing: collection, usage, sharing, and storing
• Real-time visibility: data maps auto-update as data flows change 
• No questionnaires or manual assessments needed

Consent Monitoring

• Comprehensive consent and data flow monitoring for compliance with privacy requirements in each location for websites, mobile apps, and connected TV apps
• Consent banner and policy visibility checks
• Cookie, tag, script, SDK, and third-party data flow activity tracking based on each consent action
• User journey simulation pre and post login across pages for full coverage

Auto-Risk Discovery

• Risk discovery workflows to flag potential violations to internal policies and regulations including: GDPR, CCPA/CPRA, CIPA, the FTC, HIPAA, etc. 
  
  • Data shared, cookies used, and data collected without proper consent
  • Sensitive health, location, financial, or children’s data shared with third parties and AI applications
  • Sensitive data collected, used, or stored
  • Cross-border data transfers 
  • Missing consent banners, privacy dark patterns, and missing regulatory links
• Regular privacy checks for live digital products: websites, mobile apps, connected TV apps, etc. 
• Privacy checks integrated into software development process to prevent risks before they go live
• Real-time risk alerts with evidence to enable immediate resolution

Smart Assessments

• Auto-populated RoPAs, PIAs, DPIAs, and app store compliance reports
• Auto-populated data elements, data types, 3rd parties, data stores, data flows, and processing purposes 
• Pre-built and custom assessments templates for any digital product, feature, processing activity, etc.
• Automatic assessment updates as data processing changes

Developer Tool Integrations

• Automated privacy alerts for developers as they code via integrations with source code management and CI/CD tools
• Root cause identification for privacy risks in the codebase
• Autogenerated dev tickets to resolve risks via integrations with ticketing tools

Impact driven by product privacy management

 Mitigate Privacy Risk At Scale

• Continuously identify all privacy risks across your tech stack: websites, mobile apps, connected TV apps, backend software, and third-party tools
  
  • Prevent data sharing and collection without proper consent
  • Prevent sensitive data sharing with third parties and AI applications
  • Prevent sensitive data collection, usage, and storage
  • Prevent non-compliant cross-border data transfers 
• Prevent risks from going live with privacy checks integrated into the software development process
• Enable immediate risk remediation by automatically identifying root cause and routing ticket to appropriate developer team 
• Generate and maintain accurate and up-to-date privacy compliance assessments and reports with real-time data processing evidence
• Eliminate risk of lawsuits and fines for violating privacy regulations including GDPR, CCPA/CPRA, CIPA, the FTC, HIPAA, etc. 

Increase Efficiency & Save Resources

• Complete data maps in days, not months by eliminating all manual data mapping process across digital products: websites, apps, and backend software
• Automate manual processes and reduce volume of assessments for RoPAs, PIAs, DPIAs, and app store privacy reports by auto-populating and auto-updating assessments with real-time data
• Eliminate manual consent management platform and cookie audits for websites and apps  

Accelerate Software Development

• Prevent product launch delays by identifying privacy risks early and eliminating manual privacy assessments
• Minimize developers’ time spent on privacy assessments

Key Takeaways

• Privacy teams need evidence-based solutions: Trust-based privacy controls such as manual privacy assessments, data discovery, consent management platforms have proven to break at scale. Without privacy solutions that continually verify compliance with evidence, companies risk serious privacy violations across their websites, apps, and backend software.  
• Regulators are asking for more evidence and issuing more fines: Privacy enforcement actions have increased exponentially since 2020, and the trend is likely to continue as regulators increase investigations. In 2025, the UK’s ICO, France’s CNIL, and California’s Attorney General will all launch privacy enforcement campaigns on websites and mobile apps to counter widespread personal data sharing without proper consent. 
• Product privacy management enables evidence-based privacy at scale: Software products such as websites, apps, and backend software are the primary source of privacy risk because they control data flows. By continuously monitoring how live products and products in development process personal data, product privacy management can continually provide the evidence to mitigate privacy risk across an organization. 

Learn more 

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Frequently asked questions

What is product privacy management?

Product privacy management is the practice of monitoring software products to mitigate privacy risk. Because software products control how data is processed in today’s tech-driven world, product privacy management can enable complete data visibility and continuous privacy governance at scale across an organization. By monitoring how websites, mobile apps, connected TV apps, backend software, and third-party applications process personal data, organizations can create complete data maps, proactively remediate privacy risks, and generate accurate compliance reporting. Product privacy management helps ensure privacy compliance by integrating evidence-based privacy controls across the product development lifecycle from planning through development and maintenance.

How are product privacy management solutions different from data discovery tools?

Data discovery tools scan data stores to build an inventory of all data in storage, not just personal data. Data discovery tools can only determine what personal data is stored; they lack coverage for how personal data is collected, used, or shared.

Product privacy management solutions scan the software products that control how data is collected, used, shared, and stored. By scanning code and live products, product privacy management solutions can provide complete visibility and risk mitigation of how personal data is collected, used, shared, and stored across websites, apps, backend software, and third parties.

Code scanning enables fully automated data maps, autopopulated privacy assessments, and risk discovery during and after software product development. By scanning live websites and apps, Privado also monitors whether consent banners and data flows meet all consent requirements by location. 

How is product privacy management different from application security tools that scan code?

Product privacy management solutions scan live digital products and static code to monitor personal data flows and mitigate privacy risk. 

Application security solutions scan live digital products and static code to identify security vulnerabilities such as unauthorized access to systems, cyberattacks, API token leaks, and outdated software packages. 

Product privacy management solutions enable fully automated data maps, autopopulated privacy assessments, and risk discovery during and after software product development. By scanning live websites and apps, Privado also monitors whether consent banners and data flows meet all consent requirements by location. 

What types of companies benefit most from product privacy management?

Any company processing large amounts of personal data on their websites, mobile apps, or other software products. Companies that benefit most typically develop their own websites, apps, and backend software, but that’s not always the case. Websites and mobile apps are subject to the most visible privacy risks that lead to privacy lawsuits, and any website or app at risk sharing personal data without proper consent needs continuous monitoring from product privacy management solutions. Product privacy management has successfully reduced privacy risk for companies across industries including, ecommerce, finance, healthcare, gaming, software, telecommunications, transportation, insurance, ad tech, and data intelligence.  

What products do product privacy management solutions cover? 

Product privacy management solutions monitor personal data flows and mitigate privacy risk across an organization’s tech stack, including websites, mobile apps, connected TV apps, backend software, and third-party tools.  

What is the process to implement product privacy management solutions?

No implementation is required to scan live websites and apps for privacy risk. Only a URL is needed to scan a live website, and the app store file (IPA for iOS or APK/AAB for Android) is needed to scan a live mobile app.

Scanning the code for any digital product requires one integration with your source code management tool (e.g., GitHub, GitLab, Bitbucket). This integration is typically done in the cloud or your on-premise environment. There is also a hybrid on-prem and cloud option where code scanning occurs on-premise by integrating into your CI/CD pipeline tool and sending the results to the provider’s cloud platform environment.

For reference, source code management tools contain all the code written by your engineering team and have a wide range of capabilities including deploying software updates via a CI/CD pipeline.  

Can product privacy management help my organization maintain compliance with GDPR?

Product privacy management solutions are designed to support several aspects of GDPR compliance including, data mapping, Records of Processing Activity (RoPA) automation, Data Protection Impact Assessment (DPIA) automation, and GDPR privacy risk prevention. Product privacy management prevents risks related to personal data collection, usage, 3rd party sharing, and storage as well as consent compliance auditing. 

Can product privacy management help my organization maintain compliance with CPRA?

Product privacy management solutions are designed to support several aspects of CPRA compliance including, data mapping, prevent non-compliant data sharing, and auditing consent compliance (i.e., “do not sell or share”).

How do product privacy management solutions communicate privacy risks to privacy and engineering teams? 

Product privacy management solutions communicate risks in their own platform and whichever other tools privacy and engineering teams use including privacy management (e.g., OneTrust), Slack, Teams, ticketing systems (e.g., Jira), dev tools (e.g., GitHub), etc. 

How can product privacy management build trust with stakeholders?

Product privacy management builds trust and collaboration across teams including privacy, product, engineering, etc. by translating privacy policies into automated workflows that identify what code is violating which policy. Linking data maps and risks to code enables immediate validation and resolution from engineering teams. Additionally, risks are communicated seamlessly in the tools and language that each team uses.

How can product privacy management build trust with customers?

Product privacy management builds customer trust by ensuring a company’s privacy promises to customers are followed through on. Privacy teams are given the visibility and governance to monitor and prevent violations to the privacy policies communicated to customers. 

Can product privacy management replace my current privacy management tool?

Product privacy management solutions are designed to supplement, not replace privacy management tools like OneTrust. Data maps and risks can be seamlessly synced to privacy management tools to increase their efficiency and effectiveness. 

Can product privacy management govern data used in AI applications? 

For data that engineering teams send to internal or external AI applications, Product privacy management can ensure that no sensitive personal data is shared. Policy workflows can be set up to restrict select or all personal data elements from being sent to applications flagged as AI. 

Can product privacy management scan 3rd party applications to monitor personal data flows?

Product privacy management solutions can scan certain types of 3rd party applications like customer data platforms or tag manager, but they typically cannot scan other types of 3rd party applications like Salesforce or Workday unless one-off integrations are built for each 3rd party application.